In this paper, we present a distributed computing framework designed to support higher quality of service and fault tolerance for processing deadline-driven tasks in a P2P environment. Our proposed strategy strives to build an open infrastructure that is accessible by ordinary users for both cycle donation and consumption. For jobs that fail to be locally accommodated, the proposed scheduler MET (maximum efficiency tree) builds a dynamic multi-level resource tree with minimal yet sufficient power to process the job prior to its deadline. The peer selection policy is based on a joint evaluation of the computational power and communication bandwidth at the nodes. Further, with an optimal load sharing scheme, the resulting resource tree is guaranteed to be power efficient. The proposed computing protocol offers an approach for utilizing idle computing cycles of peer computers on the Internet in a P2P manner. The protocol exhibits three attractive features - decentralized operation, optimized load balancing and guaranteed resource utilization. Extensive simulation experiments are conducted to study the effectiveness of the proposed framework under various network conditions. We compare our strategy with two other tree construction algorithms, namely MST (minimum spanning tree) and MCT (maximum computation tree). It is demonstrated that MET outperforms both MST and MCT consistently. Further, sensitivity results with random node failure/join are also furnished
['Jingnan Yao', 'Jian Zhou', 'Laxmi N. Bhuyan']
Computing Real Time Jobs in P2P Networks