Identifying Root Causes of Performance Issues in Microservice-Based Applications: The PetShop Dataset

Core Concepts

This paper introduces a dataset designed to evaluate techniques for identifying the root causes of performance issues in microservice-based applications.

Abstract

The paper introduces a dataset for evaluating root cause analysis (RCA) techniques in microservice-based applications. The dataset includes metrics such as latency, requests, and availability collected from a distributed application comprising 41 microservices. In addition to normal operation metrics, the dataset includes 68 injected performance issues, such as request overload, memory leaks, CPU hogs, and misconfigurations, which increase latency and reduce availability throughout the system. The metrics are annotated with the corresponding issues, serving as ground truth for the analysis.
The authors showcase how this dataset can be used to evaluate the accuracy of a variety of RCA methods spanning different causal and non-causal characterizations of the problem. They find that causal methods perform well when provided the causal graph, but methods relying on learning the causal graph or the full structural causal model do not yield satisfactory performance when data is limited. In this case, a simple baseline of ranking potential root causes according to their correlation with the target and filtering for anomalous variables provides a strong baseline.
The authors hope that this dataset will enable further development of robust RCA techniques that can work well with limited data and do not require access to the causal graph, which is often not available in practice.

Stats

The dataset contains metrics such as latency, requests, and availability collected in 5-minute intervals from a distributed application comprising 41 microservices.

Quotes

None

Key Insights Distilled From

The PetShop Dataset -- Finding Causes of Performance Issues across Microservices

by Mich... at arxiv.org 04-10-2024

https://arxiv.org/pdf/2311.04806.pdf

Deeper Inquiries

How would the performance of the RCA methods change if the dataset included feedback loops, where user behavior changes in response to performance issues

Incorporating feedback loops, where user behavior changes in response to performance issues, would significantly impact the performance of Root Cause Analysis (RCA) methods. These feedback loops introduce a dynamic element to the system, where the actions of users in response to anomalies can create cascading effects on system metrics.

Effect on Causal Graph: The presence of feedback loops would complicate the causal graph, as the relationships between variables become more intricate. Traditional causal models may struggle to capture these dynamic interactions effectively.

Increased Complexity: RCA methods would need to account for the bidirectional nature of feedback loops, where system performance influences user behavior, and user behavior, in turn, affects system performance. This complexity could challenge existing methods that assume a unidirectional causal flow.

Behavioral Analysis: RCA methods would need to incorporate behavioral analysis to understand how user actions propagate through the system. This could involve analyzing user interactions, patterns, and responses to performance issues to identify the true root causes.

Adaptation of Algorithms: Algorithms used in RCA would need to adapt to handle the dynamic nature of feedback loops. Techniques like dynamic Bayesian networks or reinforcement learning may be more suitable for capturing these evolving relationships.

Real-time Monitoring: Real-time monitoring and analysis of user behavior and system metrics would be essential to capture the immediate impact of feedback loops on performance issues. This would require advanced data collection and processing capabilities.

In conclusion, incorporating feedback loops into the dataset would require RCA methods to evolve to handle the dynamic and bidirectional nature of user-system interactions, presenting both challenges and opportunities for improving the accuracy of root cause identification.

How can causal RCA methods be extended to handle cases with multiple root causes

Handling cases with multiple root causes in causal RCA methods requires a more sophisticated approach to identify and differentiate the various contributing factors to an anomaly. Here are some strategies to extend causal RCA methods for multiple root causes:

Causal Interaction Analysis: Develop methods to analyze the interactions between multiple root causes and how they collectively contribute to the observed anomaly. This involves understanding the combined effects of different variables on the target metric.

Probabilistic Graphical Models: Utilize probabilistic graphical models like Bayesian networks or Markov networks to represent the complex relationships between multiple root causes and the target variable. These models can capture dependencies and interactions effectively.

Counterfactual Reasoning: Extend counterfactual reasoning techniques to assess the counterfactual impact of each potential root cause in isolation and in combination with others. This helps in quantifying the individual and joint contributions of multiple factors.

Ensemble Methods: Implement ensemble methods that combine the outputs of multiple causal RCA algorithms to provide a comprehensive analysis of the root causes. This approach can leverage the strengths of different methods to improve accuracy.

Scenario Analysis: Conduct scenario-based analysis to simulate the effects of different combinations of root causes on the system metrics. This allows for a more holistic understanding of the potential interactions and their impact.

By incorporating these strategies, causal RCA methods can be extended to effectively handle cases with multiple root causes, providing a more nuanced and comprehensive analysis of complex system anomalies.

What other types of performance issues, beyond those considered in this dataset, are commonly observed in real-world microservice-based applications, and how can the dataset be expanded to cover a more diverse set of issues

Real-world microservice-based applications often encounter a diverse range of performance issues beyond those considered in the dataset. Expanding the dataset to cover a broader set of issues can enhance its utility and relevance for evaluating RCA methods. Some common types of performance issues observed in real-world microservice applications include:

Network Latency: Issues related to network latency, packet loss, or bandwidth constraints can impact the communication between microservices, leading to delays in data transfer and processing.

Resource Contention: Problems arising from resource contention, such as CPU, memory, or disk bottlenecks, can affect the overall performance of microservices and cause slowdowns or failures.

Security Vulnerabilities: Security-related issues like unauthorized access, data breaches, or denial-of-service attacks can disrupt the normal operation of microservices and compromise system integrity.

Dependency Failures: Failures in external dependencies, such as third-party APIs, databases, or cloud services, can result in service disruptions and errors in microservice interactions.

Configuration Errors: Misconfigurations in deployment settings, environment variables, or service configurations can lead to unexpected behavior and performance degradation across microservices.

To expand the dataset to cover a more diverse set of issues, the following steps can be taken:

Issue Identification: Conduct a thorough analysis of common performance issues in microservice architectures and identify additional types of anomalies to inject into the dataset.

Data Generation: Simulate and inject a variety of new performance issues, including those related to security, network, dependencies, and configuration, into the dataset to create a more comprehensive set of scenarios.

Annotation and Ground Truth: Provide detailed annotations and ground truth information for the newly added issues to facilitate accurate evaluation and benchmarking of RCA methods.

Evaluation Metrics: Define specific evaluation metrics tailored to the new types of issues to assess the performance of RCA methods in identifying and resolving these diverse anomalies.
By expanding the dataset to encompass a wider range of performance issues, researchers and practitioners can gain deeper insights into the effectiveness of RCA methods in addressing the complexities and challenges of real-world microservice applications.