CLOUD OPERATIONS

Troubleshooting cloud services and infrastructure is an ongoing challenge for organizations of all sizes. Container services alone create a blizzard of logs. As organizations adopt more cloud services and their cloud environments grow more complex, they naturally produce more telemetry data—including application, system and security logs that document all types of events. All cloud services and infrastructure components generate their own, distinct logs.

Applying log analytics can help reduce some of the headaches associated with troubleshooting common cloud infrastructure and services issues. Uncovering these issues faster can help improve incident management KPIs, which include mean time to know (MTTK), mean time to repair (MTTR), and mean time between failure (MTBF), among others. Taking advantage of the efficient data retention of a centralized log analytics platform improves log coverage, which helps uncover lingering issues that observability platforms cannot.

Taking advantage of the efficient data retention of a centralized log analytics platform improves log coverage cannot.

Using a log analytics solution, DevOps teams can detect and resolve the following types of issues:

Cloud Security and Configuration Management

Maintaining consistent configuration management across all of your cloud infrastructure can be a major challenge. In fact, hackers often exploit common cloud misconfigurations, which include using default credentials or accidentally exposing credentials, exposed ports or poorly secured S3 buckets, and more.

Cloud Availability and Latency Issues

Sometimes issues can occur on the user’s side, and other times, server-side issues may cause latency and availability issues for public cloud services.

Cloud Application Performance Issues

Cloud applications can be delayed or fail for a number of reasons, including how they’re built and configured, poor database performance, or issues with the cloud computing services themselves.

Multi-Cloud Deployment Issues

Organizations embracing a multi-cloud approach need to learn to do the same things differently across cloud platforms. Learning a new system can be a costly and error-prone process.

4 CloudOps Use Cases for Log Analytics

In addition to troubleshooting, Cloud Operations (CloudOps) engineers can proactively leverage log analytics for a variety of use cases, such as:

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.

Overseeing Highly Automated Processes

When IT engineers or business managers subscribe to cloud services, 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. CloudOps engineers and teams must keep a close eye on what is happening to help compliance officers maintain governance standards.

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.

Understanding Utilization of Virtualized Hardware Resources

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. Even so, virtualized storage, compute, and network resources still need a lot of oversight.

4 CloudOps Use Cases for Log Analytics

In addition to troubleshooting, Cloud Operations (CloudOps) engineers can proactively leverage log analytics for a variety of use cases, such as:

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.

Overseeing Highly Automated Processes

When IT engineers or business managers subscribe to cloud services, 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. CloudOps engineers and teams must keep a close eye on what is happening to help compliance officers maintain governance standards.

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.

Understanding Utilization of Virtualized Hardware Resources

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. Even so, virtualized storage, compute, and network resources still need a lot of oversight.

Keep reading to learn about application troubleshooting and more