RAMBO: Resource Allocation for Microservices Using Bayesian Optimization

Microservices are becoming the defining paradigm of cloud applications, which raises urgent challenges for efficient datacenter management. Guaranteeing end-to-end Service Level Agreement (SLA) while optimizing resource allocation is critical to both cloud service providers and users. However, one a...

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Bibliographic Details
Published inIEEE computer architecture letters Vol. 20; no. 1; pp. 46 - 49
Main Authors Li, Qian, Li, Bin, Mercati, Pietro, Illikkal, Ramesh, Tai, Charlie, Kishinevsky, Michael, Kozyrakis, Christos
Format Journal Article
LanguageEnglish
Published New York IEEE 01.01.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Bayes methods
Bayesian analysis
Cloud computing
Distributed applications
Distributed computing
emerging technologies
Multiple objective analysis
Optimization
Resource allocation
Resource management
Servers
Service level agreements
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Summary:Microservices are becoming the defining paradigm of cloud applications, which raises urgent challenges for efficient datacenter management. Guaranteeing end-to-end Service Level Agreement (SLA) while optimizing resource allocation is critical to both cloud service providers and users. However, one application may contain hundreds of microservices, which constitute an enormous search space that is unfeasible to explore exhaustively. Thus, we propose RAMBO, an SLA-aware framework for microservices that leverages multi-objective Bayesian Optimization (BO) to allocate resources and meet performance/cost goals. Experiments conducted on a real microservice workload demonstrate that RAMBO can correctly characterize each microservice and efficiently discover Pareto-optimal solutions. We envision that the proposed methodology and results will benefit future resource planning, cluster orchestration, and job scheduling.
ISSN:1556-6056
1556-6064
DOI:10.1109/LCA.2021.3066142