Leveraging AIOps for Observability Workflows: How to Improve the Scalability and Intelligence of Observability

Editor's Note: The following is an article written for and published in DZone's 2024 Trend Report, Observability and Performance: The Precipice of Building Highly Performant Software Systems.

AIOps play a crucial role in streamlining the operational load, improving overall performance, and enhancing the security of highly distributed and complex applications. By introducing the AI and ML capabilities of AIOps into observability workflows, manual effort is saved via automation of incident detection, root cause analysis, and self-healing capabilities. As the complexity of a system grows and the volume of data increases, the efficacy of an AIOps integration improves. In this article, we will analyze AIOps capabilities to modernize and optimize observability workflows.

A Brief Review of Observability Workflows and AIOps

This section will discuss the key components of observability workflows and AIOps with examples. AIOps can be used in existing observability workflows to make them smarter.

Figure 1. AIOps in observability workflows

Key Components of Observability Workflows

Observability workflows provide a deep understanding of and visibility into a complex distributed system. This enables software teams to proactively detect issues, enhance security, optimize application performance, and scale the system.

Table 1. Observability workflow components

Observability workflows enable a scattered team working on complex and highly distributed systems to take necessary action by employing the above methodologies to ensure the high availability of these systems. A few advantages include:

• Real-time communication monitoring. Observability workflows enable software teams to collect data from various distributed systems in real time to gain insight into the application.
• Microservices monitoring. Observability workflows enable the monitoring of complex distributed systems by ingesting data from these systems and creating a report on the collected data.
• Automated incident management. Observability workflows enable teams to offload the manual workload of identifying and resolving any software incident that could impact customers.

Key Components of AIOps

The key components of AIOps overlap with observability workflows. Apart from the key components of observability workflows listed in Table 1, below are the key components of an AIOps system:

• AIOps can use natural language processing to gain a better insight into the system by using ML techniques for anomaly detection, predictive analysis, and behavior learning.
• Using AI and ML techniques, AIOps has a better healing capability than traditional observability workflows.
• AI and ML provide a better self-learning ability for ensuring security and compliance, and it also helps in preemptive detection and incident response.
• With self-learning capabilities analyzing a vast amount of data, historical patterns, and predictive capabilities, AIOps work with higher accuracy.

There are various tools (e.g., ElasticSearch, LogStash, Kibana, Kedro) and MLOps practices that can work together to create an AIOps observability workflow. These tools play a crucial role in various segments like collection, processing, storage, AI/ML, incident management, and reporting. The AIOps framework can be created using these various tools together, and once a continuous pipeline is set up, it can be used in observability workflows. These pipelines will expose interaction points of the workflows. 

AIOps for Observability Workflows

The core of both AIOps and observability workflows is data from various sources, ingestion, storage, monitoring, mitigation, and continuous learning. AIOps will consume the huge data produced by various microservices, process it, and use it to improve the AI/ML system continuously. This, when combined with observability workflows, enhances the overall performance of the system.

There are various overlapping components between these two systems like data collection, data processing, data ingestion, data storage, data visualization, and continuous learning. It is easier to extend observability workflows to utilize AIOps for improved functionality.

Key Components of AIOps for Observability Workflows

The AIOps components described in Figure 1 provide AI/ML intelligence capabilities to existing observability workflows by using ML models. These models are continuously trained on the data and actions using a feedback loop that enhances their capabilities with time. AIOps components leverage ML models to provide intelligent, automated remediation actions and evolving recommendations (Figure 1). Apart from the components mentioned in Table 1, the AIOps components for observability workflows are:

Table 2. AIOps components for observability workflows

How the Key Components Interact

A data ingestion tool, when applied with an AI/ML solution, provides an intelligent platform for observability. The components of AIOps for observability workflows work together to provide a unified experience of a continuously evolving AI/ML-based system. At the core of these interactions are data, an AI model, continuous learning, and prediction. Input data is sanitized, aggregated, and then fed to an AI/ML model, which will then be analyzed for possible risks, mitigation, and reporting. There are various open-source platforms (e.g., ELK, Prometheus, OpenTelemetry) that can be combined with AIOps platforms to provide a unified experience.

Benefits and Challenges of AIOps for Observability Workflows

AIOps introduces various benefits and increases the efficiency of a software development team. The benefits of utilizing observability workflows with AIOps are:

• AIOps in an observability workflow will detect issues faster, resulting in quicker resolution.
• With an ever-evolving system in place, less manual intervention is required.
• AIOps' full automation supported by AI/ML will lead to improved productivity.
• The security landscape is an ever-growing area, and using AIOps will strengthen security of the system.
• AIOps will result in enhanced compliance in a distributed system.
• AIOps' self-healing ability will improve the system's scalability and uptime.

However, these systems have various challenges associated with them, such as:

• The cost of implementation in terms of AI/ML inference, continuous learning pipelines, infrastructure, etc., is higher compared to observability workflows.
• AIOps will require niche knowledge of AI/ML models, training, retraining, etc., leading to a higher learning curve.
• AIOps introduces an extra component of AI/ML, which will lead to higher complexity of the overall system.
• The AIOps system will have a risk of bias associated with the data that is fed into the ML model.
• Once implemented, there could be a high chance of false positives due to the configuration of AI/ML models.
• AIOps still has skills gap issues; it requires an understanding of AI/ML, ops, and observability.
• It takes time to realize the return on investment for an AIOps system.

Enhance Existing Observability Workflows With AIOps

AIOps can be implemented in a new observability workflow as well as enable an existing workflow due to the amount of overlap between these two systems and the components that can be reused while upgrading to use AIOps. To implement an AIOps solution to an existing workflow, a systematic and modular approach is the most effective way to avoid any issues and to ensure a smooth rollout.

At a high level, the implementation process can be:

1. Study and identify the existing components of the workflow
2. Outline the reusable components once the systems are identified
3. Design the AIOps system with these components
4. Introduce the new components (e.g., AI/ML, continuous learning, monitoring, mitigation)
5. Implement a proof of concept (POC) on non-production environments using existing components
6. Plan a phased rollout for user acceptance testing once the POC is implemented successfully
7. Plan a production rollout

With a phased approach, any existing/legacy observability workflows can be made more intelligent using AIOps.

Conclusion

When combined with observability workflows, AIOps improves the capabilities of any complex software system. For instance, AIOps in observability workflows strengthen the system's security by proactively learning and mitigating security threats using AI and ML capabilities. This approach enhances the flexibility of complex distributed systems by implementing a more versatile, self-learning, evolving solution rather than a hardcoded, rule-based, traditional observability workflow. By utilizing the vast amount of generated data from various distributed systems and a feedback loop, AIOps can predict issues proactively and more accurately, thus lowering the service downtime, time to detect issues, and time to implement fixes, and providing better scalability.

AIOps does not stop there, and with time, it raises the operational excellence of the distributed systems and minimizes any manual dependence on scaling, detecting, and fixing issues. This will prove useful when looking forward; as cyber threats evolve and data volumes grow larger, AIOps will continue to be pivotal for enhancing such complex systems.

This is an excerpt from DZone's 2024 Trend Report, Observability and Performance: The Precipice of Building Highly Performant Software Systems.

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