How AWS Managed Service Providers Help Their Customer Optimize Cloud Spend and Improve Their Operation Margin with Federator.ai

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Introduction

Cloud computing has revolutionized how businesses operate, enabling them to access powerful computing resources on demand without investing in expensive hardware and infrastructure. As a result, Amazon Web Services (AWS) has emerged as a leader in the cloud computing market, offering a vast array of services and options to businesses of all sizes. According to Statista, as of the fourth quarter of 2022, AWS held the largest share of the worldwide cloud infrastructure market at 32%, which is significantly higher than its two closest competitors, Microsoft Azure (23%) and Google Cloud (10%).

Managed Service Providers (MSPs) are critical in providing AWS cloud services to end-users. MSPs can resell cloud instances, including On-Demand Instances, Reserved Instances, and Spot Instances, or provide value-added services to customers who are AWS users. However, selecting the optimal combination of instances to support an application with persuasive costs and benefits to MSPs’ revenue can be challenging. That’s where Federator.ai steps in to help.

Federator.ai is a cloud operation optimization platform that leverages advanced analytics to optimize AWS resource usage, reduce costs, and support application resilience. Its most straightforward application uses predictive and prescriptive analytics to understand workloads and determine the most efficient combination of On-Demand, Reserved, and Spot instances to support the application while minimizing costs.

By leveraging Federator.ai’s analytics capabilities, MSPs can help their customers optimize their cloud resource usage and provide more efficient and effective cloud services. This, in turn, can result in significant cost savings for the MSP and their customers while also ensuring application resilience and performance. Additionally, AWS applauded the efforts as they expect better resource planning with early commitment from their end users to handle better the data center’s growth and hardware/software expansion to support their operation.

This white paper briefly introduces the different types of AWS EC2 instances and their benefits. Then, it explains how MSPs can achieve a triple-win situation between AWS, MSPs, and their end-users with the help of Federator.ai’s patented Multi-Layer Correlation Analysis. In the end, MSPs can generate more revenue and higher margins and provide premium services to their end-users, resulting in loyal customers and business growth.

Understanding AWS Instance Types and Pricing Models

Amazon Elastic Compute Cloud (EC2) provides various virtual server instances to meet diverse computing needs. These instances are grouped into different families based on their characteristics, including the type of CPU, memory, storage, and network performance.

  1. General Purpose Instances are designed for various applications that require a balance of compute, memory, and network resources. These instances suit small to medium-sized databases, development and testing environments, and web servers.
  2. Compute Optimized Instances are designed for compute-intensive workloads that require high-performance processors. These instances suit high-performance computing, scientific modeling, batch processing, and media transcoding.
  3. Memory Optimized Instances are designed for memory-intensive workloads that require high memory capacity and fast memory access. These instances suit data analytics, in-memory databases, and real-time big data processing.
  4. Storage Optimized Instances are designed for workloads that require high-speed, low-latency storage, such as big data processing, data warehousing, and log processing.
  5. GPU Instances are designed for workloads that require high-performance graphics processing units (GPUs), such as machine learning, graphics rendering, and video encoding.
  6. High I/O Instances are designed for workloads that require high I/O performance and low-latency storage access, such as NoSQL databases and data warehousing.
  7. Bare Metal Instances provide direct access to the underlying hardware, allowing users to run applications that require access to the hardware features not available in virtualized environments. These instances suit applications requiring high-performance computing, real-time data processing, and specialized workloads.

In addition to these instance families, AWS offers various purchasing options for instances, including On-Demand Instances, Reserved Instances, and Spot Instances, which must be assigned intelligently to meet the workload demands.

AWS On-Demand Instances are a virtual server that runs on Amazon EC2, providing users with a flexible and scalable compute capacity. The key feature of On-Demand Instances is that they allow users to pay only for the compute capacity they use, with no upfront costs or long-term commitments required. This pay-as-you-go model means that users are only charged for the exact number of seconds or minutes their On-Demand Instances are running. Additionally, users have complete control over the lifecycle of their On-Demand Instances, with the ability to start, stop, hibernate, reboot, or terminate them as needed.

Amazon Reserved Instances (RIs) are a billing discount that can help you save on your Amazon EC2 usage costs. When you purchase a Reserved Instance, you commit to using a specific instance type in a particular AWS region for a predetermined period, which could be one or three years. Reserved Instances are not physical instances, but a billing discount applied to the running On-Demand Instances in your account. When you purchase a Reserved Instance, you’ll be charged an upfront fee, and you’ll receive a discounted hourly rate for the usage of the instance. Reserved Instances can significantly save your Amazon EC2 costs compared to On-Demand Instance pricing, up to 75% off the hourly rate. However, Reserved Instances are only cost-effective if you run instances for an extended period, as the upfront fee can be significant.

Amazon EC2 Spot Instances let you take advantage of unused EC2 capacity in the AWS cloud. Spot Instances are available at up to a 90% discount compared to On-Demand prices. You can use Spot Instances for stateless, fault-tolerant, or flexible applications such as big data, containerized workloads, CI/CD, web servers, high-performance computing (HPC), and test & development workloads. Because Spot Instances are tightly integrated with AWS services such as Auto Scaling, EMR, ECS, CloudFormation, Data Pipeline, and AWS Batch, you can choose how to launch and maintain your applications running on Spot Instances. In addition, you can easily combine Spot Instances with On-Demand, Reserved Instances, and Savings Plans Instances to further optimize workload cost with performance. Due to the operating scale of AWS, Spot Instances can offer scale and cost savings to run hyper-scale workloads. You also have the option to hibernate, stop, or terminate your Spot Instances when EC2 reclaims the capacity back with two minutes of notice.

AWS also offers different pricing models for its instances. On-Demand Instances are the most flexible option and charge users by the second or minute with no upfront costs or long-term commitments. Reserved Instances (RI) offer users a discount on the hourly rate in exchange for a commitment to use a specific instance type in a particular AWS region for one or three years. RIs can save up to 75% (with years of commitment from the users) compared to On-Demand pricing, but they are only cost-effective if the user runs instances for an extended period. Finally, spot Instances allow users to take advantage of unused EC2 capacity in the AWS cloud and are available at a discount of up to 90% compared to On-Demand pricing. However, the pricing for Spot Instances is variable and can change depending on supply and demand.

AWS users can also use a combination of instance types and pricing models to optimize cost and performance. For example, users can use On-Demand Instances for their baseline workload and add Spot Instances for burstable workloads when unused capacity exists. Alternatively, users can purchase RIs for their steady-state workloads and use Spot Instances for batch processing and other workloads tolerating interruptions.

In summary, AWS offers a range of instance types and pricing models to meet the diverse needs of its users. Understanding these options and choosing the right combination can help users optimize their workload performance and cost.

Leveraging Reserved Instances for Cost Savings

A. Explanation of Reserved Instances and how they work

Amazon Reserved Instances (RIs) are billing discounts allowing users to save on their Amazon EC2 usage costs. When a user purchases a Reserved Instance, they commit to using a specific instance type in a particular AWS region for a predetermined period, which can be one or three years. Reserved Instances are not physical instances, but a billing discount applied to the running On-Demand Instances in the user’s account. When a user purchases a Reserved Instance, they are charged an upfront fee and receive a discounted hourly rate for the usage of the instance.

B. Benefits of Reserved Instances

The main benefit of Reserved Instances is that they can significantly save Amazon EC2 costs compared to On-Demand Instance pricing. The discount can be up to 75% off the On-Demand Instance hourly rate, making Reserved Instances a cost-effective option for users who plan to use instances for an extended period. Additionally, Reserved Instances allow users to lock in capacity for their application workloads and provide greater cost predictability.

C. Use cases for Reserved Instances

Reserved Instances are ideal for users who run predictable workloads and are committed to using Amazon EC2 instances. They are beneficial for users who need to run instances continuously over a long period, such as production workloads or applications that require a dedicated infrastructure. Additionally, Reserved Instances can offset the costs of instances used in auto-scaling groups, where the instances are frequently started and stopped.

D. Best practices for using Reserved Instances

To get the most out of Reserved Instances, it’s essential to consider the following best practices:

  1. Identify and forecast long-term instance usage: Reserved Instances are most effective when users commit to using instances. Therefore, it’s crucial to identify and forecast long-term instance usage to determine whether a Reserved Instance makes sense for your workload.
  2. Match Reserved Instances to your workload requirements: Reserved Instances are associated with a specific instance type and region. To benefit from the discount, users must ensure that they are running instances that match the specifications of their Reserved Instance.
  3. Leverage Reserved Instances with other instance types: Users can further optimize workload cost with performance by combining Reserved Instances with other instance types, such as On-Demand or Spot Instances, using EC2 Auto Scaling.
  4. Monitor and adjust Reserved Instance coverage: As user workloads change, their needs may also change. Therefore, monitoring and adjusting Reserved Instance coverage is vital to ensure they are effectively using their capacity.

Overall, Reserved Instances can be an effective cost-saving strategy for users with a long-term commitment to using Amazon EC2 instances. By following best practices and considering use cases, users can maximize their cost savings while maintaining the flexibility and scalability offered by On-Demand and other instance types.

Utilizing AWS Spot Instances for Cost Savings

A. Explanation of Spot Instances and how they work AWS Spot

Instances allow users to take advantage of unused EC2 capacity in the AWS cloud, which can lead to significant cost savings. Spot Instances are available at a discount of up to 90% compared to On-Demand prices. However, because Spot Instances are unused capacity, their availability can be variable and not guaranteed.

To use Spot Instances, users bid on unused EC2 capacity in a particular region, specifying the maximum hourly price they’re willing to pay. If the current market price for the capacity falls below the user’s bid price, their instances will be launched and run until the user terminates them or the Spot price exceeds their bid price.

B. Benefits of Spot Instances

The primary benefit of Spot Instances is their cost savings potential, with discounts of up to 90% compared to On-Demand prices. Spot Instances are ideal for stateless, fault-tolerant, or flexible applications such as big data, containerized workloads, CI/CD, web servers, high-performance computing (HPC), and test & development workloads.

C. Use cases for Spot Instances

Spot Instances are beneficial for flexible workloads that do not concern when they can be run and how long they need to run. For example, they can be used for big data processing or HPC workloads that can be broken down into smaller jobs that can be completed in a variable amount of time.

Spot Instances are also ideal for applications that can tolerate interruptions, as they may be terminated if the Spot price exceeds the user’s bid price. Spot Instances are popular for fault-tolerant workloads (meaning that the end users need to take care of the possible fault by themselves) that can handle failures and interruptions gracefully.

D. Best practices for using Spot Instances

To make the most of Spot Instances, users should follow best practices such as using Auto Scaling groups to launch and manage Spot Instances, monitoring Spot prices and availability, setting up notifications and alarms for changes in Spot prices and availability, and using multiple Spot Instance types and availability zones to improve availability and reduce the risk of interruptions.

It’s also important to have a backup plan in case Spot Instances become unavailable or too expensive, such as using On-Demand or Reserved Instances as a fallback. By following these best practices, users can maximize the cost savings potential of Spot Instances while ensuring the availability and reliability of their workloads.

Predictive Analytics and Multi-layer Correlation Analysis for Cloud Resource Usage Planning

A. Explanation of Predictive Analytics and Multi-layer Correlation Analysis for Workload and Resource Usage

Predictive analytics is a technique that uses statistical algorithms and machine learning to analyze historical data and predict future events or behavior. For example, in cloud resource usage planning, predictive analytics can forecast future resource needs based on historical usage patterns and application behavior.

On the other hand, multi-layer correlation analysis is a patented technology developed by ProphetStor that can identify the correlations between different layers of the infrastructure stack, such as the application, virtualization layer, storage, and network. This technology identifies the root cause of performance issues and resource contention problems, allowing for more efficient resource allocation and capacity planning.

When used together, predictive analytics and multi-layer correlation analysis can provide valuable insights into workload and resource usage, enabling more informed decision-making regarding resource allocation and capacity planning.

B. Benefits of Using Predictive Analytics and Multi-layer Correlation Analysis

There are numerous benefits of using predictive analytics and multi-layer correlation analysis for cloud resource usage planning. Some of these benefits include:

  1. Improved resource utilization: Forecasting future resource needs and identifying the root cause of performance issues, predictive analytics, and multi-layer correlation analysis can help improve resource utilization, allowing for more efficient use of cloud resources.
  2. Cost savings: By accurately predicting future resource needs and using this information to make informed decisions about resource allocation, organizations can avoid overprovisioning and overspending on cloud resources.
  3. Improved application performance: By identifying the root cause of performance issues and resource contention problems, predictive analytics and multi-layer correlation analysis can help improve application performance, ensuring users have a better experience with cloud-based applications.
  4. Faster time-to-value: By leveraging historical operational metadata and using predictive analytics and multi-layer correlation analysis, organizations can achieve results more quickly, reducing the time-to-value for cloud-based applications and services.
Figure 1 A sample report showing the saving for end users and the added revenue for MSO based on Federator’s analysis and report
Figure 1 A sample report showing the saving for end users and the added revenue for MSO based on Federator’s analysis and report
Figure 2 Illustration of how MSP can help customers simultaneously optimize Cloud spend and their margin. Assuming the discount for Reserved Instances is 30%, and that for Spot Instances is 50%
Figure 2 Illustration of how MSP can help customers simultaneously optimize Cloud spend and their margin. Assuming the discount for Reserved Instances is 30%, and that for Spot Instances is 50%

C. Best Practices for Implementing Predictive Analytics and Multi-layer Correlation Analysis

To maximize the benefits of predictive analytics and multi-layer correlation analysis for cloud resource usage planning, organizations should follow some best practices, including:

  1. Choose a solution that integrates with your existing monitoring tools and services, such as Prometheus, Datadog, and Sysdig.
  2. Use historical operational metadata to train predictive models, allowing for more accurate forecasting of future resource needs.
  3. Regularly review and update predictive models to remain accurate and relevant.
  4. Use multi-layer correlation analysis to identify the root cause of performance issues and resource contention problems, allowing for more efficient resource allocation and capacity planning.
  5. Consider leveraging Reserved Instances and Spot Instances to optimize workload cost with performance and take advantage of the cost savings these instance types can offer.

By following these best practices, organizations can effectively leverage predictive analytics and multi-layer correlation analysis for planning cloud resource usage, achieving improved resource utilization, cost savings, application performance, and faster time-to-value.

Conclusion

In conclusion, Amazon EC2 instances provide users with a flexible and scalable compute capacity to meet various business needs. Furthermore, with various instance types and pricing options, users can optimize their workload performance and costs by selecting the most appropriate instances for their applications.

AWS offers various pricing models for EC2 instances, including On-Demand, Reserved, and Spot Instances. On-Demand Instances provide users with a flexible and cost-effective option to pay only for what they use, while Reserved Instances offer significant discounts for long-term usage. Spot Instances can provide up to a 90% discount compared to On-Demand pricing and are suitable for stateless, fault-tolerant, or flexible applications.

By leveraging the predictive analytics and patented Multi-layer correlation analysis of Federator.ai, MSPs can provide customers with a way to plan their cloud resource usage based on their application behaviors in the future and optimize their workload costs and performance. This is a win-win-win situation for MSPs, end-users, and AWS, as it can lead to higher margins for MSPs and better resource planning and bargaining power for AWS.

In summary, Amazon EC2 instances offer a wide range of compute capacity and pricing options to meet the diverse needs of users. MSPs can help end-users optimize their usage of EC2 instances. AWS can benefit from better resource planning by providing a range of pricing models and integrating with predictive analytics solutions like Federator.ai. MSP can benefit from Federator.ai with more revenue and a higher margin by applying a better combination of different types and the number of instances for the customer’s workload to ensure cost saving and resilience. All these can be achieved without incurring additional technical talents that are becoming more challenging to get in the market.

References

  1. Amazon Web Services. (2022). EC2 pricing. Retrieved from https://aws.amazon.com/ec2/pricing/
  2. AWS Support. (2022). AWS Support pricing. Retrieved from https://aws.amazon.com/premiumsupport/pricing/
  3. ProphetStor Data Services, Inc. (2022). Federator.ai. Retrieved from https://prophetstor.com/products/federator-ai/
  4. ProphetStor. (2023). Method for Establishing System Resource Prediction and Resource Management Model through Multi-Layer Correlations. US Patent No. 11,579,933 B2. Retrieved from https://image-ppubs.uspto.gov/dirsearch-public/print/downloadPdf/11579933
  5. Amazon Web Services. (2022). Amazon EC2 Instance Types. Retrieved from https://aws.amazon.com/ec2/instance-types/
  6. Amazon Web Services. (2022). Amazon EC2 Reserved Instances Pricing. Retrieved from https://aws.amazon.com/ec2/pricing/reserved-instances/pricing/
  7. Amazon Web Services. (2022). Amazon EC2 Spot Instances Pricing. Retrieved from https://aws.amazon.com/ec2/spot/pricing/
  8. Amazon Web Services. (2022). Amazon EC2 Spot Instances. Retrieved February 18, 2023, from https://aws.amazon.com/ec2/spot/
  9. Amazon Web Services. (2022). AWS On-Demand Instances. Retrieved February 18, 2023, from https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/ec2-on-demand-instances.html
  10. Amazon Web Services. (n.d.). Amazon Lightsail for Research. Retrieved February 18, 2023, from https://aws.amazon.com/lightsail/research/
  11. Talukder, A. (2020, October 12). Understanding Amazon EC2 Reserved Instances (RIs). Amazon Web Services. Retrieved February 18, 2023, from https://aws.amazon.com/premiumsupport/knowledge-center/ec2-ri-basics/