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Using Machine Learning Analytics to Help Meet SLAs

Jerry Melnick

The first post in this two-part series introduced machine learning analytics as a new way to find and fix the root cause of performance problems to help meet SLAs. This post explains three ways MLA can be used to better utilize resources for optimal performance.

The first way MLA helps make certain needed performance is delivered while optimally use resources is by providing the accurate information needed for IT to tune VM configurations settings. IT managers today have poor insight into the causes of poor application performance. To be extra careful, they often throw a lot of hardware at the problem in an attempt to avoid the possibility of starving the applications.

In many cases applications can be over provisioned by as much as 80 percent. Under provisioning VMs is less common but equally problematic and can lead to very poor performance. Traditional processes for right-sizing VMs, is time-consuming, error-prone and inaccurate. IT administrators need the skill, time, and tools to run multiple reports, and then manually assemble their findings to approximate the right settings.

In contrast, MLA continuously and automatically observes resource utilization patterns using real-time data from the environment to identify over- and undersized VMs and then recommends precise configuration settings to right-size the VM for performance. And if usage changes, MLA will dynamically update recommendations.

The second way MLA helps improves utilization and save money is by finding unused or wasted resources. Among the many advantages of virtualization is the ease with which VMs can be set up and torn down and how storage can be dynamically allocated. But when unused VM’s or storage snapshots are left to languish, they waste precious resources. And these situations can be extremely difficult to identify given some of these may be seemingly unused when in fact they are being used! Removing these in error could be disastrous, so IT leaves them there.

MLA solves this by observing patterns of behavior over time over multiple dimensions to identify which VM’s are truly inactive and which storage snapshots are safe to be freed up. It then recommends precisely how to recover the waste. Once again eliminating the guess work.

Some MLA systems also provide a complete summary of savings that could be achieved by removing wasted resources and right sizing VM’s. They provide comprehensive reports that include not only the saving in hardware resources, but also the savings in software licensing that can be achieved by reducing the number of hosts and VMs.

The third way machine learning analytics helps optimize resource allocations for peak performance is by identifying those applications that would benefit the most from storage acceleration through the use of all-flash arrays or host-based caching (HBC). Storage acceleration delivers substantial improvements in throughput performance by increasing I/O operations per second (IOPS). But to be successful, IT managers need to verify that a) the root cause of their performance issue is related to storage performance and b) that they have chosen the right VMs and configured the storage acceleration optimally. Today, most use a trial-and-error approach and best guess usually using simple single dimension measurements from storage tools.

Machine learning is ideal for delivering the right information to make the decisions regarding which VMs need acceleration and how best configure them. Some MLA systems are also able to perform a simulation to estimate the likely increase in IOPS, which enables the IT department to prioritize the implementation effort.

Machine learning analytics brings machine derived intelligence to task of optimally configuring the infrastructure taking the guesswork out of many aspects involved in meeting SLAs more efficiently and cost-effectively. And with the technology advancing rapidly, its future holds tremendous potential for many new and even more powerful capabilities.

Jerry Melnick is President and CEO of SIOS Technology.

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Using Machine Learning Analytics to Help Meet SLAs

Jerry Melnick

The first post in this two-part series introduced machine learning analytics as a new way to find and fix the root cause of performance problems to help meet SLAs. This post explains three ways MLA can be used to better utilize resources for optimal performance.

The first way MLA helps make certain needed performance is delivered while optimally use resources is by providing the accurate information needed for IT to tune VM configurations settings. IT managers today have poor insight into the causes of poor application performance. To be extra careful, they often throw a lot of hardware at the problem in an attempt to avoid the possibility of starving the applications.

In many cases applications can be over provisioned by as much as 80 percent. Under provisioning VMs is less common but equally problematic and can lead to very poor performance. Traditional processes for right-sizing VMs, is time-consuming, error-prone and inaccurate. IT administrators need the skill, time, and tools to run multiple reports, and then manually assemble their findings to approximate the right settings.

In contrast, MLA continuously and automatically observes resource utilization patterns using real-time data from the environment to identify over- and undersized VMs and then recommends precise configuration settings to right-size the VM for performance. And if usage changes, MLA will dynamically update recommendations.

The second way MLA helps improves utilization and save money is by finding unused or wasted resources. Among the many advantages of virtualization is the ease with which VMs can be set up and torn down and how storage can be dynamically allocated. But when unused VM’s or storage snapshots are left to languish, they waste precious resources. And these situations can be extremely difficult to identify given some of these may be seemingly unused when in fact they are being used! Removing these in error could be disastrous, so IT leaves them there.

MLA solves this by observing patterns of behavior over time over multiple dimensions to identify which VM’s are truly inactive and which storage snapshots are safe to be freed up. It then recommends precisely how to recover the waste. Once again eliminating the guess work.

Some MLA systems also provide a complete summary of savings that could be achieved by removing wasted resources and right sizing VM’s. They provide comprehensive reports that include not only the saving in hardware resources, but also the savings in software licensing that can be achieved by reducing the number of hosts and VMs.

The third way machine learning analytics helps optimize resource allocations for peak performance is by identifying those applications that would benefit the most from storage acceleration through the use of all-flash arrays or host-based caching (HBC). Storage acceleration delivers substantial improvements in throughput performance by increasing I/O operations per second (IOPS). But to be successful, IT managers need to verify that a) the root cause of their performance issue is related to storage performance and b) that they have chosen the right VMs and configured the storage acceleration optimally. Today, most use a trial-and-error approach and best guess usually using simple single dimension measurements from storage tools.

Machine learning is ideal for delivering the right information to make the decisions regarding which VMs need acceleration and how best configure them. Some MLA systems are also able to perform a simulation to estimate the likely increase in IOPS, which enables the IT department to prioritize the implementation effort.

Machine learning analytics brings machine derived intelligence to task of optimally configuring the infrastructure taking the guesswork out of many aspects involved in meeting SLAs more efficiently and cost-effectively. And with the technology advancing rapidly, its future holds tremendous potential for many new and even more powerful capabilities.

Jerry Melnick is President and CEO of SIOS Technology.

Hot Topics

The Latest

As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...

A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...

IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...

A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...

According to Gartner, Inc. the following six trends will shape the future of cloud over the next four years, ultimately resulting in new ways of working that are digital in nature and transformative in impact ...

2020 was the equivalent of a wedding with a top-shelf open bar. As businesses scrambled to adjust to remote work, digital transformation accelerated at breakneck speed. New software categories emerged overnight. Tech stacks ballooned with all sorts of SaaS apps solving ALL the problems — often with little oversight or long-term integration planning, and yes frequently a lot of duplicated functionality ... But now the music's faded. The lights are on. Everyone from the CIO to the CFO is checking the bill. Welcome to the Great SaaS Hangover ...

Regardless of OpenShift being a scalable and flexible software, it can be a pain to monitor since complete visibility into the underlying operations is not guaranteed ... To effectively monitor an OpenShift environment, IT administrators should focus on these five key elements and their associated metrics ...