AI data storage systems can recognize patterns in arrays and stacks to predict storage issues and help solve them.
With many sources of big data and an increasing volume of available data for enterprises, storage capacity planning has become an issue for storage administrators. According to an estimate, 2.5 quintillion bytes of data are generated every day. Now that’s a huge amount of data equal to 250 million human brains if counted in neurons. And, the same estimate suggests that 90% of the total world data was generated from 2016 to 2018.
It can be simply put that more and more data is generated every day, and with that is increasing the scale and complexity of storage workloads. However, AI can come to the rescue of storage administrators, helping them to store and manage data efficiently. By using AI data storage, vendors and businesses can take storage management to the next level. And, storage administrators can find a solution to the metrics they are currently struggling to manage.
Major Metrics that Storage Administrators Struggle With
Storage administrators face some challenges while managing storage issues. And, if they overcome these challenges, it would help them to find a proper balance among various aspects of data storage like where to distribute workload, how to distribute it, and how to optimize a stack. Throughput
Throughput, in general terms, means the rate at which something is processed. At the network level, the measurement unit of throughput is Mbps (megabits per second), whereas at the storage level, the measurement unit of it is MB/sec (megabytes per second). Since one byte is equivalent to eight bits, the production rate increases at the storage level. And, it becomes difficult to manage the increased production rate. Latency
Latency is the time taken by servers to fulfill a request. With reference to the storage, it means the time taken to fulfill a request for a single storage block. Storage block or block storage is a block where data is stored in volumes. Pure latency does not get affected by throughput, but application latency might get deviated with increased throughput if single block requests are large.IOPS (Input/Output Operations Per Second)
IOPS refers to the count of discrete read and write tasks that a storage stack can handle per second. A storage stack is a data structure that allows procedure invocation. That means multiple procedures are stored over one another in a stack, and then all of them are executed one by one on a call and return basis. For instance, if one procedure is called, it gets executed, and then it returns so that the next procedure gets called in a stack. And, while talking about IOPS, a storage system’s stack limits can be reached by underlying input/output tasks. For instance, reading a single large file and multiple tiny files can have an impact on IOPS. Since reading a single large file will need to execute only one read task, it can be executed at a high speed, on the other hand reading multiple files will be very slow as many reading tasks need to be executed.
How AI Data Storage Can Solve Storage Issues
Enterprise administrators and storage vendors deal with a wide variety of storage types. And, they also meet metrics of different input/output services. A large file sharing application may need decent throughput, but also must condone latency penalties as large and complex applications can have an adverse impact on latency. On the other hand, an e-mail server might require massive storage, low latency, and good throughput, but it might not require a very demanding IOPS profile. And, storage administrators are supposed to decide which storage should be given what resources. Hence, with thousands of services running in an organization, the management of underlying storage outpaces human abilities to make informed changes. And, this is where AI algorithms come in handy.
AI-Driven Storage Management and Planning
AI can monitor storage to detect patterns and performance of several workloads. Here workloads are data streams generated by various input/output characteristics or tasks of an application. By detecting these patterns of workloads, AI can help storage administrators gain insight into which workloads can put them at risk of maxing out their storage arrays. Further, storage monitoring can also help to know whether any extra workload can fit into an array or not. And also, if added to an array, then how much disruption will a workload cause. For instance, let’s say a business is adding an email server to a process. In this case, AI systems can help predict whether the storage array will be able to fulfill the storage needs of that server or will max out. With the help of such techniques, storage administrators can proactively get information about how to allocate different workloads to different storage stacks and minimize latency. Thus integration of AI into storage arrays, storage vendors, and organizations can optimize the storage stack.
In addition to monitoring the storage activity, storage administrators also need to examine and analyze the coding and bugs of the applications that the storage system is going to service. This helps them to understand better how to design storage architecture around the needs of the application. They do this by understanding the input/output pattern of an application. The most common technique that is used to do this is by capturing the strace of the application. Strace is a user space utility for Linux that can be used to diagnose, debug, and get instruction about input and output functions. But, this can be challenging for humans as a complex application can have several input/output functions. ML algorithms, on the other hand, can easily ingest and analyze a huge volume of data and solve many storage problems that are best solved by looking outside the storage system itself. Also, by training algorithms with a huge amount of data about how a particular stack or the application as a whole gathers data and store it, they can help achieve real-time observation on storage activities of that particular application to prevent maximizing stacks and improve storage capacity.
AI Data Storage for Customer Satisfaction
Telemetry data is the automatic recording and wireless transmission of data from remote or inaccessible sources. Telemetry functions in the following way: sensors at source measure data, they convert it to electrical voltages, which are then combined with timing data into a single data stream that is transmitted to a remote receiver. After receiving, the data can be processed according to user specifications. AI’s computer vision technology can scan telemetry data to protect storage arrays from vulnerabilities. When trained with historical data on vulnerabilities, ML algorithms can match incoming data from various applications with historical data to find possibilities of vulnerabilities. Thus, with AI’s predictive analytics, storage vendors can aim to prevent storage issues before they can hit customers.
AI data storage is still at its infancy but it has already shown some amazing results. And, hence cloud vendors and other storage administrators are investing more and more in AI to use hyper-converged storage systems for storage maintenance. Mainstream AI data storage adoption will surely help businesses to control all the metrics discussed above and provide better services to their customers.
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