MicroProf : Code-level Attribution of Unnecessary Data Transfer in Microservice Applications

Author:

Tariq Syed Salauddin Mohammad1ORCID,Menard Lance1ORCID,Su Pengfei2ORCID,Roy Probir1ORCID

Affiliation:

1. University of Michigan - Dearborn

2. University of California - Merced

Abstract

The microservice architecture style has gained popularity due to its ability to fault isolation, ease of scaling applications, and developer’s agility. However, writing applications in the microservice design style has its challenges. Due to the loosely coupled nature, services communicate with others through standard communication APIs. This incurs significant overhead in the application due to communication protocol and data transformation. An inefficient service communication at the microservice application logic can further overwhelm the application. We perform a grey literature review showing that unnecessary data transfer is a real challenge in the industry. To the best of our knowledge, no effective tool is currently available to accurately identify the origins of unnecessary microservice communications that lead to significant performance overhead and provide guidance for optimization. To bridge the knowledge gap, we propose MicroProf , a dynamic program analysis tool to detect unnecessary data transfer in Java-based microservice applications. At the implementation level, MicroProf proposes novel techniques such as remote object sampling and hardware debug registers to monitor remote object usage. MicroProf reports the unnecessary data transfer at the application source code level. Furthermore, MicroProf pinpoints the opportunities for communication API optimization. MicroProf is evaluated on four well-known applications involving two real-world applications and two benchmarks, identifying five inefficient remote invocations. Guided by MicroProf , API optimization achieves an 87.5% reduction in the number of fields within REST API responses. The empirical evaluation further reveals that the optimized services experience a speedup of up to 4.59×.

Funder

National Science Foundation

Publisher

Association for Computing Machinery (ACM)

Subject

Hardware and Architecture,Information Systems,Software

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