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Data Fusion of Observability Signals for Assisting Orchestration of Distributed Applications.

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  • 1School of Electrical and Computer Engineering, National Technical University of Athens, 10682 Athens, Greece.

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Summary

This study introduces a new observability approach for microservices, fusing data from tracing, metrics, and logs. This helps identify latency bottlenecks and understand application performance under stress in cloud and edge environments.

Keywords:
distributed applicationsdistributed tracingedge computing orchestrationexemplarsmicroservicesobservability

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Area of Science:

  • Computer Science
  • Software Engineering
  • Distributed Systems

Background:

  • Microservices-based applications require robust observability for managing operational issues.
  • Existing distributed tracing frameworks provide siloed information, separate from cloud orchestration platforms.
  • High latencies in microservice interactions are a significant performance bottleneck.

Purpose of the Study:

  • To develop a modern observability approach for fusing data from various sources in cloud and edge computing.
  • To enhance the management and insight generation for distributed applications.
  • To enable effective root cause analysis of performance issues.

Main Methods:

  • Integration of signals from open-source monitoring and observability frameworks (metrics, logs, distributed tracing).
  • Development of a data fusion approach for edge and cloud computing orchestration platforms.
  • Validation through deployment and stress testing of a microservices-based application in an experimental environment.

Main Results:

  • Successful fusion of disparate observability data (metrics, logs, traces).
  • Identification of primary latency causes within different application components.
  • Improved understanding of application behavior under various stressing conditions.

Conclusions:

  • The proposed approach enhances observability by integrating diverse data sources.
  • Data fusion enables better performance issue detection and root cause analysis in microservices.
  • The pilot implementation demonstrates the practical utility in cloud and edge orchestration.