Storage is one of the most critical pillars in our application stack. Most of us are working with data, using data, and surrounded by data. We generate lots of data from the moment we wake up until the moment we fall asleep. This data is very critical, and we have to make sure that it is stored very securely and efficiently and that they are available at any time whenever we need it.

Managing this data storage is critical, and it's one of the major headaches for every SRE in the world. In this article, we will discuss cloud storage solutions, challenges you may face when you are running cloud storage services at scale, and how Sedai can autonomously help you optimize them.

Understanding Cloud Storage Solutions

Cloud storage means storing, accessing, and maintaining storage in the cloud. You don’t need to worry about the physical infrastructure in the cloud. Before the cloud era began, people were setting up their own data centres and maintaining the servers for storing the data. But now, in the cloud era, you can move all your data into the cloud.

You don't need to worry about operational parts. A cloud service provider manages everything. You don't need to worry about the capital expenditure as well. Everything will be taken care of by the cloud service provider. You just need to provision the services, use them, and pay for them.

If you look at the cloud storage landscape, almost all cloud service providers provide a variety of services for you. Let’s take an example of AWS. Here are the main storage services offered by  AWS:

• Amazon S3 is the best object storage service. It is scalable for storing large amounts of data.

• Amazon EBS is a leading block storage service in the cloud. It provides storage for Amazon EC2.

• Amazon EF is a fully managed file storage service. It offers file storage for AWS cloud services.

• Amazon FSx is also a file storage service for Windows and Lustre file systems. It provides fully managed file storage for Windows applications. 

As an administrator or SRE, you may face challenges when working with cloud services. These include:

• One of the challenges that you may face is selecting the right service for your workload.
• If you're using block storage, provisioning the right amount of storage volume for your workload or setting up the right IOPS for your workload will also be challenging. 

• The most critical and most challenging task is choosing the right metric based on which you are taking action on your storage services.

If you're running a small workload or taking care of a single tenant, it might be very easy for you. However, dealing with data on a large scale will be very difficult. 

Amazon S3 and Its Use Cases

Amazon S3 is one of the most popular object storage services in the cloud. It provides users with a variety of services. Here are some of these:

• It acts as a solution for local data storage needs.
• It manages large volumes of data generated by IoT devices.
• It serves as a strong back-end for big data processing and analytics.
• People use it for data storage, big data analytics, and content distribution.
• It is also used for local storage, analytics, data migrations, and applications.

S3 is one of the first storage services AWS announced. It is very useful if you are running a small workload. Most users start with one or two S3 buckets and eventually reach tens or hundreds of S3 buckets.

Taking into account an enterprise customer, they have thousands of S3 buckets and store terabytes or even petabytes of data in the cloud. If you are managing data at a large scale like this, these are some operational challenges that you may face. The most common ones are cost management and data lifecycle management.

AWS provides a couple of recommendations that users can optimize. These include:

• Use storage class wisely: Select the suitable storage class for cost and access needs.
• Lifecycle policies: Automate data transitions to save costs.
• Enable transfer acceleration: Speed up global file transfers.
• Monitoring and governance: Track usage and ensure compliance.
• Access control: Apply least-privilege access controls.
• Regularly review and optimize: Regularly optimize resources for efficiency.

When dealing with petabytes of data, an individual human or a group of people will not be able to handle it properly. Here, Sedai can help.

The Need for Autonomous Management

To deal with large amounts of data, Sedai, an autonomous platform can help you.

Data is growing faster than ever. Generative AI has already taken over the world, using a large amount of data to produce the results that the user wants.

Sedai has been working with many enterprise customers. Most of these customers are optimizing their modern application stacks.

Sedai is optimizing serverless and containerized applications. Customers re-visit them with the most satisfied responses.

Announcing Autonomous Storage Optimization

With the help of giant, noteworthy fintech partners with whom we have collaborated, we announce Sedai Autonomous Storage Optimization. If you are an existing customer of Sedai, you can configure Sedai Autonomous Optimization for storage in your dashboard. 

If you are a customer looking for a solution for storage optimization, Sedai is the right choice for you. Sign up for Sedai now.

How Sedai Optimizes Storage Solutions for Amazon S3

Now that we have discussed various cloud storage options and the challenges that we are facing in detail, let’s see how Sedai is autonomously optimizing storage solutions for Amazon S3. 

When a customer starts cloud storage, his initial requirements are fulfilled by a small number of data buckets. However, over the years of development, the requirements have grown considerably, resulting in tens of thousands of data buckets.

Imagine configuring all these buckets to their optimal configuration. It is almost impossible to configure these individually or manually. Not even an automated system can handle this scale to optimize all the buckets of data and configure all this properly.

This is where an autonomous system can benefit you. Instead of manually handling all these buckets, you can trust an autonomous system with this job. This system can configure all these buckets individually to fit their optimal configuration. 

Understanding Cloud Storage Classes

Storage classes are purpose-built to provide the lowest-cost storage for different access patterns. Amazon S3 (Simple Storage Service) was launched on March 14, 2006, and at that time, it was the only storage class available as standard storage. This storage class is irrespective of whether you use it for archiving or instant retrieval. The rate of storage remains constant. 

Over the years, AWS has identified different ways S3 is being used and developed these different storage classes.

Choose the right storage class when you configure an S3 bucket. Take an example of access frequency. If a user accesses the bucket a few times a month, the access pattern is frequent. So, the best fit for that would be the S3 Standard storage class. It is very ideal for applications such as website hosting or content distribution.

While choosing the right storage class for Amazon S3, you need to consider some factors:

• Data Retrieval Latency: How quickly do you need to access your data? If you need data almost instantly (in milliseconds), the S3 Standard storage class is ideal. If you can wait a few hours, you might consider options like S3 Glacier.

• Availability: Do you need your data to be available in several locations for redundancy purposes, or is it right to store it in just one Availability Zone? Classes like S3 Standard will ensure your data is strewn about multiple zones to ensure availability, whereas S3 One Zone-IA stores data in a single zone.

• Durability: How long do you need to keep your data? If you need longer-term storage, you might steer toward classes like S3 Glacier Deep Archive, which charges very low storage costs but retrieves with much higher costs and long wait times.

• Intelligent-Tiering: This is an extra feature that automatically shifts your data to any storage class, depending on the access patterns. Amazon monitors the access to your data and shifts it to a lower-cost storage class but monitoring service at an extra cost.

Now that we know different storage classes, let's see how this information can be used and fed into an autonomous system so that it can manage the configuring of all your data buckets on a large scale. 

Observing Access Patterns for Optimization

The first thing that the autonomous system would do is observe the access patterns of your bucket. This data can be sourced from CloudWatch metrics and storage lens metrics. This data can also be obtained from server access logs, which will give access information at an individual level.

This will help the user to identify what kind of access pattern does a certain bucket or a set of buckets has. 

Here, we can categorize access patterns into two main types:

• When access patterns are predictable, we can assign objects or buckets to the appropriate storage classes. 
• For buckets with unpredictable access patterns, it is advisable to use intelligent tiering. 

Below are some benefits of Intelligent Tiering:

• Automatically observes and tracks access patterns.
• Moves objects between storage classes based on real-time usage.
• Confirms cost-effectiveness without requiring manual intervention.

If additional monitoring costs are involved, they will be cheaper than keeping all your data in the standard storage class. The system has to monitor all the buckets continuously. It adapts to changing access patterns.

Cost Benefits of Autonomous Management

Below is an image from Sedai’s application that shows the cost benefits we can achieve by employing this autonomous management of S3 buckets.

The image shows that there is approximately 30% cost savings. This means that the Sedai application can bring in 30% cost savings by configuring the buckets autonomously.

On the right side of the image, you can see that the bucket has a standard storage class. What Sedai recommends will appear there, too. In this case, Sedai recommends moving this bucket into intelligent tiering.

If you look at the total number of objects and the number of frequently accessed objects, you will be able to observe that the percentage of objects that are frequently accessed is much less compared to the total number of objects. 

Another advantage is that we can also see the cost when it is in the standard storage class. This helps in summarising the net savings that we are making.

After the complete process of additional monitoring costs, it is still cheaper than keeping all the objects in the standard storage class. Once you enable autonomous mode for this bucket, Sedai will take care of the transition so that you can see the cost benefits.

Optimize Your Cloud Storage Costs with Sedai Autonomous System

We've discussed the difficulties of overseeing cloud-based data systems. We observe and monitor your data access patterns to determine the appropriate storage class. Sedai helps manage the data lifecycle in cloud storage effectively while not wasting storage space at any particular point in time.

The autonomous optimization solution from Sedai can save expenses and improve the operation of your data buckets. We help reduce costs by giving recommendations on efficient storage solutions and configurations. To find out how Sedai might help you, sign up for a demo with Sedai.