The Transformation of Performance Monitoring with AIOps

Introduction

IT professionals today are tasked with ensuring the performance and reliability of all their IT systems across various work environments. Modern applications have brought an overflow of data to the IT work environments along with speed and flexibility. AIOps approach is thus being applied globally across industries.

What is AIOps?

AIOps is an abbreviation for Artificial Intelligence for IT Operations. The term was first coined by Gartner. AIOps utilizes big data, machine learning, and automation technologies to support activities including service desks, automation processes, and data center monitoring.

AIOps platform merges functions of Big Data and Machine Learning (ML) to enhance and partially replace primary IT operations like performance monitoring, IT service management, automation, and event correlation and analysis. It automatically addresses issues in real-time while providing traditional analysis.

AIOps brings together the three different IT disciplines:

• Service Management (Engage)
• Performance Management (Observe)
• Automation (Act)

Gartner predicts that the exclusive use of AIOps in large enterprises will rise to 30% in 2023.

Some of the major Elements of AIOps are:

• Big Data: This includes modern big data platforms like Elastic Stack, Hadoop 2.0, or Apache technologies. They allow real-time processing.
• Artificial Intelligence (AI): AI is trained to adapt to the unfamiliarity in an environment.
• Machine Learning (ML): ML automates pre-existing, manual analytics, and authorizes new or altered analytics. It is based on the output of new data. The scale and speed are thanks to AIOps.

AIOps Tools Vendors:

Since AIOps can be integrated with different information sources, the area of AIOps tools vendors is extensive. These tools can be split into two types and it depends on how they collect information. These are:

• Domain Agnostic: Tools that rely heavily on integrations with different services to collect data.
• Domain Centric: Tools that collect most, or all required information themselves. Domain centric tools are inclined towards special domains. Example: log management and Application Performance Monitoring (APM).

Domain Agnostic Tools

• BigPanda

BigPanda is a US software company founded in 2012. It collects data from distinct services to feed its ML algorithms with data and correlation, and problem identification. BigPanda offers a free version and a free trial. It includes features such as dashboard, assignment management, escalation management, issue auditing, knowledge-based project management, and so on. 

• Splunk/Victorops

Splunk and Victorops can be used either independently or together. Splunk falls under the Domain Centric World as it is designed for large amounts of log data. However, in unification with Victorops, it’s only one of the many integrations to source data from. Splunk makes it easier to collect, analyze the data, and take action upon the unused value of big data. Victorops provides an analytical engine to correlate the gathered data from multiple services.

• Moogsoft

Moogsoft integrates with external services to collect the necessary information. It provides noise reduction, detects incidents prior, and fixes problems with speed and efficiency.

Domain Centric Tools

• AppDynamics

AppDynamics is based in the USA, is a software company founded in 2008. It offers a software known as “AppDynamics” that provides its data collectors. The data retrieved from external systems are integrated into correlation algorithms. It includes features such as diagnostic tools, performance control, and resource management.

• New Relic

New Relic, a software company founded in 2008 in the US offers the software titled “New Relic”. Its AI capability supports features devoted to AIOps. Information is collected by the New Relic One platform through self-deployed agents. It stores, correlates and creates cases. New Relic’s integrations with external tools are vast.

• Zenoss

Zenoss Service Dynamics is named after a software organization by the name Zenoss founded in 2005. This software complements SaaS, Mac, Windows, iPhone, and iPad. For AIOps, it is combined with log and incident management and intelligence and automation. “ZenPacks” can integrate into all kinds of systems. Data is collected through these ZenPacks. The app offers a free version and 24/7 live support.

Introduction to Performance Monitoring

Performance Monitoring is the act of verifying and tracking how steadily the digital platforms are performing. It is a set of processes and tools able to determine how fast applications are running.

Application Performance Monitoring (APM) measures how well cloud applications, infrastructure, and networks perform. APM helps in finding out where the bottleneck lies. Operations teams monitor APM tools to make sure all systems are in good condition.

AIOps and Digital Transformation

Monitoring and managing the performance of applications is vital. Being able to spot anomalies quickly, addressing problems, and optimizing systems can prove to be productive. It might lead to idle time if systems do not perform at their full potential. But as IT businesses grow, data expands rampantly. It gets harder for IT administrators to monitor performances manually.

AIOps can automate and transform application performance. AI can reduce delays caused by technical issues. AI and Machine Learning effectively deal with huge quantities of information.

Based on ML, algorithms analyze different data sets to find connections between services and infrastructure. Understanding relationships is critical for the identification of root causes and remedial actions when necessary.

The three main things that can be done with AIOps are achievable by using application monitoring techniques like:

• Forecasting Future Issues

AIOps boosts predictive analytics activities. It closely studies past and present behavior and deduces most likely future scenarios. Businesses can proactively adjust their strategies to their advantage. It can foresee and mark the irregularities of upcoming business risks or failures.

• Spotting Hidden Relationships

With AIOps you can compare performance metrics across numerous systems to identify the impact of IT applications on overall performance and customer satisfaction. AIOps algorithms can spot patterns in combined business and IT data. You can understand the relationship and build up a chain that identifies what activities affect a particular business.

• Making most out of Customer and Transaction data

AIOps’ Machine Learning capabilities assist in pattern recognition, finding variances, and classification. They’re key elements of big data. It can help understand how user behavior impacts the wider IT system.

Benefits of AIOps

• Fast Data Processing: AIOps can enable real-time data correlation. Raw data can be consumed by smart algorithms generated by big data and ML. It helps in the creation of new targets for key metrics.

• Data-driven Decision Making: AIOps brings ML techniques that can learn from data without rule-based programming to ITOps. This helps in decision making by enabling data-driven automated responses. Such responses eliminate human error.

• Proactiveness in IT work: in a business environment, the success of any business depends on customer satisfaction. Hence, it is essential to predict possible issues and delays. AIOps aids ITOps to predict and provide solutions for performance issues across all applications, services, and infrastructure before their materialization.

• Improved Root Cause Analysis: Machine Learning and analytics enable the systems to execute root cause analysis. This increases its ability to troubleshoot and rectify unusual issues, resulting in improved data analytics.
• Reduced burden on staff: AIOps reduces time and improves problem identification. The increased automation tends to minimize time given by staff on mundane, routine tasks every day. They can focus on complex issues and initiate processes that increase business stability and performance.

Conclusion

As the volume of data increases across all the digital platforms, it becomes challenging to manage it manually. While automation has helped in operational planning, today’s complex environment demands more. By applying AI to ITOps problems can be easier to predict and prevent. It accelerates diagnosis and analysis minimizing disruption to customers.

Application Performance Monitoring solutions are essential in taking control by providing real-time insights needed to take action. Monitoring tools provide visibility into networks, clouds, and servers. 

Hence, the long-term impact of AIOps on performance monitoring and IT as a whole will be transformative. Therefore, companies must make a wise choice before the implementation of AIOps.