A scalable super-peer approach for public scientific computation

Many types of distributed scientific and commercial applications require the submission of a large number of independent jobs. One highly successful, and low cost mechanism for acquiring the necessary compute power is the ''public-resource computing'' paradigm, which exploits the computational power of private computers. Recently decentralized peer-to-peer and super-peer technologies have been proposed for adaptation in these systems. We designed a super-peer protocol for the execution of jobs based upon the volunteer requests of workers, and a super-peer overlay for performing two kinds of matching operations: the assignment of jobs to workers and the download of input data needed for job execution. This paper analyzes a dynamic and general scenario, in which: (i) workers can leave the network at any time; (ii) each job is executed multiple times, either to obtain better statistical accuracy or to perform parameter sweep analysis; and, (iii) input data is replicated and distributed to multiple data caches on-the-fly. A simulation study was performed to analyze the super-peer protocol and specifically evaluate performance in terms of execution time, utilization of data centers, load balancing, and ability to efficiently scale with the number of jobs and the network size.