Nowadays, many Internet users make use of Peer-to-Peer (P2P) systems to download electronic content including music, movies, software, etc. Growing popularity in P2P based protocol implementations for file sharing purposes caused that the P2P traffic exceeds Web traffic and in accordance with to many statistics, P2P systems produce a more than 50% of the whole Internet traffic. Therefore, P2P systems provide remarkable income for Internet Service Providers (ISP). However, at the same time P2P systems generates many problems related to traffic engineering, optimization, network congestion. In this paper we focus on the problem of flow optimization in P2P file sharing systems. Corresponding to BitTorrent-based systems behaviour, the optimization of P2P flows is very complex and in this work we consider different heuristic strategies for content distribution and moreover we propose a new evolutionary algorithm (EA) for this problem. We compare results of the algorithms against optimal results yielded by CPLEX solver for networks including 10 peers and relation to random algorithm for 100-node systems. According to numerical experiments, the EA provides solutions close to optimal for small instances and all of the heuristics exhibit a superior performance over random search.
Recently, distributed computing system have been gaining much attention due to a growing demand for various kinds of effective computations in both industry and academia. In this paper, we focus on Peer-to-Peer (P2P) computing systems, also called public-resource computing systems or global computing systems. P2P computing systems, contrary to grids, use personal computers and other relatively simple electronic equipment (e.g., the PlayStation console) to process sophisticated computational projects. A significant example of the P2P computing idea is the BOINC (Berkeley Open Infrastructure for Network Computing) project. To improve the performance of the computing system, we propose to use the P2P approach to distribute results of computational projects, i.e., results are transmitted in the system like in P2P file sharing systems (e.g., BitTorrent). In this work, we concentrate on offline optimization of the P2P computing system including two elements: scheduling of computations and data distribution. The objective is to minimize the system OPEX cost related to data processing and data transmission. We formulate an Integer Linear Problem (ILP) to model the system and apply this formulation to obtain optimal results using the CPLEX solver. Next, we propose two heuristic algorithms that provide results very close to an optimum and can be used for larger problem instances than those solvable by CPLEX or other ILP solvers.
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Various kinds of distributed systems gain much attention in recent years. One of the most significant example is the Peer-to-Peer (P2P) paradigm widely used in many applications including: file-sharing systems (e.g. BitTorrent), computing systems (e.g. SETI@home), communication systems (e.g. Skype) and many others. In this work we present our latest research related to the problem of P2P-based distributed systems optimization. We consider two following problems: optimization of data distribution P2P systems and optimization of P2P computing systems. For both problems we formulate Integer Programming models. Due to the complexity of these problems, exact methods can be applied only for relatively small instances. Therefore, we propose several heuristic algorithms including tabu search, evolutionary algorithm, constructive heuristic and random approach. Results of extensive numerical experiments show the effectiveness of proposed algorithms in comparison to optimal results yielded by CPLEX solver. The optimization methods presented in this paper can be used for optimization of various P2P systems.
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