Centralized Log Management for Cloud Streamlines Root Cause Analysis

Cloud services make the daily tasks of business easier. They enable remote workforce collaboration, streamline administrative tasks, and reduce capital costs. However, these “pros” come with a few “cons.” The IT stack’s increased complexity means staff work across divergent log management tools when something breaks. Centralized log management for the cloud makes root cause analysis easier by aggregating all event log data in a single location. 

Why is root cause analysis difficult in cloud-based IT stacks?

Root cause analysis is the process of detecting, finding, and identifying unexpected problems in your IT stack so that you can fix them. Generally, you look in code signals for anomalies as part of your investigation into errors and exceptions. In complex, interconnected cloud IT stacks, this process is increasingly difficult. 

Creating a Strategy

According to one source, the average enterprise uses 1,295 cloud services. At any given time, several of these services could have a problem. Every action that an application takes generates logs. 

The sheer amount of information makes it challenging to create a strategy. Additionally, applications often connect to one another, so creating a focused, structured process becomes complicated. 

Timing of the Problem

Your company created a digital transformation strategy to benefit from the cloud’s scalability and flexibility. However, these benefits are a problem when you’re trying to complete a root cause analysis. Everything moves faster in the cloud. 

For example, serverless applications pose a problem because they execute quickly, making it challenging to log their success or failure times. Adding additional code to serverless functions to send logs through an API can lead to security or performance issues. 

Locating the Error

In cloud deployments, the sheer amount of code creates problems when looking for root causes. They often consist of diverse sets of microservices, machines, and servers. Your problem can come from a new feature, an updated line of code, or legacy code that needs updating.

For example, microservices architectures (MSAs) can be resilient and robust. However, they also make it difficult to detect problems and locate root causes. Their complex dependencies mean that when one component fails, it impacts everything that relies on it. This increases the number of log events, making it difficult to find actionable insights. 

Additionally, many companies use MSAs so that their developers can use different programming languages. Unfortunately, the log file formats can be different for each language. This makes it hard to aggregate logs in a meaningful way. 

Defining the Problem

Platform-as-a-Service (PaaS) models also make root cause analysis more difficult. A PaaS deployment includes development tools, database management, operating system, servers, storage, and firewalls. Any one of these can be the root cause of the outage or service problem. 

As a layered model, PaaS makes it hard to tell the difference between an application or infrastructure code problem. Consider a “file not found” error. The problem’s root cause could be that the file doesn’t exist or that the filename is not readable. If you can’t define the problem up front, your research becomes that much more difficult. 

What logs can help with root cause analysis?

Log collection is one of the most critical parts of root cause analysis. It’s a hard balance trying to collect the right logs to gain insight into the point or points of failure. 

Application logs

If you’re trying to find a problem with a specific application, the application log is a good starting point. If you’re the developer, then you get to control the information that these logs contain. Some critical information includes:

  • Context: gives background information about the application’s state 
  • Timestamps: tells the date and time an event occurred
  • Log level: tells you whether the event is informational, warning, or error 

Business logic logs

Crash logs

Crash logs can help developers working on mobile applications or looking for problems with an operating system. Two helpful crash logs are:

  • Application Not Responding (ANR): tells you when an application is “frozen” 
  • Exception Stack trace: gives visibility into what the application was doing when the problem occurred

Device logs

With these logs, you can get information about what a device was doing when the error occurred. Some event logs that help you during root cause analysis include:

  • Connect to server
  • Disconnect to server
  • Receive a configuration update
  • Command sent to device from server

Application load balancer logs

Too much network traffic can slow down or freeze up an application. If your development environment is in Amazon Web Services (AWS), you can use their application load balancer logs to get information. 

These logs contain some of the following information:

  • Request processing time
  • Target processing time
  • Target status
  • Trace ID

Server logs

Although server logs help detect security issues, they also give valuable insight into the root cause of a problem. Some of the most common events that these logs track include:

  • Unexpected reboot: tells you if a system shut down unexpectedly 
  • Application hang: helps you determine if the application is stuck in a loop or trying to reach an unavailable resource
  • Application fault: tells you whether the application code has a big 

System logs

Operating systems generate log data that can help identify issues with software, hardware, system components, and system processes. System logs give valuable information by classifying the events by type, including:

  • Error
  • Informational
  • Warning
  • Emergency
  • Alert
  • Critical 
  • Debug

How Centralized Log Management (CLM) makes root cause analysis easier

In theory, the more data you collect the faster you should be able to find the problem. However, if you have a complex cloud environment, you should consider a centralized log management solution.

Data normalization

CLMs help you aggregate log data and can create a standardized log format. Often, it’s hard to find the answers because each application, operating system, and device uses different formatting. For example:

  • Syslog defines dates as Month Abbreviation Date Year (Feb 20 2021) 
  • kernel.log defines as Day-Month Number-Year (20-02-2021)

A centralized log management solution automates the data normalization process. This means that you can find answers faster and compare data points better.

Log Filtering

Not only do logs come in different formats, but they also come in different types. Your log analyzer solution should give you a way to filter event logs by type. If you can filter by types such as error, critical, or debug, you can find the problem’s root cause faster.

Data correlation 

Through data normalization and log filtering, you can correlate event data better. Both of these features make it easier to compare data points to each other. Instead of doing it manually, the log management system does that work for you.

Graylog: Streamlining your root cause analysis process

Graylog’s centralized log management solution helps you find the answers you need. The Graylog Extended Log Format (GELF) standardizes log formats for better data correlation. Our solution also enables multi-threaded and distributed searches. Our solution enables rapid results with multiple processors and buffers.

Your team can also build workflows to save searches. This way, if you’re worried about downtime impacting mission-critical applications, they don’t need to start from scratch. This lets you keep your IT operation lean while also empowering the team.

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