AutoTune: Improving End-to-end Performance and Resource Efficiency for Microservice Applications Article Swipe
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Michael Alan Chang
,
Aurojit Panda
,
Hantao Wang
,
Yuan-Cheng Tsai
,
Rahul Balakrishnan
,
Scott Shenker
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2106.10334
· OA: W3174628199
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2106.10334
· OA: W3174628199
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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