Advanced Strategies for Precise and Transparent Debugging of Performance Issues in In-Memory Data Store-Based Microservices
Herve Mbikayi Kabamba, Matthew Khouzam, Michel Dagenais

TL;DR
This paper presents a novel framework for transparent, fine-grained performance debugging in in-memory data store-based microservices, addressing the complexity and transparency challenges of existing monitoring methods.
Contribution
It introduces a new transparent tracing framework with a two-level analysis model for precise performance debugging in microservices.
Findings
Enables fine-grained performance analysis without code instrumentation
Provides visualization tools for performance issue identification
Improves debugging efficiency in complex microservice environments
Abstract
The rise of microservice architectures has revolutionized application design, fostering adaptability and resilience. These architectures facilitate scaling and encourage collaborative efforts among specialized teams, streamlining deployment and maintenance. Critical to this ecosystem is the demand for low latency, prompting the adoption of cloud-based structures and in-memory data storage. This shift optimizes data access times, supplanting direct disk access and driving the adoption of non-relational databases. Despite their benefits, microservice architectures present challenges in system performance and debugging, particularly as complexity grows. Performance issues can readily cascade through components, jeopardizing user satisfaction and service quality. Existing monitoring approaches often require code instrumentation, demanding extensive developer involvement. Recent strategies…
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Taxonomy
TopicsSoftware System Performance and Reliability · Cloud Computing and Resource Management · IoT and Edge/Fog Computing
