Transformation Approach to Model Online Gaming Traffic

In this paper, we propose a transformation scheme used to analyze online gaming traffic properties and develop a traffic model. We analyze the packet size and the inter departure time distributions of a popular first‐person shooter game (Left 4 Dead) and a massively multiplayer online role‐playing game (World of Warcraft) in order to compare them to the existing scheme. Recent online gaming traffic is erratically distributed, so it is very difficult to analyze. Therefore, our research focuses on a transformation scheme to obtain new smooth patterns from a messy dataset. It extracts relatively heavy‐weighted density data and then transforms them into a corresponding dataset domain to obtain a simplified graph. We compare the analytical model histogram, the chi‐square statistic, and the quantile‐quantile plot of the proposed scheme to an existing scheme. The results show that the proposed scheme demonstrates a good fit in all parts. The chi‐square statistic of our scheme for the Left 4 Dead packet size distribution is less than one ninth of the existing one when dealing with erratic traffic.     

Task scheduling algorithm using minimized duplications in homogeneous systems

For fine grain task graphs, duplication-based scheduling algorithms are generally more efficient than list and cluster-based algorithms. However, most duplication-based heuristics try to duplicate all possible ancestor nodes of a given join node, in order to reduce the earliest start time (EST) of the join node, even though these ancestor nodes have already been allocated in previous steps. Thus, these duplication heuristics inevitably induce redundant duplications, which lead to the superfluous consumption of resources and generally deteriorate the scheduling result in the case of a bounded number of processors. When scheduling algorithms are used on an unbounded number of processors, the required number of processors grows excessively with the size of the task graph, thereby limiting the practicality of these algorithms for large task graphs. In this paper, we propose a novel algorithm designed to allocate join nodes without redundant duplications. In the proposed algorithm, if the ancestor nodes of a join node are duplicated when scheduling the join node, the original allocations of these ancestor nodes are removed using a very efficient method. The performance of the proposed algorithm, in terms of its normalized schedule length and efficiency, is compared with that of some of the recently proposed algorithms. The proposed algorithm generates better or comparable schedules with minimized duplication. Specifically, the simulation results show that it is most useful on a bounded number of processors.