MIRAS: Model-based reinforcement learning for microservice resource allocation over scientific workflows

Zhe Yang, Phuong Nguyen, Haiming Jin, Klara Nahrstedt

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Microservice, an architectural design that decomposes applications into loosely coupled services, is adopted in modern software design, including cloud-based scientific workflow processing. The microservice design makes scientific workflow systems more modular, more flexible, and easier to develop. However, cloud deployment of microservice workflow execution systems doesn't come for free, and proper resource management decisions have to be made in order to achieve certain performance objective (e.g., response time) within constraint operation cost. Nevertheless, effective online resource allocation decisions are hard to achieve due to dynamic workloads and the complicated interactions of microservices in each workflow. In this paper, we propose an adaptive resource allocation approach for microservice workflow system based on recent advances in reinforcement learning. Our approach (1) assumes little prior knowledge of the microservice workflow system and does not require any elaborately designed model or crafted representative simulator of the underlying system, and (2) avoids high sample complexity which is a common drawback of model-free reinforcement learning when applied to real-world scenarios. We show that our proposed approach automatically achieves effective policy for resource allocation with limited number of time-consuming interactions with the microservice workflow system. We perform extensive evaluations to validate the effectiveness of our approach and demonstrate that it outperforms existing resource allocation approaches with read-world emulated workflows.

Original languageEnglish (US)
Title of host publicationProceedings - 2019 39th IEEE International Conference on Distributed Computing Systems, ICDCS 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages122-132
Number of pages11
ISBN (Electronic)9781728125190
DOIs
StatePublished - Jul 2019
Event39th IEEE International Conference on Distributed Computing Systems, ICDCS 2019 - Richardson, United States
Duration: Jul 7 2019Jul 9 2019

Publication series

NameProceedings - International Conference on Distributed Computing Systems
Volume2019-July

Conference

Conference39th IEEE International Conference on Distributed Computing Systems, ICDCS 2019
CountryUnited States
CityRichardson
Period7/7/197/9/19

Keywords

  • Microservice
  • Reinforcement learning
  • Resource allocation

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Networks and Communications

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    Yang, Z., Nguyen, P., Jin, H., & Nahrstedt, K. (2019). MIRAS: Model-based reinforcement learning for microservice resource allocation over scientific workflows. In Proceedings - 2019 39th IEEE International Conference on Distributed Computing Systems, ICDCS 2019 (pp. 122-132). [8885267] (Proceedings - International Conference on Distributed Computing Systems; Vol. 2019-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICDCS.2019.00021