AWS Storage Auto Scaling Best Practices

Amazon Web Services (AWS) offers Auto Scaling—a valuable tool for managing varying web application traffic. For applications experiencing fluctuating usage throughout the day, especially with peak periods, Auto Scaling efficiently adjusts the number of EC2 instances, ensuring optimal resource allocation in response to demand changes.

However, while Auto Scaling effectively manages compute resources, its oversight of storage resources presents a key challenge.

This blog aims to provide a comprehensive understanding of Auto Scaling fundamentals while delving into the importance and methods of auto-scaling AWS storage.

Introduction To AWS Auto Scaling

AWS Auto Scaling simplifies the scaling procedure by offering suggestions to enhance performance, minimize expenses, or achieve a harmonious balance between the two aspects.

By utilizing AWS Auto Scaling, you can guarantee that your applications consistently access the appropriate resources when required, thereby enhancing efficiency and cost-effectiveness.

However, AWS Auto Scaling primarily focuses on scaling compute resources and provides limited support for storage scaling, emphasizing expansion only.

AWS auto scaling cannot automatically adapt storage capacity as it does with compute resources. Adjusting storage scaling requires a manual approach and relies on the particular AWS storage service being utilized. Take, for instance, EBS Volume.

To increase storage capacity, it may be necessary to configure larger EBS volumes manually. This involves detaching the current volume, creating a larger one, and attaching it to the respective instance. It is important to note that resizing the file system on the instance might also be required in certain situations.

Since auto-scaling storage resources mainly require manual intervention, they are time-consuming and prone to errors. Scaling storage resources with conventional methods often leads to either overprovisioning, which wastes valuable resources, or underprovisioning, causing performance bottlenecks.

This is why there is an urgent need for auto-scaling solutions to simplify the process without affecting the application's performance.

Why should you focus on auto-scaling of the storage resources?

According to a recent study by Virtana, 'The State of Hybrid Cloud Storage 2023,' 94% of IT leaders reported an increase in cloud storage, with 54% confirming that cloud storage costs are escalating faster than their overall cloud expenses.

We audited leading organizations to grasp the full impact of storage costs on comprehensive cloud bills. The findings revealed that these organizations allocated 40% of their total cloud expenditure to storage.

Hence, storage plays a significant role in impacting the cloud bill, and you need an auto scaling solution that focuses on computing and storage resources so that your cloud bill doesn't hurt your financial standing.

While there are several routes to scale storage, like EBS volume scaling and offering storage scaling solutions, you can only expand the storage resources, and there is no straightforward way to shrink the storage resources if the demands decrease. You need an autoscaling solution that expands and shrinks with the fluctuating demand. While the expansion will ensure you have room for increasing storage demand, what about the situation when the activity period is low?

We carried out an extensive audit covering more than 100 corporate clients to better understand the expenses related to storage in AWS, primarily in low-activity periods.

Our examination unveiled a noteworthy finding: 15% of the total cloud costs were associated with utilizing Elastic Block Store (EBS).

Additionally, the average disk utilization among these clients was 25%. Although there were instances of excessive provisioning, a recurring problem emerged in the form of quarterly downtimes caused by insufficient disk space.

• Over-utilized Volumes: Overutilized volumes refer to storage volumes, like Amazon EBS volumes, that are not utilized or cannot be accessed. Despite their lack of operational use, these volumes continue to consume resources and add to expenses.
  Maintaining idle volumes means incurring costs for storage resources that provide no value. Therefore, it is crucial to promptly identify and decommission such volumes to optimize costs, release resources, and reduce expenses associated with unused storage.
• Idle Volumes: Idle volumes refer to storage volumes that consistently operate at or near-maximum capacity, leading to overutilization. This can result in performance issues and increased costs, requiring additional resources or more efficient storage types to meet demand.
  During a recent storage audit for a client, we identified idle resources as a significant contributor to unnecessary expenses. This wastage may occur when storage is not attached to a virtual machine (VM) or when it is attached to a stopped VM.
  Recognizing and addressing idle volumes is essential for optimizing costs and improving resource efficiency.
• Overprovisioned Volumes: Overprovisioning happens when a storage volume is allocated with more capacity than necessary to fulfill workload requirements. This surplus provisioning often arises from inaccurate predictions of storage necessities or changing usage trends.
  Our storage review emphasized a crucial discovery - 95% of the waste identified was due to overprovisioned resources.

This signals the need for adequate shrinkage of storage resources. However, the shrinkage of storage resources is not directly supported by AWS. There is an indirect method. However, it entails implementing 3-4 tools and is excessively resource-intensive. This process is prone to errors and downtime. 

Look for an auto-scaling solution that offers both shrinkage and expansion- a comprehensive auto-scaling solution. 

While the above reasons necessitate the need for shrinkage of storage resources, several other reasons make it important. Shrinking storage resources addresses the following problems associated with overprovisioning.

• AWS charges are determined by the amount of resources used, so removing excess storage can help prevent unnecessary costs for unused capacity. 
• Over-provisioning leads to avoidable expenses. By reducing storage resources to match the actual requirements of your application, you can significantly decrease your AWS expenses. 
• Efficient utilization of resources is a fundamental principle in cloud computing. Decreasing storage resources ensures that only necessary components are utilized, optimizing the efficiency of your infrastructure. 
• Adequate storage resources prevent bottlenecks and latency issues arising from excessive capacity. 
• Managing and maintaining surplus storage can be challenging and time-consuming. Reducing storage resources simplifies operational tasks, facilitating your infrastructure's monitoring, scaling, and maintenance. 
• Streamlining resource management minimizes the likelihood of errors and enhances the overall stability of your system.

Shrinking storage resources is pivotal in achieving cost optimization, enhancing resource efficiency, optimizing performance, ensuring operational simplicity, and facilitating scalability.

Best Practices To Auto Scale Your AWS Storage

Now that we have established the significance of shrinking storage resources in AWS let us proceed with some of the best ways to auto-scale your AWS storage.

Use Correct EBS Volume

Selecting the suitable EBS volume type, depending on your application's performance and capacity demands, is an imperative measure to enhance the efficiency and cost-efficiency of your storage infrastructure.

This becomes particularly vital during auto-scaling implementation, as the storage layer must effortlessly expand alongside the compute resources to maintain a well-balanced and highly responsive system.

Understand Your Workload And Forecast Peaks

Perform a comprehensive examination of your workload, comprehending its trends, busiest periods, and usual usage scenarios. Such an analysis yields valuable information about the storage requirements throughout different stages.

Use predictive scaling models that are derived from historical data and workload patterns. Take advantage of tools such as Amazon CloudWatch metrics and machine learning algorithms to accurately predict storage needs.

However, predicting storage needs accurately can be a laborious and somewhat imprecise task, unlike other aspects of auto-scaling. Several challenges contribute to this complexity.

• Workload patterns can greatly influence storage requirements, and these patterns can vary over time. It can be challenging to anticipate how much storage will be needed for fluctuating workloads, especially those with unpredictable spikes.
• Capacity forecasting typically involves estimating future needs based on historical data. However, relying solely on historical patterns might result in underestimating the storage demand during peak periods. Organizations may be cautious and over-provision storage resources to mitigate the risk of downtime.
• Data growth does not always follow a linear or easily predictable trajectory. Unstructured data, such as user-generated content or log files, may experience sudden spikes, complicating the accurate forecasting of storage needs.

Setting Up Scaling Policies 

AWS auto scaling has a prominent loophole that can not be overlooked. While it offers resource expansion, there is no straightforward way to shrink the capacity.

While there is a method that involves creating a new smaller volume, the steps involved in the process, such as attaching, formatting, and mounting a new volume, lead to significant downtime.

However, if you want to expand AWS storage resources, there is a technique. Using AWS step functions and system manager, as mentioned below, you can expand capacity in the AWS.

• The automation process should be triggered once the capacity of the EBS volume reaches 80%. 
• Before resizing, a series of checks must be carried out to account for exceptions regarding specific systems. This includes custom-built applications that require manual disk expansion or instances governed by particular policies.
  It is essential to identify these excluded systems and instances with special requirements. Additionally, strict adherence to governance policies should be ensured, and manual intervention should be employed when necessary.
• As a precautionary step to prevent data corruption while resizing, capturing a snapshot of the EBS volume is advisable. This will entail creating an Amazon EBS snapshot to preserve the current state of the volume.
  AWS snapshot can be a reliable means to restore data if any unforeseen issues arise during the resizing process.
• To scale up the EBS volume on the AWS layer, use AWS Lambda. This serverless computing service can be utilized to automatically modify the size of the EBS volume according to the specified percentage. Trigger the Lambda functions through the monitoring system as soon as the capacity threshold is met.
• Verify the current status and ensure that the volume has been adequately expanded and the file system has been extended without any issues. Implement necessary checks to monitor the EBS volume status and the file system's expansion.
  Ensure that the expansion process has been completed successfully and that the capacity now meets the desired requirements.

Monitoring And Alerting

Monitoring the performance of your application and establishing suitable alerts are vital elements in guaranteeing uninterrupted operation, mainly when dealing with AWS EBS shrinkage.

You can promptly pinpoint performance issues and irregularities in real-time by monitoring. Early detection of problems, such as unexpected decreases in available storage or performance deterioration during EBS shrinkage, allows for proactive resolution before negatively impacting application uptime.

In addition, monitoring aids in cost control by identifying areas where resources can be optimized. Alerts regarding storage utilization and cost metrics support informed decision-making concerning the appropriate timing for shrinking EBS volumes and aligning resource usage with actual application requirements.

Furthermore, monitoring facilitates optimizing resource utilization, ensuring efficient resource usage. Efficient management of storage resources, including appropriate shrinking of EBS volumes when necessary, promotes cost savings and prevents unnecessary over-provisioning.

However, monitoring various storage resources across the environment can be resource-intensive, demanding significant time and effort from the DevOps team. The traditional monitoring process involves setting up different alerts across multiple touchpoints, which can be time-consuming and prone to errors.

The native tools like CloudWatch, which, by default, provides data is precise per minute, but there are restrictions when acquiring more detailed information. You would need additional monitoring tools or custom solutions to gain granular insight into every storage metric.

Furthermore, using third-party tools like Datadog means implementing and configuring a complex setup. The cost considerations can also become a concern, especially for smaller organizations or those with limited budget constraints. 

This is where Lucidity's Storage Audit comes to your rescue. The Lucidity Storage Audit is a user-friendly and fully automated tool allowing seamless storage environment monitoring.

By simply clicking a button, this accessible and comprehensive solution provides complete visibility into your storage system. With the Lucidity Storage Audit, you can effectively analyze your expenses, identify areas of inefficiency, and evaluate potential downtime risks.

It offers insight into the following parameters.

• Holistic Disk Expenditure Analysis: Obtain a comprehensive analysis of your spending patterns, gain valuable insights into optimizing your billing structure, and receive practical strategies to reduce disk spending by up to 70%.
• Identification of Disk Wastage: Identify and address the underlying causes of wastage through idle volumes or overprovisioning and benefit from personalized recommendations for eliminating inefficiencies.
• Mitigation of Disk Downtime Risk: Rely on our expertise to minimize the risks of disk downtime, protecting your business operations, reputation, and financial stability.

With Lucidity's Storage Audit offering you comprehensive cost-saving opportunities, you can rest assured you will have the insights required to allocate storage resources cost-efficiently.

Downfalls Of Existing Methods

When it comes to scaling storage resources, conventional approaches often lead to either excessive allocation of valuable resources or the creation of performance bottlenecks due to inadequate provisioning.

While we have covered the monitoring and alerting related issues with Auto Scaling in AWS, other equally concerning reasons make Auto Scaling through conventional methods less effective.

• Buffer Time: One of the biggest challenges we have encountered with the existing auto-scaling methods is that they require a significant buffer time between consequent scaling operations.
  In our study, we found out that a minimum of 6h gap is required. This can pose challenges, mainly when immediate adjustments are necessary to meet the shifting demand. In situations requiring prompt adaptability to varying workloads, the system's efficiency in responding promptly might be hindered due to this limitation.

• Performance drop: When scaling operations are performed, such as scaling out (adding resources) or scaling in (removing resources), performance impacts can be encountered during the transition.
  Instances may undergo a temporary decrease in performance as they are being launched or terminated. This could potentially impact the responsiveness of applications and the overall user experience.
• No live shrinking: The traditional auto-scaling methods do not support live shrinking. This lack of real-time capability to efficiently reduce resource allocation may present organizations with difficulties when it comes to maximizing cost savings and resource utilization during periods of reduced demand.
  Moreover, the time required to shrink 1 TB disk using the existing method leads to a downtime of 4 hours.

Auto Scaling Made Easy With Lucidity Auto Scaler

Understanding the challenges associated with the current Auto Scaling methods, we at Lucidity have designed an autonomous storage orchestration solution that creates the block storage that your system requires.

‍Lucidity Auto Scaler is the industry's first auto-scaling solution that offers automated shrinkage and expansion of storage resources based on changes in requirements.

With Lucidity Auto Scaler, you can easily manage your storage resources. This efficient tool seamlessly expands and shrinks your storage capacity without requiring any modifications in your code.

• Zero Downtime Assurance: Lucidity eliminates the risk of manual provisioning errors and minimizes downtime by automating capacity management. This ensures uninterrupted operations as your storage resources adapt to changing needs dynamically, enabling seamless scaling without downtime.
  Implementing the Lucidity Auto Scaler is quick and simple and maintains high performance, with the agent consuming only 2% of CPU or RAM usage.
  To enhance confidence in uninterrupted operations, Lucidity provides the ability to create personalized policies. These policies seamlessly coordinate instances according to your specific requirements, allowing you to set limits on resource usage, define minimum disk capacity, and optimize buffer sizes.
  With the freedom to establish unlimited policies, you can efficiently adjust storage resources to meet changing demands with precision.

• Effortless Expansion and Shrinkage: With Lucidity, you can effortlessly handle fluctuations in demand by automatically expanding and shrinking resources. Whether there is a surge in requirements or a period of low activity, Lucidity ensures you always have the optimal amount of storage resources available.
• Significant Cost Savings: Say goodbye to paying for idle or underutilized resources with Lucidity. Experience up to a remarkable 70% reduction in storage costs, thanks to the automated resource expansion and contraction provided by Lucidity.
  Achieve a comprehensive evaluation by utilizing our ROI calculator, which delves into crucial factors such as disk expenses, utilization metrics, and yearly progress. Enhance your company's overall profitability by obtaining a more precise understanding of possible cost reductions.

What if you want to go back to the original state of the disk?

Lucidity optimizes the resource deboarding, guaranteeing a streamlined and accurate decommissioning process. By employing automation, this method eliminates needless expenses and reduces resource wastage, enhancing efficiency and cost-effectiveness.

Efficiently Provision Resources With Auto Scaling

Employing best practices is paramount to optimize the advantages of AWS auto scaling. These practices are guiding principles, ensuring top-notch performance, cost-effectiveness, and resilience in dynamic cloud environments.

By meticulously adhering to these best practices, organizations can fine-tune their auto scaling configurations, balancing resource availability and cost efficiency.

Comprehending and aligning with these guidelines allows for smooth adaptation to fluctuating workloads, mitigating inadequate or excessive provisioning scenarios that may jeopardize application performance and financial efficacy.

Moreover, embracing these best practices facilitates efficient monitoring, alerting, and strategic decision-making, thereby contributing to the overall triumph of an AWS auto scaling implementation.

If you are looking for a quick and easy way to automate your Auto Scaling of storage resources, reach out to Lucidity for a demo of how we can help you manage your storage resources cost-efficiently.