Fine-grained resource scaling in a public cloud: A Tenant's perspective

Cheng Wang, Aayush Gupta, Bhuvan Urgaonkar

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

7 Scopus citations

Abstract

Growing tenant workload needs and an increasingly competitive market will force cloud providers to operate their data centers at significantly higher utilization levels than seen today. We argue that a key enabler of such cloud ecosystems would be facilities for tenants to engage in fine-grained resource scaling in addition to those offered by current providers. The basic unit of resource scaling exposed by current cloud providers is the canonical interface of virtual machines (VMs) with relatively static resource capacities. This paper describes opportunities and challenges in augmenting this interface to also include finegrained scaling of CPU and memory within an already procured VM. Qualitative arguments for why this would offer cost benefits for both the provider and its tenants are presented. We focus on the cost-effective operation of a tenant in such an environment via the design of a feedback controller. The efficacy of our ideas is illustrated by implementing a case study in a Memcached tenant workload. Our results are promising and point to an interesting and broad area for further research-e.g., with the real-world workload in our evaluation, up to 50% utility improvement can be achieved by just applying memory scaling; a further 66% improvement can be achieved by coordinating fine-grained CPU and memory scaling.

Original languageEnglish (US)
Title of host publicationProceedings - 2016 IEEE 9th International Conference on Cloud Computing, CLOUD 2016
EditorsIan Foster, Nimish Radia, Ian Foster
PublisherIEEE Computer Society
Pages124-131
Number of pages8
ISBN (Electronic)9781509026197
DOIs
StatePublished - Jan 17 2017
Event9th International Conference on Cloud Computing, CLOUD 2016 - San Francisco, United States
Duration: Jun 27 2016Jul 2 2016

Publication series

NameIEEE International Conference on Cloud Computing, CLOUD
ISSN (Print)2159-6182
ISSN (Electronic)2159-6190

Other

Other9th International Conference on Cloud Computing, CLOUD 2016
CountryUnited States
CitySan Francisco
Period6/27/167/2/16

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Information Systems
  • Software

Fingerprint Dive into the research topics of 'Fine-grained resource scaling in a public cloud: A Tenant's perspective'. Together they form a unique fingerprint.

Cite this