CSR: Small: Collaborative Research: Hybrid Opportunistic Computing for Green Clouds

Start Date: 09/01/2009
End Date: 08/31/2013

On-demand, service-oriented cloud computing infrastructures continue to increase in popularity with organizations. Three observations motivate us to investigate running high-throughput, data-intensive tasks as background workloads on these cloud infrastructures. First, the rapid growth in hardware parallelism leaves more residue resources to be exploited. Second, the ``incremental power usage'' of piggybacking a secondary background workload onto the foreground workload to utilize those residue resources is relatively low. Third, the advances in GPGPU (General-Purpose GPU) processing enable a novel coupling of concurrent workloads.

 

This project will explore a new computing model of offering cloud services on active nodes that are serving on-demand utility computing users. We plan to (1) assess the efficacy of resource sharing between foreground and background workloads and investigate the relationship between their resource usage patterns and the benefit and cost of their mixed execution; (2) develop scheduling and load management middleware that performs dynamic background workload distribution considering the energy-performance tradeoff; and (3) exploit the use of GPGPUs for cloud services on active nodes that are running foreground workloads mainly on the CPUs.

 

Our research will explore a revolutionary change in the use of cloud computing and may influence their hosting organizations' future resource configuration and planning to create greener clouds. The research will be closely integrated with education-oriented cloud platforms at NCSU. The PIs will also leverage their established services and connections to increase the participation of women and minority students and to promote students' interactions with industry partners.

Grant Institution: National Science Foundation

Amount: $150,156

People associated with this grant:

Wu Feng