Cloud computing shapes the ability of enterprises to transform themselves and compete in the 2020s. By renting elastic cloud resources, enterprises can support new customer platforms, distributed workforces, and back-office operations.
The cross-functional discipline of CloudOps helps enterprises realize the promise of cloud computing by optimizing applications and infrastructure on cloud platforms. My recent blog for ChaosSearch defines the discipline of CloudOps and the supporting technique of log analytics. This clip from my recent webinar with Thomas Hazel summarizes what we mean by CloudOps:
In this blog, we’ll define use cases in which log analytics assists CloudOps. Let’s start by defining use cases in which log analytics helps achieve two specific CloudOps goals: stability and agility.
Use case 1: Improving CloudOps stability
The CloudOps engineer monitors log trends to identify an application issue, then parses and correlates logs from that application as well as its container, compute, and storage resources. This helps manage performance and meet SLAs. They also use log analytics to track user actions with sensitive data to improve compliance.
Use case 2: Increasing CloudOps agility
The CloudOps engineer learns from their container logs that their latest application version consumes more compute cycles for certain workloads than the last version. They also might learn from their compute cluster logs that performance becomes erratic during bursts of user logins. These insights help them rapidly build and release a better application version, with the right allocation of compute resources.
CloudOps and log analytics also help manage two distinct aspects of cloud environments: highly automated processes, and virtualized hardware that you never touch. Here are definitions of these distinct characteristics, and the resulting additional use cases for log analytics.
Use case 3: Overseeing highly automated processes.
When IT engineers or business managers subscribe to cloud services such as a software as a service (SaaS) application, development workspace, or compute cluster, they kick off automated processes. AWS, Azure, and Google Cloud provision those services by activating workflows that execute tasks across components of the cloud environment: compute, storage, containers, etc. Cloud providers also automate processes to remediate issues that arise, helping enterprises meet performance and availability SLAs. In these ways, automated cloud processes make things more stable and more agile. However, automated cloud processes can hurt stability and agility in other ways. CloudOps engineers and teams must keep a close eye on what is happening to help compliance officers maintain governance standards.
- Use cases: CloudOps engineers must audit how containers and applications authenticate users, and how those users handle Personally Identifiable Information (PII). CloudOps engineers also need to understand how automated cloud processes and on-premises processes impact one another. For example, you might use a SaaS CRM application that integrates with a legacy on-premises customer support application. If the on-prem application stops updating records, are users of the SaaS application automatically notified? Sales reps need to know if their top customer just complained to the support desk. Log analytics helps CloudOps teams understand and control these factors.
Use case 4: Understanding utilization of virtualized hardware resources.
Who needs a wrench? Rather than taking weeks to install cabinets and plugging cables into ports, CloudOps teams spin up virtualized cloud resources through their cloud provider’s portal in a matter of hours. This makes enterprise IT more stable because cloud providers assume the responsibility and liability of this cumbersome physical work. It makes enterprises more agile by helping them scale rapidly. However, virtualized storage, compute, and network resources still need a lot of oversight.
- Use cases: CloudOps teams must study how users, applications, and containers utilize those resources to support chargeback and prevent cost overruns. They also must study resource utilization to optimize the performance of applications they develop and release onto a cloud platform. Log analytics helps CloudOps teams understand and control these factors as well. The diagram below illustrates the role of log analytics in on-premises and cloud environments.
For additional color on these points, check out this clip from our webinar:
Cloud computing simplifies some aspects of IT, but makes others more complicated. You need to manage your applications on new infrastructure, govern your data, and control variable costs. Log analytics enables you to meet these requirements and thereby create a stable, agile environment in which your business can thrive.
To learn more:
- Read the blog — How Log Analytics Powers Cloud Operations: Three Best Practices for CloudOps Engineers
- Read the whitepaper — Log Analytics for CloudOps: Making Cloud Operations Stable and Agile
- Watch the webinar — Why and How Log Analytics Makes Cloud Operations Smarter