When talking to customers and users, this question is frequently left open with the note that they’ll check and tell us later. With storage being one of the main cost factors of cloud services such as Amazon’s AWS, this is not what it should be.

But who could blame them? It’s not like AWS is making it obvious to you how much of your storage resources (not only capacity but especially IOPS and throughput) you really use. It might be bad for AWS’ revenue.

Why We Built This

We believe that there is no reason to pay more than necessary. However, since it’s so hard to get hard facts on storage use, we tend to overprovision—by a lot.

Hence, we decided to do something about it. Today, we’re excited to share our new open-source tool – the AWS EBS Volume Usage Exporter!

What makes this particularly interesting is that, based on our experience, organizations typically utilize only 20-30% of their provisioned AWS EBS volumes. That means 70-80% of provisioned storage is sitting idle, quietly adding to your monthly AWS bill. Making someone else happy but you.

What Our Tool Does

The EBS Volume Usage Exporter runs in your EKS cluster and collects detailed metrics about your EBS volumes, including:

• Actual usage patterns
• IOPS consumption
• Throughput utilization
• Available disk space
• Snapshot information

All this data gets exported into a simple CSV file that you can analyze however you want.

If you like convenience, we’ve also built a handy calculator (that runs entirely in your browser – no data leaves your machine!) to help you quickly understand potential cost savings. Here’s the link to our EBS Volume Usage Calculator. No need to use it, though. The data is easy enough to get basic insight. Our calculator just automates the pricing and potential saving calculation based on current AWS price lists.

Super Simple to Get Started

To get you started quickly, we packaged everything as a Helm chart to make deployment as smooth as possible. You’ll need:

• An EKS cluster with cluster-admin privileges
• An S3 bucket
• Basic AWS permissions

The setup takes just a few minutes – we’ve included all the commands you need in our GitHub repository.

After a successful run, you can simply delete the helm chart deployment and be done with it. The exported data are available in the provided S3 bucket for download.

We Want Your Feedback!

This is just the beginning, and we’d love to hear from you!

Do the calculated numbers match your actual costs?
What other features would you find useful?

We already heard people asking for a tool that can run outside of EKS, and we’re looking into it. We would also love to extend support to utilize existing orchestration infrastructure such as DataDog, Dynatrace, or others. Most of the data is already available and should be easy to extract.

For those storage pros out there who can answer the EBS utilization question off the top of your head – we’d love to hear your stories, too!

Share your experiences and help us make this tool even better.

Try It Out!

The AWS EBS Volume Usage Exporter is open source and available now on GitHub. Give it a spin, and let us know what you think!

And hey – if you’re wondering whether you really need this tool, ask yourself: “Do I know exactly how much of my provisioned EBS storage is actually being used right now?”

If there’s even a moment of hesitation, you should check this out!

At simplyblock, we’re passionate about helping organizations optimize their cloud storage. This tool represents our commitment to the open-source community and our mission to eliminate cloud storage waste.

The post We Built a Tool to Help You Understand Your Real EBS Usage! appeared first on simplyblock.

For companies that use Amazon Web Services (AWS) for storage, having firm control over costs is key. Mismanaging storage can directly lead to soaring expenses and unexpected losses in a fast-paced world with fluctuating workloads. A reliable and effective way to keep costs in check for a business is to implement efficient storage solutions and in the right size. This can ensure solid growth and performance, making it a plan that works for AWS-reliant businesses.

This article will look at several good ways to manage storage costs on AWS and give you practical tips to size storage in changing environments.

What Does Right-Sizing Mean for AWS Storage?

Choosing the right size in AWS means going with suitable storage solution types and scales based on the particular needs of a business. It’s a major strategic factor that can help you achieve and maintain a competitive lead in the market by avoiding unnecessary costs and putting a stop to overspending. All it takes is actively monitoring storage policies, checking how much storage usually goes unused, and proactively making prompt changes accordingly.

AWS offers various storage types, including Amazon S3 for object storage, Amazon EBS for block storage, and Amazon EFS for file storage, each suitable for different applications. By right-sizing, businesses can avoid paying for idle storage resources and only use what’s necessary.

AWS Storage Services and The Cost-Savings They Offer

With AWS, you get a few storage options at different price points that you can choose from based on your business needs

• Amazon S3 (Simple Storage Service) offers an incredible amount of scalability, allowing growing businesses to adapt well. It works well for unorganized data and uses a pay-as-you-go system, which keeps costs down when storage needs change.
• Amazon EBS (Elastic Block Store) gives lasting block storage for EC2 instances. EBS prices change based on the type of volume, its size, and input/output actions, so you need to watch it to keep expenses in check.
• Amazon EFS (Elastic File System) is a managed file storage service that grows on its own, which helps applications that need shared storage. While it’s convenient, data costs can rise as volume grows.

To reduce overall cloud spending, it’s essential to understand which storage type suits your workloads and to handle these services.

Editor’s note: If you’re looking for ways to consolidate your Amazon EBS volumes, simplyblock got you covered.

Ways to Optimize Storage Size on AWS

1. Use Storage Class Levels

Amazon S3 offers various storage classes with different costs and speeds. You can save money by placing data in the appropriate class based on access frequency and retrieval speed needs. Here’s a breakdown:

• S3 Standard is best for accessed data, but it’s more expensive.
• S3 Infrequent Access (IA) is cheaper for less-used data that still needs retrieval.
• S3 Glacier and Glacier Deep Archive are the least expensive options for long-term accessed data.

You can cut costs without losing access by reviewing and moving data to suitable storage classes based on usage patterns.

2. Set Up Data Lifecycle Rules

Managing data lifecycles helps companies with changing storage needs to save money. AWS lets you make rules to move, store, or delete data based on certain conditions. With S3 lifecycle policies, you can set up your data to move from S3 Standard to S3 IA and then to Glacier or be removed after a set time.

3. Use Automatic Tracking and Warnings

AWS offers tools to keep an eye on storage use and costs, like AWS CloudWatch and AWS Budgets. These tools help spot wasted resources, odd spikes in use, or costs that go over your set budget. Setting up warnings through AWS Budgets can tell you when you’re close to your budget limit and stop extra costs before they pile up.

4. Make EBS Volumes the Right Size

Elastic Block Store (EBS) volumes often waste resources when they’re bigger than needed. Checking EBS use often can show volumes that aren’t used much or at all. AWS has a tool called EBS Right Sizing Recommendation that helps find volumes you can make smaller without slowing things down.

EBS provides Volume Types such as General Purpose (gp3), Provisioned IOPS (io2), and Throughput Optimized (st1). Picking the right volume type for each workload, plus sizing, cuts costs. You pay for the storage performance and capacity you need.

Editor’s note: Save up to 80% on your high-performance AWS storage costs.

Smart Ways to Cut AWS Storage Costs Further

1. Use Reserved Instances and Savings Plans

For workloads you can predict, think about AWS Reserved Instances (RIs) and Savings Plans. People often link these to EC2 instances, but they also offer cheap options for related EBS storage. RIs and Savings Plans let you promise to use a certain amount, giving you lower rates if you commit for one or three years. This works best for steady workloads that need the same amount of storage over time, where you’re less likely to buy too much.

Savings plans shouldn’t just be limited to storage solutions. You can also reconsider other costs, such as switching to a cheap web hosting service that still meets your business needs.

2. Make Multi-Region Storage More Efficient

AWS gives you ways to copy your data across different regions. This makes your data more secure and helps you recover if something goes wrong. But storing data in multiple regions can cost a lot because of copying and moving data between regions. To cut these costs, you can look at how people use your data and put it in regions close to most of your users.

3. Consider Spot Instances for Short-Term Storage Needs

Spot Instances offer a more affordable option to handle tasks that can cope with interruptions. You can use short-term storage on Spot Instances for less crucial brief projects where storage requirements fluctuate. When you combine Spot Instances with Amazon EBS or S3, you gain flexibility and cut costs. However, remember that AWS has the right to reclaim Spot Instances at any moment. This makes them unsuitable for critical or high-availability tasks.

Summing Up: Managing AWS Storage Costs

Smart AWS cost control begins with a hands-on strategy to size storage. This includes picking the right S3 storage types, setting up lifecycle rules, keeping an eye on EBS use, or taking advantage of reserved options. These methods can help you keep a lid on your storage bills.

When you check usage and put these tried-and-true tips into action, you’ll be in a better position to handle your AWS expenses. At the same time, you’ll keep the ability to scale and the reliability your workloads need. In a cloud world where storage costs can get out of hand, clever management will pay off. It’ll help your company stay nimble and budget-friendly.

The post AWS Cost Management: Strategies for Right-Sizing Storage in Dynamic Environments appeared first on simplyblock.

The AWS Cloud ecosystem is a dynamic and rapidly evolving environment that supports a vast array of services and applications. As organizations increasingly rely on AWS for their cloud computing needs, open-source tools have become invaluable for enhancing AWS operations. These tools provide essential capabilities such as infrastructure management, cost optimization, security, and monitoring, ensuring that your AWS environment runs efficiently and securely. As AWS continues to grow in popularity, the demand for effective and reliable open-source tools has surged. Cloud architects, developers, and operations teams are always looking for tools that can help them manage their AWS environments more effectively. In this post, we will explore nine must-know open-source tools that can help you optimize your AWS Cloud experience.

1. Terraform

Terraform is a powerful infrastructure-as-code (IaC) tool that allows you to define and provision your AWS infrastructure using a simple, declarative configuration language. With Terraform, you can version control your infrastructure, automate deployments, and ensure consistency across your environments. It’s a must-have tool for managing complex AWS environments and streamlining cloud operations.

2. Ansible

Ansible is an open-source automation tool that simplifies the process of managing AWS resources. It uses a simple, human-readable language (YAML) to define tasks and configurations, making it easy to automate provisioning, configuration management, and application deployment. Ansible’s extensive AWS modules enable seamless integration with AWS services, helping you automate cloud operations with ease.

3. Prometheus

Prometheus is a leading open-source monitoring and alerting toolkit widely used for tracking the performance and health of AWS infrastructure. It collects metrics from your AWS services, stores them, and allows you to visualize and query the data. Prometheus is essential for ensuring that your AWS applications and services are running smoothly and for identifying potential issues before they impact your users.

4. Kubernetes (K8s) on AWS (EKS)

Kubernetes is a powerful container orchestration platform, and when combined with Amazon Elastic Kubernetes Service (EKS), it becomes a robust solution for managing containerized applications on AWS. It automates the deployment, scaling, and operation of application containers, while EKS provides a fully managed Kubernetes control plane, simplifying cluster management. This combination is ideal for deploying, managing, and scaling containerized applications on AWS.

5. AWS CDK (Cloud Development Kit)

The AWS CDK is an open-source software development framework that enables you to define your cloud infrastructure using familiar programming languages such as Python, JavaScript, and TypeScript. CDK simplifies cloud infrastructure management by allowing developers to use code to define and provision AWS resources, resulting in more maintainable and scalable infrastructure-as-code practices.

6. Packer

Packer is an open-source tool that automates the creation of machine images for AWS, including Amazon Machine Images (AMIs). It integrates seamlessly with your existing CI/CD pipelines, enabling you to create consistent, pre-configured images that can be used across your AWS environments. Packer is crucial for ensuring that your infrastructure is consistent, secure, and easy to deploy.

7. ElasticSearch (on Amazon Elasticsearch Service)

Elasticsearch is a widely-used open-source search and analytics engine that, when paired with Amazon Elasticsearch Service (OpenSearch Service), provides a scalable and secure way to search, analyze, and visualize data on AWS. It is particularly useful for log and event data analysis, making it easier to monitor and troubleshoot applications running in the cloud.

8. Cloud Custodian

Cloud Custodian is an open-source governance-as-code tool that allows you to manage and automate AWS resource policies. It enables you to define rules for resource provisioning, security, and compliance using simple YAML configurations. Cloud Custodian is invaluable for ensuring that your AWS environments adhere to best practices and regulatory requirements.

9. Grafana

Grafana is an open-source data visualization and monitoring tool that integrates with Prometheus and other data sources to provide comprehensive dashboards for monitoring AWS resources. It also offers powerful visualizations, alerting capabilities, and flexible query options.

How to Optimize AWS Cloud with Open-source Tools

This guide explored nine essential open-source tools for AWS Cloud, from Terraform’s infrastructure as code to Grafana’s visualization capabilities. While these tools excel at different aspects – Ansible for automation, Prometheus for monitoring, and Kubernetes for container orchestration – proper implementation is crucial. Tools like AWS CDK enable programmatic infrastructure definition, while Cloud Custodian and Packer provide governance and image management capabilities. Each tool offers unique approaches to managing and optimizing AWS resources.

Why Choose simplyblock for AWS Cloud?

While AWS provides robust cloud services, protecting cloud workloads against ransomware and ensuring business continuity across regions is crucial. This is where simplyblock’s specialized protection approach creates unique value:

Cloud Infrastructure Protection

Simplyblock ensures the integrity of your AWS environment by providing immutable backups of critical cloud resources, including EC2 instances, EBS volumes, and RDS databases. Unlike traditional backup solutions, simplyblock’s immutable storage architecture protects your AWS workloads against ransomware attacks while maintaining cross-region availability. The platform integrates seamlessly with AWS’s native services while adding an extra layer of ransomware-proof protection for your critical data.

Zero-Downtime Cloud Recovery

Simplyblock enables rapid recovery of AWS environments by preserving complete infrastructure states, maintaining data consistency across availability zones, and ensuring immediate access to clean backup copies. In the event of a ransomware attack or disaster, organizations can quickly restore their AWS workloads without paying ransoms or experiencing extended downtime. This approach ensures business continuity across your entire AWS infrastructure, from compute resources to storage volumes.

Enterprise-Grade AWS Protection

Simplyblock optimizes AWS protection through efficient management of backup storage, intelligent handling of cross-region replication, and preservation of infrastructure configurations. By leveraging AWS’s global infrastructure while adding immutable protection, simplyblock ensures both data integrity and cost efficiency for your cloud workloads.

If you’re looking to further streamline your AWS operations, simplyblock offers comprehensive solutions that integrate seamlessly with these tools, helping you get the most out of your AWS environment.

Ready to take your AWS management to the next level? Contact simplyblock today to learn how we can help you simplify and enhance your AWS journey.

The post Best Open Source Tools for AWS Cloud appeared first on simplyblock.

Due to this movement, other cloud operators, many database companies (such as Neon and Nile), and infrastructure teams at large enterprises are building serverless environments, either on their premises or in their private cloud platforms.

While there are great options for serverless compute, providing serverless storage to your serverless platform tends to be more challenging. This is often fueled by a lack of understanding of what serverless storage has to provide and its requirements.

What is a Serverless Architecture?

Serverless architecture is a software design pattern that leverages serverless computing resources to build and run applications without managing the underlying architecture. These serverless compute resources are commonly provided by cloud providers such as AWS Lambda, Google Cloud Functions, or Azure Functions and can be dynamically scaled up and down.

When designing a serverless architecture, you’ll encounter the so-called Function-as-a-Service (FaaS), meaning that the application’s core logic will be implemented in small, stateless functions that respond to events.

That said, typically, several FaaS make up the actual application, sending events between them. Since the underlying infrastructure is abstracted away, the functions don’t know how requests or responses are handled, and their implementations are designed for vendor lock-in and built against a cloud-provider-specific API.

Cloud-vendor-agnostic solutions exist, such as knative, but require at least parts of the team to manage the Kubernetes infrastructure. They can, however, take the burden away from other internal and external development teams.

What is Serverless Compute?

While a serverless architecture describes the application design that runs on top of a serverless compute infrastructure, serverless compute itself describes the cloud computing model in which the cloud provider dynamically manages the allocation and provisioning of server resources.

It is essential to understand that serverless doesn’t mean “without servers” but “as a user, I don’t have to plan, provision, or manage the infrastructure.”

In essence, the cloud provider (or whoever manages the serverless infrastructure) takes the burden from the developer. Serverless compute environments fully auto-scale, starting or stopping instances of the functions according to the needed capacity. Due to their stateless nature, it’s easy to stop and restart them at any point in time. That means that function instances are often very short-lived.

Popular serverless compute platforms include AWS Lambda, Google Cloud Functions, and Azure Functions. For self-managed operations, there is knative (mentioned before), as well as OpenFaaS and OpenFunction (which seems to have less activity in the recent future).

They all enable developers to focus on writing code without managing the underlying infrastructure.

What is a Serverless Storage System?

Serverless storage refers to a cloud storage model where the underlying infrastructure, capacity planning, and scaling are abstracted away from the user. With serverless storage, customers don’t have to worry about provisioning or managing storage servers or volumes. Instead, they can store and retrieve data while the serverless storage handles all the backend infrastructure.

Serverless storage solutions come in different forms and shapes, beginning with an object storage interface, such as Amazon S3 or Google Cloud Storage. Object storage is excellent when storing unstructured data, such as documents or media.

Another option that people love to use for serverless storage is serverless databases. Various options are available, depending on your needs: relational, NoSQL, time-series, and graph databases. This might be the easiest way to go, depending on how you need to access data. Examples of such serverless databases include Amazon Aurora Serverless, Google’s Cloud Datastore, and external companies such as Neon or Nile.

When self-managing your serverless infrastructure with knative or one of the alternative options, you can use Kubernetes CSI storage providers to provide storage into your functions. However, you may add considerable startup time if you choose the wrong CSI driver. I might be biased, but simplyblock is an excellent option with its neglectable provisioning and attachment times, as well as features such as multi-attach, where a volume can be attached to multiple functions (for example, to provide a shared set of data).

Why Serverless Architectures?

Most people think of cost-efficiency when it comes to serverless architectures. However, this is only one side of the coin. If your use cases aren’t a good fit for a serverless environment, it will hold true—more on when serverless makes sense later.

In serverless architectures, functions are triggered through an event, either from the outside world (like an HTTP request) or an event initiated by another function. If no function instance is up and running, a new instance will be started. The same goes for situations where all function instances are busy. If function instances idle, they’ll be shut down.

Serverless functions usually use a pay-per-use model. A function’s extremely short lifespan can lead to cost reductions over deployment models like containers and virtual machines, which tend to run longer.

Apart from that, serverless architectures have more benefits. Many are moving in the same direction as microservices architectures, but with the premise that they are easier to implement and maintain.

First and foremost, serverless solutions are designed for scalability and elasticity. They quickly and automatically scale up and down depending on the incoming workload. It’s all hands-free.

Another benefit is that development cycles are often shortened. Due to the limited size and functionality of a FaaS, changes are fast to implement and easy to test. Additionally, updating the function is as simple as deploying the new version. All existing function instances finish their current work and shut down. In the meantime, the latest version will be started up. Due to its stateless nature, this is easy to achieve.

What are the Complexities of Serverless Architecture?

Writing serverless solutions has the benefits of fast iteration, simplified deployments, and potential cost savings. However, they also come with their own set of complexities.

Designing real stateless code isn’t easy, at least when we’re not just talking about simple transformation functionality. That’s why a FaaS receives and passes context information along during its events.

What works great for small bits of context is challenging for larger pieces. In this situation, a larger context, or state, can mean lots of things, starting from simple cross-request information that should be available without transferring it with every request over more involved data, such as lookup information to enrich and cross-check, all the way to actual complex data, like when you want to implement a serverless database. And yes, a serverless database needs to store its data somewhere.

That’s where serverless storage comes in, and simply put, this is why all serverless solutions have state storage alternatives.

What is Serverless Storage?

Serverless storage refers to storage solutions that are fully integrated into serverless compute environments without manual intervention. These solutions scale and grow according to user demand and complement the pay-by-use payment model of serverless platforms.

Serverless storage lets you store information across multiple requests or functions. 

As mentioned above, cloud environments offer a wide selection of serverless storage options. However, all of them are vendor-bound and lock you into their services. 

However, when you design your serverless infrastructure or service, these services don’t help you. It’s up to you to provide the serverless storage. In this case, a cloud-native and serverless-supporting storage engine can simplify this talk immensely. Whether you want to provide object storage, a serverless database, or file-based storage, an underlying cloud-native block storage solution is the perfect building block underneath. However, this block storage solution needs to be able to scale and grow with your needs easily and quickly to provision and support snapshotting, cloning, and attaching to multiple function instances.

Why do Serverless Architectures Require Serverless Storage?

Serverless storage has particular properties designed for serverless environments. It needs to keep up with the specific requirements of serverless architectures, most specifically short lifetimes, extremely fast up and down scaling or restarts, easy use across multiple versions during updates, and easy integration through APIs utilized by the FaaS.

The most significant issues are that it must be used by multiple function instances simultaneously and is quickly available to new instances on other nodes, regardless of whether those are migrated over or used for scaling out. That means that the underlying storage technology must be prepared to handle these tasks easily.

These are just the most significant requirements, but there are more:

1. Stateless nature: Serverless functions spin up, execute, and terminate due to their stateless nature. Without fast, persistent storage that can be attached or accessed without any additional delay, this fundamental property of serverless functions would become a struggle.
2. Scalability needs: Serverless compute is built to scale automatically based on user demand. A storage layer needs to seamlessly support the growth and shrinking of serverless infrastructures and handle variations in I/O patterns, meaning that traditional storage systems with fixed capacity limits don’t align well with the requirements of serverless workloads.
3. Cost efficiency: One reason people engage with serverless compute solutions is cost efficiency. Serverless compute users pay by actual execution time. That means that serverless storage must support similar payment structures and help serverless infrastructure operators efficiently manage and scale their storage capacities and performance characteristics.
4. Management overhead: Serverless compute environments are designed to eliminate manual server management. Therefore, the storage solution needs to minimize its manual administrative tasks. Allocating and scaling storage requirements must be fully integratable and automated via API calls or fully automatic. Also, the integration must be seamless if multiple storage tiers are available for additional cost savings.
5. Performance requirements: Serverless functions require fast, if not immediate, access to data when they spin up. Traditional storage solutions introduce delays due to allocation and additional latency, negatively impacting serverless functions’ performance. As functions are paid by runtime, their operational cost increases.
6. Integration needs: Serverless architectures typically combine many services, as individual functions use different services. That said, the underlying storage solution of a serverless environment needs to support all kinds of services provided to users. Additionally, seamless integration with the management services of the serverless platform is required.

There are quite some requirements. For the alignment of serverless compute and serverless storage, storage solutions need to provide an efficient and manageable layer that seamlessly integrates with the overall management layer of the serverless platform.

Simplyblock for Serverless Storage

When designing a serverless environment, the storage layer must be designed to keep up with the pace. Simplyblock enables serverless infrastructures to provide dynamic and scalable storage.

To achieve this, simplyblock provides several characteristics that perfectly align with serverless principles:

1. Dynamic resource allocation: Simplyblock’s thin provisioning makes capacity planning irrelevant. Storage is allocated on-demand as data is written, similar to how serverless platforms allocate resources. That means every volume can be arbitrarily large to accommodate unpredictable future growth. Additionally, simplyblock’s logical volumes are resizable, meaning that the volume can be enlarged at any point in the future.
2. Automatic scaling: Simplyblock’s storage engine can indefinitely grow. To acquire additional backend storage, simplyblock can automatically acquire additional persistent disks (like Amazon EBS volumes) from cloud providers or attach additional storage nodes to its cluster when capacity is about to exceed, handling scaling without user intervention.
3. Abstraction of infrastructure: Users interact with simplyblock’s virtual drives like normal hard disks. This abstracts away the complexity of the underlying storage pooling and backend storage technologies.
4. Unified interface: Simplyblock provides a unified storage interface (NVMe) logical device that abstracts away underlying, diverging storage interfaces behind an easy-to-understand disk design. That enables services not specifically designed to talk to object storages or similar technologies to immediately benefit from them, just like PostgreSQL or MySQL.
5. Extensibility: Due to its disk-like storage interface, simplyblock is highly extensible in terms of solutions that can be run on top of it. Databases, object storage, file storage, and specific storage APIs, simplyblock provides scalable block storage to all of them, making it the perfect backend solution for serverless environments.
6. Crash-consistent and recoverable: Serverless storage must always be up and running. Simplyblock’s distributed erasure coding (parity information similar to RAID-5 or 6) enables high availability and fault tolerance on the storage level with a high storage efficiency, way below simple replication. Additionally, simplyblock provides storage cluster replication (sync / async), consistent snapshots across multiple logical volumes, and disaster recovery options.
7. Automated management: With features like automatic storage tiering to cheaper object storage (such as Amazon S3), automatic scaling, as well as erasure coding and backups for data protection, simplyblock eliminates manual management overhead and hands-on tasks. Simplyblock clusters are fully autonomous and manage the underlying storage backend automatically.
8. Flexible integration: Serverless platforms require storage to be seamlessly allocated and provisioned. Simplyblock achieves this through its API, which can be integrated into the standard provisioning flow of new customer sign-ups. If the new infrastructure runs on Kubernetes, integration is even easier with the Kubernetes CSI driver, allowing seamless integration with container-based serverless platforms such as knative.
9. Pay-per-use potential: Due to the automatic scalability, thin provisioning, and seamless resizing and integration, simplyblock enables you to provide your customers with an industry-loved pay-by-use model for managed service providers, perfectly aligning with the most common serverless pricing models.

Simplyblock is the perfect backend storage for all your serverless storage needs while future-proofing your infrastructure. As data grows and evolves, simplyblock’s flexibility and scalability ensure you can adapt without massive overhauls or migrations.

Remember, simplyblock offers powerful features like thin provisioning, storage pooling, and tiering, helping you to provide a cost-efficient, pay-by-use enabled storage solution. Get started now and find out how easy it is to operate services on top of simplyblock.

The post Serverless Compute Need Serverless Storage appeared first on simplyblock.

In this episode of Cloud Frontier, Rob Pankow interviews Elias Schneider, founder of Codesphere, about his journey and the evolution of developer platforms at scale. With a background at Google, Elias brings deep expertise in cloud-native development processes. They discuss the challenges of building large-scale developer platforms and why enterprise customers are crucial for scaling such solutions

This interview is part of the simplyblock Cloud Frontier Podcast, available on Youtube, Spotify, iTunes/Apple Podcasts, and our show site.

Key Takeaways

Q: What are the current trends in cloud migration and infrastructure management?

One major trend is the shift back to on-premise infrastructure from the cloud, driven by rising cloud costs and increased control requirements. Many enterprises are adopting a hybrid approach, keeping some workloads on-prem while utilizing cloud services for scaling and fluctuating demands. This allows businesses to balance cost and performance while managing regulatory concerns.

Q: Why is it important to use managed services in cloud environments?

Managed services in cloud environments allow companies to offload the complexity of infrastructure management. This includes automatic updates, monitoring, and scaling, which reduces the need for dedicated personnel and ensures the infrastructure runs efficiently. Without managed services, companies face increased operational overhead and risk of downtime.

In addition to highlighting the key takeaways, it’s essential to provide context that enriches the listener’s understanding of the episode. By offering this added layer of information, we ensure that when you tune in, you’ll have a clearer grasp of the nuances behind the discussion. This approach helps shed light on the reasoning and perspective behind the thoughtful questions posed by our host, Rob Pankow. Ultimately, this allows for a more immersive and insightful listening experience.

Key Learnings

Q: What are the benefits of developers managing their own cloud environments?

Allowing developers to manage their own cloud environments enables faster iterations and more autonomy. It eliminates the need for constant back-and-forth with DevOps teams, which can slow down development. Developers can directly deploy, test, and scale applications, which leads to more agile development cycles.

Simplyblock Insight: When developers have control over their own environments, the development cycle speeds up significantly. Simplyblock’s orchestration tools simplify the deployment and management process, enabling developers to maintain performance and scalability while reducing the overhead typically associated with infrastructure management.

Q: What are the main challenges companies face with cloud scalability?

One major challenge with cloud scalability is managing the complexity of infrastructure as the number of services and applications grows. Many companies struggle with orchestrating resources efficiently, leading to cost overruns and increased downtime. Additionally, scaling globally while maintaining performance and compliance can be difficult without the right tools.

Simplyblock Insight: Ensuring optimal performance while scaling requires intelligent automation and resource orchestration. Simplyblock helps companies optimize storage and performance across distributed environments, automating resource allocation to reduce costs and prevent performance bottlenecks as businesses scale.

Q: What role does infrastructure sovereignty play in cloud adoption?

Infrastructure sovereignty refers to the ability of a company to maintain control over its infrastructure, especially when operating across public and private clouds. This is particularly important for enterprises facing regulatory constraints or data sovereignty laws that require specific handling of sensitive information.

Simplyblock Insight: With hybrid cloud setups becoming more common, maintaining control over where and how data is stored is crucial. Simplyblock offers solutions that allow businesses to manage data across multiple infrastructures, ensuring compliance with data regulations while optimizing performance and cost-efficiency.

Additional Nugget of Information

Q: Why are hybrid cloud solutions becoming more popular?

As companies scale their cloud operations, hybrid cloud solutions are becoming increasingly popular. A hybrid approach allows businesses to combine the benefits of on-premise infrastructure with cloud services, offering more flexibility, better cost management, and the ability to meet regulatory requirements. This approach enables companies to maintain control over critical workloads while benefiting from the scalability of the cloud.

Conclusion

In this episode, Elias Schneider shares his journey from Google to founding Codesphere, emphasizing the importance of addressing the needs of large enterprises. Codesphere helps companies standardize their development processes, enabling faster deployments and reducing costs. As you think about your company’s cloud strategy, consider how platforms like Codesphere can offer scalability, sovereignty, and streamlined processes.

If you’re in the process of scaling your development or infrastructure, now is the time to explore solutions that empower your developers and improve operational efficiency. Whether you are considering hybrid cloud solutions or simply aiming to enhance your development workflows, the insights from this episode provide valuable guidance.

If you’re eager to learn more about founding early-stage cloud infrastructure startups, entrepreneurship, or taking visionary ideas to market, be sure to tune in to future episodes of the Cloud Frontier Podcast.

Stay updated with expert insights that can help shape the next generation of cloud infrastructure innovations!

The post Developer Platforms at Scale | Elias Schneider appeared first on simplyblock.

When we hear about hoarding goods, we often think of so-called preppers preparing for some type of serious event. Many people would laugh about that kind of behavior. However, it is commonplace when we are talking about cloud environments.

In the past, most workloads used their own servers, often barely utilizing any of the machines. That’s why we invented virtualization techniques, first with virtual machines and later with containers. We didn’t like the idea of wasting resources and money.

That didn’t stop when workloads were moved to the cloud, or did it?

What is Over-Provisioning?

As briefly mentioned above, over-provisioning refers to allocating more resources than are needed for a given workload or application. That means we actively request more resources than we need, and we know it. Over-provisioning typically occurs across various infrastructure components: CPU, memory, and storage. Let’s look at some basic examples to understand what that means:

1. CPU Over-Provisioning: Imagine running a web server on a virtual machine instance (e.g., Amazon EC2) with 16 vCPUs. At the same time, your application only requires four vCPUs for the current load and number of customers. You expect to increase the number of customers in the next year or so. Until then, the excess computing power sits idle, wasting resources and money.
2. Memory Over-Provisioning: Consider a database server provisioned with 64GB of RAM when the database service commonly only uses 16GB, except during peak loads. The unused memory is essentially paid for but unutilized most of the time.
3. Storage Over-Provisioning: Consider a Kubernetes cluster with ten instances of the same stateful service (like a database), each requesting a block storage volume (e.g., Amazon EBS) of 100 GB but will only slowly fill it up over the course of a year. In this case, each container uses about 20 GB as of now, meaning we over-provisioned 800 GB, and we have to pay for it.

Why is EBS Over-Provisioning an Issue?

EBS Over-provisioning isn’t an issue by itself, and we lived happily ever after (almost) with it for decades. While over-provisioning seems to be the safe bet to ensure performance and plannability, it comes with a set of drawbacks.

1. High initial cost: When you overprovision, you pay for resources you don’t use from day one. This can significantly inflate your cloud bill, especially at scale.
2. Resource waste: Unused resources aren’t just a financial burden. They also waste valuable computing power that could be better allocated elsewhere. Not to mention the environmental effects of over-provisioning, think CO2 footprint.
3. Hard to estimate upfront: Predicting exact resource needs is challenging, especially for new applications or those with variable workloads. This uncertainty often leads us to very conservative (and excessive) provisioning decisions.
4. Limitations when resizing: While cloud providers like AWS allow resource resizing, limitations exist. Amazon EBS volumes can only be modified every 6 hours, making it difficult to adjust to changing needs quickly.

On top of those issues, which are all financial impact related, over-provisioning can also directly or indirectly contribute to topics such as:

• Reduced budget for innovation
• Complex and hard-to-manage infrastructures
• Potential compliance issues in regulated industries
• Decreased infrastructure efficiency

The Solution is Pay-By-Use

Pay-by-use refers to the concept that customers are billed only for what they actually use. That said, using our earlier example of a 100 GB Amazon EBS volume where only 20 GB is used, we would only be charged for those 20 GB. As a customer, I’d love the pay-by-use option since it makes it easy and relieves me of the burden of the initial estimate.

So why isn’t everyone just offering pay-by-use models?

The Complexity of Pay-By-Use

Many organizations dream of an actual pay-by-use model, where they only pay for the exact resources consumed. This improves the financial impact, optimizes the overall resource utilization, and brings environmental benefits. However, implementing this is challenging for several reasons:

1. Technical Complexity: Building a system that can accurately measure and bill for precise resource usage in real time is technically complex.
2. Performance Concerns: Constant scaling and de-scaling to match exact usage can potentially impact performance and introduce latency.
3. Unpredictable Costs: While pay-by-use can save money, it can also make costs less predictable, making budgeting challenging.
4. Legacy Systems: Many existing applications aren’t designed to work with dynamically allocated resources.
5. Cloud Provider Greed: While this is probably exaggerated, there is still some truth. Cloud providers overcommit CPU, RAM, and network bandwidth, which is why they offer both machine types with dedicated resources and ones without (where they tend to over-provision resources, and you might encounter the “noisy neighbor” problem). On the storage side, they thinly provision your storage out of a large, ever-growing storage pool.

Over-Provisioning in AWS

Like most cloud providers, AWS has several components where over-provisioning is typical. The most obvious one is resources around Amazon EC2. However, since many other services are built upon EC2 machines (like Kubernetes clusters), this is the most common entry point to look into optimization.

Amazon EC2 (CPU and Memory)

When looking at Amazon EC2 instances to save some hard-earned money, AWS offers some tools by itself:

• Use AWS CloudWatch to monitor CPU and memory utilization.
• Implement auto-scaling groups to adjust instance counts dynamically based on demand.
• Consider using EC2 Auto Scaling with predictive scaling to anticipate future needs.

In addition, some external tools, such as AutoSpotting or Cast.ai, enable you to find over-provisioned VMs and adjust them accordingly automatically or exchange them with so-called spot instances. Spot instances are VM instances that are way cheaper but can be taken away from you with only a few seconds’ notice. The idea is that AWS offers these instances at a reduced rate when they can’t be sold for their regular price. That said, if the capacity is required, they’ll take them away from you—still a great way to save some money.

Last but not least, companies like DoIT work as resellers for hyperscalers like AWS. They have custom rates and offer additional features like bursting beyond your typical requirements. This is a great way to get cheaper VMs and extra services. It’s worth a look.

Amazon EBS Storage Over-Provisioning

One of the most common causes of over-provisioning happens with block storage volumes, such as Amazon EBS. With EBS, the over-provisioning is normally driven by:

• Pre-allocated Capacity: EBS volumes are provisioned with a fixed size, and you pay for the entire allocated space regardless of usage.
• Modification Limitations: EBS volumes can only be modified every 6 hours, making rapid adjustments difficult.
• Performance Considerations: A common belief is that larger volumes perform better, so people feel incentivized to over-provision.

One interesting note, though, is that while customers have to pay for the total allocated size, AWS likely uses technologies such as thin provisioning internally, allowing it to oversell its actual physical storage. Imagine this overselling margin would be on your end and not the hyperscaler.

How Simplyblock Can Help with EBS Storage Over-Provisioning

Simplyblock offers an innovative storage optimization platform to address storage over-provisioning challenges. By providing you with a comprehensive set of technologies, simplyblock enables several features that significantly optimize storage usage and costs.

Thin Provisioning

Thin provisioning is a technique where a storage entity of any capacity will be created without pre-allocating the requested capacity. A thinly provisioned volume will only require as much physical storage as the data consumes at any point in time. This enables overcommitting the underlying storage, like ten volumes with a provisioned capacity of 1 TB each. Still, only 100GB being used will require around 1 TB at this time, meaning you can save around 9 TB of storage that is not paid for unless used.

Simplyblock’s thin provisioning technology allows you to create logical volumes of any size without pre-allocating the total capacity. You only consume (and pay for) the actual space your data uses. This eliminates the need to over-provision “just in case” and allows for more efficient use of your storage resources. When your actual storage requirements increase, simplyblock automatically allocates additional underlying storage to keep up with your demands.

Copy-on-Write, Snapshots, and Instant Clones

Simplyblock’s storage technology is a fully copy-on-write-enabled system. Copy-on-write is a technique also known as shadowing. Instead of copying data right away when multiple copies are created, copy-on-write will only create a second instance when the data is actually changed. This means the old version is still around since other copies still refer to it, while only one specific copy refers to the changed data. Copy-on-write enables the instant creation of volume snapshots and clones without duplicating data. This is particularly useful for development and testing environments, where multiple copies of large datasets are often needed. Instead of provisioning full copies of production data, you can create instant, space-efficient clones specifically attractive for databases, AI / ML workloads, or analytics data.

Transparent Tiering

With most data sets, parts of the data are typically assumed to be “cold,” meaning that the data is very infrequently used, if ever. This is true for any data that needs to be kept available for regulatory reasons or historical manufacturing data (such as process information for car part manufacturing). This data can be moved to slower but much less expensive storage options. Simplyblock automatically moves infrequently accessed data to cheaper storage tiers such as object storage (e.g., Amazon S3 or MinIO) and non-NVMe SSD or HDD pools while keeping hot data on high-performance storage. This tiering is completely transparent to your applications, database, or other workload and helps optimize costs without sacrificing performance. With tiering integrated into the storage layer, application and system developers can focus on business logic rather than storage requirements.

Storage Pooling

Storage pooling is a technique in which multiple storage devices or services are used in conjunction. It enables technologies like thin provisioning and data tiering, which were already mentioned above.

By pooling multiple cloud block storage volumes (e.g., Amazon EBS volumes), simplyblock can provide better performance and more flexible scaling. This pooling allows for more granular storage growth, preventing the provision of large EBS volumes upfront.

Additionally, simplyblock can leverage directly attached fast SSD storage (NVMe), also called local instance storage, and make it part of the storage pool or use it as an even faster workload-local data cache.

NVMe over Fabrics

NVMe over Fabrics is an industry-standard for remotely attaching block devices to clients. It can be assumed to be the successor of iSCSI and enables the full feature set and performance of NVMe-based SSD storage. Simplyblock uses NVMe over Fabrics (specifically the NVMe/TCP version) to provide high-performance, low-latency access to storage.

This enables the consolidation of multiple storage locations into a centralized one, enabling even greater savings on storage capacity and compute power.

Pay-By-Use Model Enablement

As stated above, pay-by-use models are a real business advantage, specifically for storage. Implementing a pay-by-use model in the cloud requires taking charge of how storage works. This is complex and requires a lot of engineering effort. This is where simplyblock helps bring a competitive advantage to your doorstep.

With its underlying technology and features such as thin provisioning, simplyblock makes it easier for managed service providers to implement a true pay-by-use model for their customers, giving you the competitive advantage at no extra cost or development effort, all fully transparent to your database or application workload.

AWS Storage Optimization with Simplyblock

By addressing the core issues of EBS over-provisioning, simplyblock helps reduce costs and improves overall storage efficiency and flexibility. For businesses struggling with storage over-provisioning in AWS, simplyblock offers a compelling solution to optimize their infrastructure and better align costs with actual usage.

In conclusion, while over-provisioning remains a significant challenge in AWS environments, particularly with storage, simplyblock paves the way for more efficient, cost-effective cloud storage optimization management. By combining advanced technologies with a deep understanding of cloud storage dynamics, simplyblock enables businesses to achieve the elusive goal of paying only for what they use without sacrificing performance or flexibility.

Take your competitive advantage and get started with simplyblock today.

The post AWS Storage Optimization: Avoid EBS Over-provisioning appeared first on simplyblock.

The debate over the optimal storage solution has been ongoing. Local instance storage on AWS (i.e. ephemeral NVMe disk attached to EC2 instance) brings remarkable cost-performance ratios. It offers 20 times better performance and 10 times lower access latency than EBS. It’s a powerhouse for quick, ephemeral storage needs. In simple words, local NVME disk is very fast and relatively cheap, but not scalable and not persistent.

Recently, Vantage posted an article titled “Don’t use EBS for Cloud Native Services“. We agree with this problem statement, however we also strongly believe that there is a better solution that using Local NVMe SSD Storage on AWS as an alternative to EBS. Local NVMe to EBS is not like comparing apples to apples, but more like apples to oranges.

The Local Instance NVMe Storage Advantage

Local storage on AWS excels in speed and cost-efficiency, delivering performance that’s 20 times better and latency that’s 10 times lower compared to EBS. For certain workloads with temporary storage needs, it’s a clear winner. But, let’s acknowledge the reasons why data centers have traditionally separated storage and compute.

Overcoming Traditional Challenges of Local Storage

Simplyblock provides an innovatrive approach that marries the cost and performance advantages of local instance storage with the benefits of pooled cloud storage. It offers the best of both worlds—high-speed, low-latency performance near to local storage, coupled with the robustness and flexibility of pooled cloud storage.

Why Choose simplyblock on AWS?

1. Performance and Cost Efficiency: Enjoy the benefits of local storage without compromising on scalability, reliability, and high availability.
2. Data Protection: simplyblock employs advanced data protection mechanisms,
   ensuring that your data survives any instance outage.
3. Seamless Operations: Upgrade and maintain compute instances without impacting storage, ensuring continuous operations.
4. Data Services Galore: Unlock the potential of various data services without affecting local CPU performance.

While local instance storage has its merits, the future lies in a harmonious blend of the speed of local storage and the resilience of cloud-pooled storage. With simplyblock, we transcend the limitations of local NVMe disk, providing you with a storage solution that’s not just powerful but also versatile, scalable, and intelligently designed for the complexities of the cloud era.

The post Local NVMe Storage on AWS – Pros and Cons appeared first on simplyblock.

The Database Dilemma: Managed Services or Self-Managed?

The first crucial decision comes when choosing the operating model for your databases: should you opt for managed services like AWS RDS or take the reins yourself with a self-managed solution on Kubernetes? It’s not just about the upfront costs – there’s a whole iceberg of expenses lurking beneath the surface.

The Allure of Managed Services

At first glance, managed services like AWS RDS seem to be a no-brainer. They promise hassle-free management, automatic updates, and round-the-clock support. But is it really as rosy as it seems?

The Visible Costs

1. Subscription Fees : You’re paying for the convenience, and it doesn’t come cheap.
2. Storage Costs : Every gigabyte counts, and it adds up quickly.
3. Data Transfer Fees : Moving data in and out? Be prepared to open your wallet.

The Hidden Expenses

1. Overprovisioning : Are you paying for more than you are actually using?
2. Personnel costs : Using RDS and assuming that you don’t need to understand databases anymore? Surprise! You still need team that will need to configure the database and set it up for your requirements.
3. Performance Limitations : When you hit a ceiling, scaling up can be costly.
4. Vendor Lock-in : Switching providers? That’ll cost you in time and money.
5. Data Migration : Moving data between services can cost a fortune.
6. Backup and Storage : Those “convenient” backups? They’re not free. In addition, AWS RDS does not let you plug in other storage solution than AWS-native EBS volumes, which can get quite expensive if your database is IO-intensive

The Power of Self-Managed Kubernetes Databases

On the flip side, managing your databases on Kubernetes might seem daunting at first. But let’s break it down and see where you could be saving big.

Initial Investment

1. Learning Curve : Yes, there’s an upfront cost in time and training. You need to have on your team engineers that are comfortable with Kubernetes or Amazon EKS.
2. Setup and Configuration : Getting things right takes effort, but it pays off.

Long-term Savings

1. Flexibility : Scale up or down as needed, without overpaying.
2. Multi-Cloud Freedom : Avoid vendor lock-in and negotiate better rates.
3. Resource Optimization : Use your hardware efficiently across workloads.
4. Resource Sharing : Kubernetes lets you efficiently allocate resources.
5. Open-Source Tools : Leverage free, powerful tools for monitoring and management.
6. Customization : Tailor your setup to your exact needs, no compromise.

Where are the Savings Coming from when using Kubernetes for your Database Management?

In a self-managed Kubernetes environment, you have greater control over resource allocation, leading to improved utilization and efficiency. Here’s why:

Self-managed Kubernetes databases can also significantly reduce data transfer costs compared to managed services. Here’s how:

By leveraging these strategies in a self-managed Kubernetes environment, organizations can significantly optimize their resource usage and reduce data transfer costs, leading to substantial savings compared to typical managed database services.

Breaking down the Numbers: a Cost Comparison between PostgreSQL on RDS vs EKS

Let’s get down to brass tacks. How do the costs really stack up? We’ve crunched the numbers for a small Postgres database between using managed RDS service and hosting on Kubernetes. For Kubernetes we are using EC2 instances with local NVMe disks that are managed on EKS and simplyblock as storage orchestration layer.

Scenario: 3TB Postgres Database with High Availability (3 nodes) and Single AZ Deployment

Managed Service (AWS RDS) using three Db.m4.2xlarge on Demand with Gp3 Volumes

Editorial: See the pricing calculator for Amazon RDS for PostgreSQL

Self-Managed on Kubernetes (EKS) using three i3en.xlarge Instances on Demand

That’s a whopping 48% saving over three years! But wait, there’s more to consider. We have made some simplistic assumptions to estimate additional benefits of self-hosting to showcase the real potential of savings. While the actual efficiencies may vary from company to company, it should at least give a good understanding of where the hidden benefits might lie.

Additional Benefits of Self-Hosting (Estimated Annual Savings)

1. Resource optimization/sharing : Assumption: 20% better resource utilization (assuming existing Kubernetes clusters) Estimated Annual Saving: 20% x 989.88 x 12= $2,375
2. Reduced Data Transfer Costs : Assumption: 50% reduction in data transfer fees Estimated Annual Saving: $2,000
3. Flexible Scaling : Avoid over-provisioning during non-peak times Estimated Annual Saving: $3,000
4. Multi-Cloud Strategy : Ability to negotiate better rates across providers Estimated Annual Saving: $5,000
5. Open-Source Tools : Reduced licensing costs for management tools Estimated Annual Saving: $4,000

Disaster Recovery Insights

• RTO (Recovery Time Objective) Improvement : Self-managed: Potential for 40% faster recovery Estimated value: $10,000 per hour of downtime prevented
• RPO (Recovery Point Objective) Enhancement : Self-managed: Achieve near-zero data loss Estimated annual value: $20,000 in potential data loss prevention

Total Estimated Annual Benefit of Self-Hosting

Self-hosting pays off. Here is the summary of benefits: Base Savings: $8,400/year Additional Benefits: $15,920/year Disaster Recovery Improvement: $30,000/year (conservative estimate)

Total Estimated Annual Additional Benefit: $54,695

Total Estimated Additional Benefits over 3 Years: $164,085

Note: These figures are estimates and can vary based on specific use cases, implementation efficiency, and negotiated rates with cloud providers.

Beyond the Dollar Signs: the Real Value Proposition

Money talks, but it’s not the only factor in play. Let’s look at the broader picture.

Performance and Scalability

With self-managed Kubernetes databases, you’re in the driver’s seat. Need to scale up for a traffic spike? Done. Want to optimize for a specific workload? You’ve got the power.

Security and Compliance

Think managed services have the upper hand in security? Think again. With self-managed solutions, you have granular control over your security measures. Plus, you’re not sharing infrastructure with unknown entities.

Innovation and Agility

In the fast-paced tech world, agility is king. Self-managed solutions on Kubernetes allow you to adopt cutting-edge technologies and practices without waiting for your provider to catch up.

Is the Database on Kubernetes for Everyone?

Definitely not. While self-managed databases on Kubernetes offer significant benefits in terms of cost savings, flexibility, and control, they’re not a one-size-fits-all solution. Here’s why:

• Expertise: Managing databases on Kubernetes demands a high level of expertise in both database administration and Kubernetes orchestration. Not all organizations have this skill set readily available. Self-management means taking on responsibilities like security patching, performance tuning, and disaster recovery planning. For smaller teams or those with limited DevOps resources, this can be overwhelming.
• Scale of operations : For simple applications with predictable, low-to-moderate database requirements, the advanced features and flexibility of Kubernetes might be overkill. Managed services could be more cost-effective in these scenarios. Same applies for very small operations or startups in early stages – the cost benefits of self-managed databases on Kubernetes might not outweigh the added complexity and resource requirements.

While database management on Kubernetes offers compelling advantages, organizations must carefully assess their specific needs, resources, and constraints before making the switch. For many, especially larger enterprises or those with complex, dynamic database requirements, the benefits can be substantial. However, others might find that managed services better suit their current needs and capabilities.

Bonus: Simplyblock

There is one more bonus benefit that you get when running your databases in Kubernetes – you can add simplyblock as your storage orchestration layer behind a single CSI driver that will automatically and intelligently serve storage service of your choice. Do you need fast NVMe cache for some hot transactional data with random IO but don’t want to keep it hot forever? We’ve got you covered!

Simplyblock is an innovative cloud-native storage product, which runs on AWS, as well as other major cloud platforms. Simplyblock virtualizes, optimizes, and orchestrates existing cloud storage services (such as Amazon EBS or Amazon S3) behind a NVMe storage interface and a Kubernetes CSI driver. As such, it provides storage for compute instances (VMs) and containers. We have optimized for IO-heavy database workloads, including OLTP relational databases, graph databases, non-relational document databases, analytical databases, fast key-value stores, vector databases, and similar solutions.

This optimization has been built from the ground up to orchestrate a wide range of database storage needs, such as reliable and fast (high write-IOPS) storage for write-ahead logs and support for ultra-low latency, as well as high IOPS for random read operations. Simplyblock is highly configurable to optimally serve the different database query engines.

Some of the key benefits of using simplyblock alongside your stateful Kubernetes workloads are:

• Cost Reduction, Margin Increase: Thin provisioning, compression, deduplication of hot-standby nodes, and storage virtualization with multiple tenants increases storage usage while enabling gradual storage increase.
• Easy Scalability of Storage: Single node databases require highly scalable storage (IOPS, throughput, capacity) since data cannot be distributed to scale. Simplyblock pools either Amazon EBS volumes or local instance storage from EC2 virtual machines and provides a scalable and cost effective storage solution for single node databases.
• Enables Database Branching Features: Using instant snapshots and clones, databases can be quickly branched out and provided to customers. Due to copy-on-write, the storage usage doesn’t increase unless the data is changed on either the primary or branch. Customers could be charged for “additional storage” though.
• Enhances Security: Using an S3-based streaming of a recovery journal, the database can be quickly recovered from full AZ and even region outages. It also provides protection against typical ransomware attacks where data gets encrypted by enabling Point-in-Time-Recovery down to a few hundred milliseconds granularity.

Conclusion: the True Cost Revealed

When it comes to database management, the true cost goes far beyond the monthly bill. By choosing a self-managed Kubernetes solution, you’re not just saving money – you’re investing in flexibility, performance, and future-readiness. The savings and benefits will be always use-case and company-specific but the general conclusion shall remain unchanged. While operating databases in Kubernetes is not for everyone, for those who have the privilege of such choice, it should be a no-brainer kind of decision.

The post RDS vs. EKS: The True Cost of Database Management appeared first on simplyblock.

Why Migrate to AWS?

Migrating to AWS offers numerous benefits, including scalability, cost savings, improved performance, and enhanced security. AWS’s extensive range of storage solutions caters to diverse needs, from simple object storage to high-performance block storage. By leveraging AWS’s robust infrastructure, businesses can focus on innovation and growth without worrying about underlying IT challenges.

Understanding AWS Storage Options

Before diving into the migration process, it’s crucial to understand the various storage options AWS offers:

• Amazon S3 (Simple Storage Service) Amazon S3 is an object storage service that provides scalability, data availability, security, and performance. It’s ideal for storing and retrieving data at any time.
• Amazon EBS (Elastic Block Store) Amazon EBS provides block storage for EC2 instances. It’s suitable for applications requiring low-latency data access and offers different volume types optimized for performance and cost.
• Amazon EFS (Elastic File System) Amazon EFS is designed to be highly scalable and elastic. It provides scalable file storage for use with AWS Cloud services and on-premises resources.
• Amazon Glacier Amazon Glacier is a secure, durable, and extremely low-cost cloud storage service for data archiving and long-term backup. It’s ideal for data that is infrequently accessed

AWS provides several migration tools, such as AWS DataSync and AWS Snowball, to ensure a smooth and efficient data migration process. Based on your data volume and migration requirements, choose the right tool.

How is data stored in AWS? AWS stores the data of each storage service separately. That means that AWS storage services are not synchronized and your data might be frequently duplicated multiple times. Coordination between AWS storage services might be resolved using orchestration tools such as simplyblock.

Steps for Migrating to AWS

1. Assess your Current Environment

Begin by evaluating your current storage infrastructure. Identify the types of data you store, how often it’s accessed, and any compliance requirements. This assessment will help you choose the right AWS storage services for your needs.

2. Plan your Migration Strategy

Develop a comprehensive migration plan that outlines the steps, timelines, and resources required. Decide whether you’ll use a lift-and-shift approach, re-architecting, or a hybrid strategy.

3. Choose the right AWS Storage Services

Based on your assessment, select the appropriate AWS storage services. For instance, Amazon S3 can be used for object storage, EBS for block storage, and EFS for scalable file storage.

4. Set up the AWS Environment

Set up your AWS environment, including creating an AWS account, configuring Identity and Access Management (IAM) roles, and setting up Virtual Private Clouds (VPCs).

5. Use AWS Migration Tools

AWS offers several tools to assist with migration, such as

• AWS Storage Gateway, which bridges your on-premises data and AWS Cloud storage
• AWS DataSync automates moving data between on-premises storage and AWS
• AWS Snowball physically transports large amounts of data to AWS.

6. Migrate Data

Start migrating your data using the chosen AWS tools and services. Ensure data integrity and security during the transfer process. Test the migrated data to verify its accuracy and completeness.

7. Optimize Storage Performance

After migration, monitor and optimize your storage performance. Use AWS CloudWatch to track performance metrics and make necessary adjustments to enhance efficiency.

8. Ensure Data Security and Compliance

AWS provides various security features to protect your data, including encryption, access controls, and monitoring. Ensure your data meets regulatory compliance requirements.

9. Validate and Test

Conduct thorough testing to validate that your applications function correctly in the new environment. Ensure that data access and performance meet your expectations.

10. Decommission Legacy Systems

Once you’ve confirmed your data’s successful migration and testing, you can decommission your legacy storage systems. Ensure all data has been securely transferred and backed up before decommissioning.

Common Challenges in AWS Migration

1. Data Transfer Speed

Large data transfers can take time. Use tools like AWS Snowball for faster data transfer.

2. Data Compatibility

Ensure your data formats are compatible with AWS storage services. Consider data transformation if necessary.

3. Security Concerns

Data security is paramount. Utilize AWS security features such as encryption and IAM roles.

4. Cost Management

Monitor and manage your AWS storage costs. Use AWS Cost Explorer and set up budget alerts.

Benefits of AWS Storage Solutions

1. Scalability: AWS storage solutions scale according to your needs, ensuring you never run out of space.
2. Cost-Effectiveness: Pay only for the storage you actually use and leverage different storage tiers to optimize costs.
3. Reliability: AWS guarantees high availability and durability for your data.
4. Security: Robust security features protect your data against unauthorized access and threats.
5. Flexibility: Choose from various storage options for different workloads and applications.

Conclusion

Migrating to AWS from a storage perspective involves careful planning, execution, and optimization. By understanding the various AWS storage options and following a structured migration process, you can ensure a smooth transition to the cloud. AWS’s comprehensive suite of tools and services simplifies the migration journey, allowing you to focus on leveraging the cloud’s benefits for your business.

FAQs

How Simplyblock can be used with AWS Migration

Migrating to AWS can be a complex process, but using simplyblock can significantly simplify this journey while optimizing your costs, too.

Simplyblock software provides a seamless bridge between local NVMe disk, Amazon EBS, and Amazon S3, integrating these storage options into a cohesive system designed for the ultimate scale and performance of IO-intensive stateful workloads. By combining the high performance of local NVMe storage with the reliability and cost-efficiency of EBS (gp2 and gp3 volumes) and S3, respectively, simplyblock enables enterprises to optimize their storage infrastructure for stateful applications, ensuring scalability, cost savings, and enhanced performance. With simplyblock, you can save up to 80% of your AWS database storage costs.

Our technology uses NVMe over TCP for minimal access latency, high IOPS/GB, and efficient CPU core utilization, outperforming local NVMe disks and Amazon EBS in cost/performance ratio at scale. Ideal for high-performance Kubernetes environments, simplyblock combines the benefits of local-like latency with the scalability and flexibility necessary for dynamic AWS EKS deployments, ensuring optimal performance for I/O-sensitive workloads like databases. Using erasure coding (a better RAID) instead of replicas, simplyblock minimizes storage overhead while maintaining data safety and fault tolerance. This approach reduces storage costs without compromising reliability.

Simplyblock also includes additional features such as instant snapshots (full and incremental), copy-on-write clones, thin provisioning, compression, encryption, and many more – in short, there are many ways in which simplyblock can help you optimize your cloud costs. Get started using simplyblock right now and see how simplyblock can simplify and optimize your AWS migration. Simplyblock is available on AWS Marketplace.

The post AWS Migration: How to Migrate into the Cloud? Data Storage Perspective. appeared first on simplyblock.

The AWS Workload Migration Program is a comprehensive framework designed to help organizations migrate their workloads to the AWS cloud efficiently and effectively. It encompasses a range of tools, best practices, and services that streamline the migration process.

Key Features of the AWS Workload Migration Program

1. Comprehensive Migration Strategy: The program offers a step-by-step migration strategy tailored to meet the specific needs of different workloads and industries.
2. Robust Tools and Services: AWS provides a suite of robust tools and services, including AWS Migration Hub , AWS Application Migration Service, and AWS Database Migration Service, to facilitate smooth and secure migrations.

Benefits of using AWS Workload Migration Program

1. Reduced Migration Time: With pre-defined best practices and automated tools, the migration process is significantly faster, reducing downtime and disruption.
2. Minimized Risks: The program includes risk management strategies to ensure data integrity and security throughout the migration process.

Steps Involved in the AWS Workload Migration Program

1. Assessment Phase Evaluating Current Workloads: Assessing your current workloads to understand their requirements and dependencies is the first step in the migration process. Identifying Migration Objectives: Define clear objectives for what you want to achieve with the migration, such as improved performance, cost savings, or scalability.
2. Planning Phase Creating a Migration Plan: Develop a detailed migration plan that outlines the steps, timelines, and resources required for the migration. Defining Success Criteria: Establish success criteria to measure the effectiveness of the migration and ensure it meets your business goals.
3. Migration Phase Executing the Migration: Carry out the migration using AWS tools and services, ensuring minimal disruption to your operations. Ensuring Minimal Downtime: Implement strategies to minimize downtime during the migration, such as using live data replication and phased cutovers.
4. Optimization Phase Post-Migration Optimization: After migration, optimize your workloads for performance and cost-efficiency using AWS and simplyblock tools. Continuous Monitoring: Continuously monitor your workloads to ensure they are running optimally and to identify any areas for improvement.

Challenges in Cloud Migration

1. Common Migration Hurdles Data Security Concerns: Ensuring the security of data during and after migration is a top priority and a common challenge. Compatibility Issues: Ensuring that applications and systems are compatible with the new cloud environment can be complex.
2. Overcoming Migration Challenges Using the Right Tools: Leveraging the right tools, such as AWS Migration Hub and simplyblock’s storage solutions, can help overcome these challenges. Expert Guidance: Working with experienced cloud migration experts can provide the guidance needed to navigate complex migrations successfully.

Simplyblock and Cloud Migration

Introduction to Simplyblock

Simplyblock offers advanced AWS storage orchestration solutions designed to enhance the performance and reliability of cloud workloads. Simplyblock integrates seamlessly with AWS, making it easy to use their advanced storage solutions in conjunction with AWS services.

Key Benefits of using Simplyblock for Cloud Migration

1. Enhanced Performance: simplyblock’s advanced storage solutions deliver superior performance, reducing latency and increasing IOPS for your workloads, offering the benefits of storage tiering, thin provisioning, and multi-attach that are not commonly available in the cloud while a standard in private cloud data centers.
2. Improved Cost Efficiency: simplyblock helps you optimize storage costs while maintaining high performance, making cloud migration more cost-effective. You don’t have to pay more for storage in the cloud compared to your SAN system in private cloud.
3. Increased Reliability: simplyblock’s storage solutions offer high durability and reliability, ensuring your data is secure and available when you need it. You can optimize data durability to your needs. Simplyblock offers full flexibility in how the storage is orchestrated and provides various Disaster Recovery and Cybersecurity protection options.

Best Practices for Cloud Migration with Simplyblock

Pre-Migration Preparations

Assessing Storage Needs: Evaluate your storage requirements to choose the right simplyblock solutions for your migration. Data Backup Strategies: Implement robust data backup strategies to protect your data during the migration process.

Migration Execution

Using simplyblock Tools: Leverage simplyblock’s tools to streamline the migration process and ensure a smooth transition. Monitoring Progress: Continuously monitor the migration to identify and address any issues promptly.

Post-Migration Tips

Optimizing Performance: Optimize your workloads post-migration to ensure they are running at peak performance. Ensuring Data Security: Maintain stringent security measures to protect your data in the cloud environment.

Simplyblock integrates seamlessly with AWS, providing robust storage solutions that complement the AWS Workload Migration Program. Optimize your cloud journey with simplyblock.

Frequently Asked Questions (FAQs)

What is the AWS Workload Migration Program?

The AWS Workload Migration Program is a comprehensive framework designed to help organizations migrate their workloads to the AWS cloud efficiently and effectively.

How does Simplyblock Integrate with AWS?

Simplyblock integrates seamlessly with AWS, providing advanced storage solutions that enhance performance and reliability during and after migration.

What are the Key Benefits of using Simplyblock for Cloud Migration?

Using simplyblock for cloud migration offers enhanced performance, improved cost efficiency, and increased reliability, ensuring a smooth transition to the cloud.

How can Simplyblock Improve the Performance of Migrated Workloads?

Simplyblock can help lowerign access latency and providing high density of IOPS/GB, ensuring efficient data handling and superior performance for migrated workloads.

What are some Common Challenges in Cloud Migration and how does Simplyblock Address Them?

Common challenges in cloud migration include data security concerns and compatibility issues. Simplyblock addresses these challenges with robust security features, seamless AWS integration, and advanced storage solutions.

How Simplyblock can be used with Workload Migration Program

When migrating workloads to AWS, simplyblock can significantly optimize your storage infrastructure and reduce costs.

simplyblock is a cloud storage orchestration platform that optimizes AWS database storage costs by 50-75% . It offers a single interface to various storage services, combining the high performance of local NVMe disks with the durability of S3 storage. Savings are mostly achieved by:

1. Data reduction: Eliminating storage that you provision and pay for but do not use (thin provisioning)
2. Intelligent tiering: Optimizing data placement for cost and performance between various storage tiers (NVMe, EBS, S3, Glacier, etc)
3. Data efficiency features: Reducing data duplication on storage via multi-attach and deduplication

All services are accessible via a single logical interface (Kubernetes CSI or NVMe), fully abstracting cloud storage complexity from the database.

Our technology employs NVMe over TCP to deliver minimal access latency, high IOPS/GB, and efficient CPU core utilization, outperforming both local NVMe disks and Amazon EBS in cost/performance ratio at scale. It is particularly well-suited for high-performance Kubernetes environments, combining the low latency of local storage with the scalability and flexibility necessary for dynamic AWS EKS deployments . This ensures optimal performance for I/O-sensitive workloads like databases. Simplyblock also uses erasure coding (a more efficient alternative to RAID) to reduce storage overhead while maintaining data safety and fault tolerance, further lowering storage costs without compromising reliability.

Simplyblock offers features such as instant snapshots (full and incremental), copy-on-write clones, thin provisioning, compression, and encryption. These capabilities provide various ways to optimize your cloud costs. Start using simplyblock today and experience how it can enhance your AWS migration strategy . Simplyblock is available on AWS Marketplace.

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