Introducing AIOps and GreenOps in Multi-Cloud

AIOps stands for Artificial Intelligence for Operations, but what does it really mean? AIOps is still a rather new concept but can help to optimize your multi-cloud platform. It analyzes the health and behavior of workloads end to end—that is, right from the application’s code all the way down to the underlying infrastructure. AIOps tooling will help in discovering issues, thereby providing advice for optimization. The best part is that good AIOps tools do this cross-platform since they operate from the perspective of the application and even the business chain.

This chapter is an introduction to the concept of AIOps. The components of AIOps will be discussed, including data analytics, automation, and Machine Learning (ML). After completing this chapter, you will have a good understanding of how AIOps can help in optimizing cloud environments and how enterprises can get started with implementing AIOps.

We will also...

Understanding the concept of AIOps

AIOps combines analytics of big data and ML to automatically investigate and remediate incidents that occur in the IT environment. AIOps systems learn how to correlate incidents across the various components in the environment by continuously analyzing all logging sources and the performance of assets within the entire IT landscape of an enterprise. They learn what the dependencies are inside and outside of IT systems.

Especially in the world of multi-cloud, where enterprises have systems in various clouds and still on-premises, gaining visibility over the full landscape is not easy. How would an engineer tell that the bad performance of a website that hosts its frontend in a specific cloud is caused by a bad query in a database that runs from a data lake in a different cloud?

AIOps requires highly sophisticated systems, comprising the following components:

• Data analytics: The system gathers data from various sources containing...

Optimizing cloud environments using AIOps

The two major benefits of AIOps are, first, the speed and accuracy in detecting anomalies and responding to them without human intervention. Second, AIOps can be used for capacity optimization. Most cloud providers offer some form of scale-out/-up mechanism driven by metrics, already available natively within the platform. AIOps can optimize this scaling since it knows what thresholds are required to do this, whereas the cloud provider requires engineers to define and hardcode it.

Since the system is learning, it can help in predicting when and what resources are needed. The following diagram shows the evaluation of operations, from descriptive to prescriptive. Most monitoring tools are descriptive, whereas AIOps is predictive:

Figure 19.1: Evolution of monitoring to AIOps

Monitoring simply registers what’s happening. With log analytics, companies can set a diagnosis of events and take remediation actions based on...

Exploring AIOps tools for multi-cloud

AIOps helps enterprises in becoming data-driven organizations. From the first chapter of this book, the message has been that IT—and IT architecture—is driven by business decisions. But business itself is driven by data: how fast does a market develop, where are the customers, what are the demands of these customers, and how can IT prepare for these demands? The agility to adapt to market changes is key in IT, and that’s exactly what cloud environments are for—that is, cloud systems can adapt quickly to changes. It becomes even faster when data drives the changes directly, without human interference. Data drives every decision.

That’s the promise of AIOps. An organization that adopts the principle of becoming a data-driven enterprise must have access to vast amounts of data from a lot of different sources, inside and outside IT. It needs to embrace automation. But above all, it needs to trust and rely on...

Introducing GreenOps

The concept of GreenOps is becoming increasingly relevant in today’s world, where concerns about climate change and sustainability are growing. What do we mean by GreenOps? Before we dive into that, it’s relevant to notice that GreenOps and AIOps are actually quite intensively related. Both concepts make use of AI, as we will learn.

In short, we can define GreenOps as the practice of using cloud technology to optimize the environmental sustainability of IT operations, helping organizations to reduce their carbon footprint and operate in a more environmentally friendly manner. The cloud offers a number of benefits when it comes to achieving sustainability goals. One of the most significant advantages of the cloud is that it allows organizations to use shared resources, which can reduce the overall energy consumption of IT operations. By using virtualized infrastructure and shared resources, organizations can improve the efficiency of their IT...

Summary

AIOps was a new kid on the block but, since 2020, it has emerged as an almost essential platform for managing complex cloud environments. AIOps systems help organizations in detecting changes and anomalies in their IT environments and already predicting what impact these events might have on other components within their environments. AIOps systems can even predict this from planned changes coming from DevOps systems such as CI/CD pipelines. To be able to do that, AIOps makes use of big data analysis: it has access to a lot of different data sources, inside and outside IT environments. This data is analyzed and fed into algorithms: this is where AI comes in, and ML. AIOps systems learn so that they can actually predict future events.

AIOps are complex systems that require vast investments from vendors, and thus from companies that want to start working with AIOps. However, most organizations want to become more and more data-driven, meaning that data is driving all decisions...

Questions

1. AIOps correlates data from a lot of different systems, including IT systems that are not directly in the delivery chain of an application but might be impacted by changes to that chain. What are these systems called in terms of AIOps definitions?
2. Name at least two vendors of cloud-agnostic AIOps systems, recognized as such by market analysts.
3. AIOps works in layers. Rate the following statement as true or false: most AIOps systems have separate solutions for the layers that are combined in an AIOps platform.
4. What is the aim of GreenOps?

Further reading

You can refer to a blog and video on AIOps at https://searchitoperations.techtarget.com/feature/Just-what-can-AI-in-IT-operations-accomplish.

Helpful blog posts about GreenOps can be found at https://www.computerweekly.com/opinion/IT-Sustainability-Think-Tank-Embedding-GreenOps-into-enterprises and https://blogs.gartner.com/lydia_leong/2023/03/13/greenops-for-sustainability-must-parallel-finops-for-cost/.