SLA-based resource scheduling for Big Data Analytics as a service in Cloud computing environments

Yali Zhao, Rodrigo N. Calheiros, Graeme Gange, Kotagiri Ramamohanarao, Rajkumar Buyya

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

29 Citations (Scopus)

Abstract

Data analytics plays a significant role in gaining insight of big data that can benefit in decision making and problem solving for various application domains such as science, engineering, and commerce. Cloud computing is a suitable platform for Big Data Analytic Applications (BDAAs) that can greatly reduce application cost by elastically provisioning resources based on user requirements and in a pay as you go model. BDAAs are typically catered for specific domains and are usually expensive. Moreover, it is difficult to provision resources for BDAAs with fluctuating resource requirements and reduce the resource cost. As a result, BDAAs are mostly used by large enterprises. Therefore, it is necessary to have a general Analytics as a Service (AaaS) platform that can provision BDAAs to users in various domains as consumable services in an easy to use way and at lower price. To support the AaaS platform, our research focuses on efficiently scheduling Cloud resources for BDAAs to satisfy Quality of Service (QoS) requirements of budget and deadline for data analytic requests and maximize profit for the AaaS platform. We propose an admission control and resource scheduling algorithm, which not only satisfies QoS requirements of requests as guaranteed in Service Level Agreements (SLAs), but also increases the profit for AaaS providers by offering a cost-effective resource scheduling solution. We propose the architecture and models for the AaaS platform and conduct experiments to evaluate the proposed algorithm. Results show the efficiency of the algorithm in SLA guarantee, profit enhancement, and cost saving.

Original languageEnglish
Title of host publicationProceedings - 2015 44th International Annual Conference on Parallel Processing, The 44th Annual Conference, ICPP 2015
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages510-519
Number of pages10
ISBN (Electronic)9781467375870
DOIs
Publication statusPublished - 2015
Externally publishedYes
EventIEEE International Conference on Intelligent Computer Communication and Processing 2015 - Beijing, China
Duration: 1 Sep 20154 Sep 2015
Conference number: 44th
https://web.archive.org/web/20150822093921/http://icpp2015.tsinghua.edu.cn/

Conference

ConferenceIEEE International Conference on Intelligent Computer Communication and Processing 2015
Abbreviated titleICPP 2015
CountryChina
CityBeijing
Period1/09/154/09/15
Internet address

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