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arXiv · 2106.10334

AutoTune: Improving End-to-end Performance and Resource Efficiency for Microservice Applications

Michael Alan Chang·Aurojit Panda·Hantao Wang·Yuancheng Tsai·Rahul Balakrishnan·Scott Shenker
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Abstract

Most large web-scale applications are now built by composing collections (from a few up to 100s or 1000s) of microservices. Operators need to decide how many resources are allocated to each microservice, and these allocations can have a large impact on application performance. Manually determining allocations that are both cost-efficient and meet performance requirements is challenging, even for experienced operators. In this paper we present AutoTune, an end-to-end tool that automatically minimizes resource utilization while maintaining good application performance.

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Michael Alan Chang, Aurojit Panda, Hantao Wang, Yuancheng Tsai, Rahul Balakrishnan, Scott Shenker. 2021-06-18. AutoTune: Improving End-to-end Performance and Resource Efficiency for Microservice Applications. https://arxiv.org/abs/2106.10334

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