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ITOA - Essential for Hybrid Cloud

Sasha Gilenson

Today organizations use hybrid cloud in a number of ways – a relatively static environment allowing the ability to handle occasional spikes in capacity demand. More advanced uses involve dynamic automated workload management ensuring optimal application performance.

In both cases, change is a main threat to performance of applications running on the hybrid cloud infrastructure – change in application code, application and cloud stack configuration, application data, workload etc. As you move from static to dynamic cloud, the number of changes grow as increases to the level of automation required to deliver these changes is continuous.

Historically, a lot of attention was paid to automation of deployments and more generally, continuous delivery – including rollback and recovery steps. However, when performance suffers the question becomes “to where do I rollback in order to recover performance and determine what change caused a performance issue?”

Today IT Operations Analytics technology detecting the latest state of the hybrid cloud environments and tracking history of the introduced changes is essential to creating visibility into an otherwise black box of the cloud. However, getting all the data is not enough as there will be too much data to manually process.

An analytics engine automatically prioritizing detected changes, correlating them with delivery context and performance indicators, and analyzing history of the environment is required to identify a safe environment state, find issue root cause and predict if any of the changes will cause a performance issue in the future.

Sasha Gilenson is the Founder and CEO of Evolven Software.

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ITOA - Essential for Hybrid Cloud

Sasha Gilenson

Today organizations use hybrid cloud in a number of ways – a relatively static environment allowing the ability to handle occasional spikes in capacity demand. More advanced uses involve dynamic automated workload management ensuring optimal application performance.

In both cases, change is a main threat to performance of applications running on the hybrid cloud infrastructure – change in application code, application and cloud stack configuration, application data, workload etc. As you move from static to dynamic cloud, the number of changes grow as increases to the level of automation required to deliver these changes is continuous.

Historically, a lot of attention was paid to automation of deployments and more generally, continuous delivery – including rollback and recovery steps. However, when performance suffers the question becomes “to where do I rollback in order to recover performance and determine what change caused a performance issue?”

Today IT Operations Analytics technology detecting the latest state of the hybrid cloud environments and tracking history of the introduced changes is essential to creating visibility into an otherwise black box of the cloud. However, getting all the data is not enough as there will be too much data to manually process.

An analytics engine automatically prioritizing detected changes, correlating them with delivery context and performance indicators, and analyzing history of the environment is required to identify a safe environment state, find issue root cause and predict if any of the changes will cause a performance issue in the future.

Sasha Gilenson is the Founder and CEO of Evolven Software.

Hot Topics

The Latest

Organizations that perform regular audits and assessments of AI system performance and compliance are over three times more likely to achieve high GenAI value than organizations that do not, according to a survey by Gartner ...

Kubernetes has become the backbone of cloud infrastructure, but it's also one of its biggest cost drivers. Recent research shows that 98% of senior IT leaders say Kubernetes now drives cloud spend, yet 91% still can't optimize it effectively. After years of adoption, most organizations have moved past discovery. They know container sprawl, idle resources and reactive scaling inflate costs. What they don't know is how to fix it ...

Artificial intelligence is no longer a future investment. It's already embedded in how we work — whether through copilots in productivity apps, real-time transcription tools in meetings, or machine learning models fueling analytics and personalization. But while enterprise adoption accelerates, there's one critical area many leaders have yet to examine: Can your network actually support AI at the speed your users expect? ...

The more technology businesses invest in, the more potential attack surfaces they have that can be exploited. Without the right continuity plans in place, the disruptions caused by these attacks can bring operations to a standstill and cause irreparable damage to an organization. It's essential to take the time now to ensure your business has the right tools, processes, and recovery initiatives in place to weather any type of IT disaster that comes up. Here are some effective strategies you can follow to achieve this ...

In today's fast-paced AI landscape, CIOs, IT leaders, and engineers are constantly challenged to manage increasingly complex and interconnected systems. The sheer scale and velocity of data generated by modern infrastructure can be overwhelming, making it difficult to maintain uptime, prevent outages, and create a seamless customer experience. This complexity is magnified by the industry's shift towards agentic AI ...

In MEAN TIME TO INSIGHT Episode 19, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA explains the cause of the AWS outage in October ... 

The explosion of generative AI and machine learning capabilities has fundamentally changed the conversation around cloud migration. It's no longer just about modernization or cost savings — it's about being able to compete in a market where AI is rapidly becoming table stakes. Companies that can't quickly spin up AI workloads, feed models with data at scale, or experiment with new capabilities are falling behind faster than ever before. But here's what I'm seeing: many organizations want to capitalize on AI, but they're stuck ...

On September 16, the world celebrated the 10th annual IT Pro Day, giving companies a chance to laud the professionals who serve as the backbone to almost every successful business across the globe. Despite the growing importance of their roles, many IT pros still work in the background and often go underappreciated ...

Artificial Intelligence (AI) is reshaping observability, and observability is becoming essential for AI. This is a two-way relationship that is increasingly relevant as enterprises scale generative AI ... This dual role makes AI and observability inseparable. In this blog, I cover more details of each side ...

Poor DEX directly costs global businesses an average of 470,000 hours per year, equivalent to around 226 full-time employees, according to a new report from Nexthink, Cracking the DEX Equation: The Annual Workplace Productivity Report. This indicates that digital friction is a vital and underreported element of the global productivity crisis ...