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THE PROBLEM OF ASSIGNING CLIENTS TO SERVERS FOR MAXIMIZING THE INTERACTIVITY OF DISTRIBUTED INTERACTIVE APPLICATION

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Distributed interactive applications (DIAs), such as multiplayer online games and distributed interactive simulations, allow participants at different locations to interact with one another through networks. Thus, the interactivity of DIAs is important for participants to have enjoyable interaction experiences. Normally, interactivity is characterized by the duration from the time when a participant issues an operation to the time when the effect of the operation is presented to the same participant or other participants. Interactivity is a primary performance measure for distributed interactive applications (DIAs) that enable participants at different locations to interact with each other in real time. Wide geographical spreads of participants in large-scale DIAs necessitate distributed deployment of servers to improve interactivity. In a distributed server architecture, the interactivity performance depends on not only client-to-server network latencies but also interserver network latencies, as well as synchronization delays to meet the consistency and fairness requirements of DIAs. All of these factors are directly affected by how the clients are assigned to the servers. In this abstract, we investigate the problem of effectively assigning clients to servers for maximizing the interactivity of DIAs. We focus on continuous DIAs that changes their states not only in response to user operations but also due to the passing of time. We analyze the minimum achievable interaction time for DIAs to preserve consistency and provide fairness among clients, and formulate the client assignment problem as a combinatorial optimization problem. We prove that this problem is NP-complete. Three heuristic assignment algorithms are proposed and their approximation ratios are theoretically analyzed. The performance of the algorithms is also experimentally evaluated using real Internet latency data. The experimental results show that our proposed Greedy Assignment and Distributed-Modify Assignment algorithms generally produce near optimal interactivity and significantly reduce the interaction time between clients compared to the intuitive algorithm that assigns each client to its nearest server.