We investigate an underlying mathematical model and algorithms for optimizing the performance of a class of distributed systems over the Internet. Such a system consists of a large number of clients who communicate with each other indirectly via a number of intermediate servers. Optimizing the overall performance of such a system then can be formulated as a client-server assignment problem whose aim is to assign the clients to the servers in such a way to satisfy some prespecified requirements on the communication cost and load balancing. We show that 1) the total communication load and load balancing are two opposing metrics, and consequently, their tradeoff is inherent in this class of distributed systems; 2) in general, finding the optimal client-server assignment for some prespecified requirements on the total load and load balancing is NP-hard, and therefore; 3) we propose a heuristic via relaxed convex optimization for finding the approximate solution. Our simulation results indicate that the proposed algorithm produces superior performance than other heuristics, including the popular Normalized Cuts algorithm.

We investigate an underlying mathematical model and algorithm for optimizing the performance of a class of distributed systems over the Internet. Such a system consists of a large number of clients who communicate with each other indirectly via a number of intermediate servers. Optimizing the overall performance of such a system then can be formulated as a client server assignment problem whose aim is to assign the clients to the servers in such a way to satisfy some prespecified requirements on the communication cost and load balancing. We show that 1) the total communication load and load balancing are two opposing metrics, and consequently, their trade-off is inherent to this class of distributed systems; 2) in general, finding the optimal client-server assignment for some pre-specified requirements on the total load and load balancing is NP-hard, and therefore; 3) we propose a heuristic via relaxed convex optimization for finding the approximate solution to the client-server assignment problem. Our simulation results indicate that the proposed algorithm produces superior performance than other heuristics, including the popular Normalized Cuts algorithm.

For many P2P systems, implementing right incentives and policies to promote efficient and fair resource sharing is the key to improve the overall system performance. In this paper, we propose a points-based incentive mechanism named Global Contribution (GC) approach that efficiently and naturally maintains fairness in a P2P network. In this approach, a proposed GC algorithm first calculates a global score for each peer that accurately reflects its bandwidth contribution to the entire network. Then, these scores are used in a proposed data transfer policy to determine whether one peer can download data from other peers. Thus, the GC approach achieves: 1) efficiently preventing free-riding, 2) naturally balancing the upload and download amounts in each peer, and 3) reducing rejections in transactions between cooperative peers. Moreover, the GC algorithm requires only private transaction history as an input and can be fully decentralized. Also, its time complexities are approximately O(N^2) in a centralized system and O(N) per peer in a decentralized system.