Crowdware: a framework for GPU-based public-resource computing with energy-aware incentive mechanism

Zhongli Dong, Young Choon Lee, Albert Y. Zomaya

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

1 Citation (Scopus)

Abstract

The power of the crowd, more precisely crowdsourced resources, is in its ubiquity. Accounting for traditional desktop/laptop computers and recent mobile computing devices including tablets and smart phones far surpasses the number of servers in cloud data centers. Besides, the capacity and capability of these resources owned by the crowd (crowd-sourced resources) has increased dramatically with GPUs in particular. Although a myriad of public-resource (or volunteer) computing projects, including SETI@home and Milkyway@home, have attracted the participation of crowd-sourced resources at very large scale, the sustainability of such participation is in doubt due primarily to ever-increasing energy costs. In this paper, we present Crowdware, a framework for enabling sustainable GPU-based public-resource computing with a realistic financial incentive mechanism. To this end, we design an auction-based resource allocation algorithm and a profit-based resource participation algorithm, explicitly considering the electricity cost of participating resources. Our results show that Crowdware greatly promotes profitability and cost efficiency for resource providers and resource consumers, respectively. Specifically, Crowdware has enabled the execution of MD5 password recovery jobs, in our testbed, with only 2.2% of the cost of using Amazon EC2 GPU instances while the participation of crowd-sourced resources is profitable with an average profit rate of 9.2%. Crowdware also shows great scalability with its fat-client and thin-server design. Together, Crowdware significantly improves the sustainability of public-resource computing.

Original languageEnglish
Title of host publicationProceedings - IEEE 7th International Conference on Cloud Computing Technology and Science, CloudCom 2015
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages266-273
Number of pages8
ISBN (Electronic)9781467395601
ISBN (Print)9781467395618
DOIs
Publication statusPublished - 2015
Event7th IEEE International Conference on Cloud Computing Technology and Science, CloudCom 2015 - Vancouver, Canada
Duration: 30 Nov 20153 Dec 2015

Other

Other7th IEEE International Conference on Cloud Computing Technology and Science, CloudCom 2015
CountryCanada
CityVancouver
Period30/11/153/12/15

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