Advanced Strategies for Precise and Transparent Debugging of Performance Issues in In-Memory Data Store-Based Microservices.
CoRR(2023)
摘要
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
like proxies and service meshes aim to enhance tracing transparency, but
introduce added configuration complexities.
Our innovative solution introduces a new framework that transparently
integrates heterogeneous microservices, enabling the creation of tailored tools
for fine-grained performance debugging, especially for in-memory data
store-based microservices. This approach leverages transparent user-level
tracing, employing a two-level abstraction analysis model to pinpoint key
performance influencers. It harnesses system tracing and advanced analysis to
provide visualization tools for identifying intricate performance issues. In a
performance-centric landscape, this approach offers a promising solution to
ensure peak efficiency and reliability for in-memory data store-based cloud
applications.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要