Cloud Bursting Scheduler for cost efficiency

Young Choon Lee, Bing Lian

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

11 Citations (Scopus)

Abstract

Clouds have been increasingly adopted due to primarily their elasticity and pay-as-you-go (PAYG) pricing. While many organizations outsource the entire ICT solution to public clouds like Amazon Web Services Elastic Compute Cloud (EC2), others consider occasional workload offloading (cloud bursting) due to various reasons including governance and security. In this paper, we present Cloud Bursting Scheduler (CBS), a new cloud bursting algorithm. CBS explicitly takes into account cost factors of private in-house system (or private cloud) and public cloud. In particular, CBS attempts to optimize the cost to performance ratio by offloading jobs to public cloud explicitly taking into account time-varying electricity rates with private clouds and the timeinvariant rental rate of many public clouds. Based on simulation results obtained using real workload traces, CBS saves costs of running workloads by 55% and 12% compared with costs of cloud sourcing and private cloud, respectively. It also improves resource utilization (to 89%) by judiciously (de)activating inhouse resources and dynamically provisioning cloud resources.

Original languageEnglish
Title of host publicationProceedings - 2017 IEEE 10th International Conference on Cloud Computing
Subtitle of host publicationCLOUD 2017
EditorsGeoffrey C. Fox
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages774-777
Number of pages4
ISBN (Electronic)9781538619933
DOIs
Publication statusPublished - 8 Sep 2017
Event10th IEEE International Conference on Cloud Computing, CLOUD 2017 - Honolulu, United States
Duration: 25 Jun 201730 Jun 2017

Conference

Conference10th IEEE International Conference on Cloud Computing, CLOUD 2017
CountryUnited States
CityHonolulu
Period25/06/1730/06/17

Keywords

  • cloud bursting
  • Cloud computing
  • cost efficiency
  • energy efficiency
  • scheduling

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