Crowdware: A Framework for GPU-Based Public-Resource Computing with Energy-Aware Incentive Mechanism

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.