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Generating fault-focused clusters is a common practice used in parallel fault localization where fault-focused rankings that are likely leading to the same firstly identified faulty statement are grouped together. With respect to the performance of fault-focused clustering and fault localization cost, one critical impact factor is the metric used to measure the distance between two rankings. Current work prefers using Kendall tau distance for its fitness in computing the ranking disagreement. In this paper, we tend to apply two other well-established ranking distance metrics, Spearman’s Footrule and a set intersection-based measure, to fault-focused clustering and cross compare the parallel fault location performance of using these three distinct distance metrics. [ABSTRACT FROM AUTHOR]

Modern data center networks for a fat-tree topology typically adopt a multirooted hierarchical tree structure to achieve multiple-path capability and increase bisection bandwidth. However, the performance of a data center network highly depends on the routing protocols. Conventional routing protocols are unsuitable for modern data center topologies because they lack multiple-path routing support. Another crucial concern in data center networks is load balancing. Certain routing protocol limitations could lead to overloaded or underloaded utilization of a link, thereby considerably reducing the performance of a data center network. Therefore, we present a genetic algorithm (GA)-based dynamic load-balancing routing algorithm, which is heuristic and involves the use of a centralized scheduling technique. This algorithm mainly uses a GA to search for optimal solutions. We implement our algorithm in an OpenFlow controller RYU and Mininet emulator, which is based on software-defined networking architecture. Our evaluation results revealed that our algorithm can effectively achieve load balancing and increase bisection bandwidth. [ABSTRACT FROM AUTHOR]

In this paper, we present the partitioning of mutual-exclusion (mutex) constraints in temporal planning problems and its implementation in the SGPlan4 planner. Based on the strong locality of mutex constraints observed in many benchmarks of the Fourth International Planning Competition (IPC4), we propose to partition the constraints of a planning problem into groups based on their subgoals. Constraint partitioning leads to significantly easier subproblems that are similar to the original problem and that can be efficiently solved by the same planner with some modifications to its objective function. We present a partition-and-resolve strategy that looks for locally optimal subplans in constraint-partitioned temporal planning subproblems and that resolves those inconsistent global constraints across the subproblems. We also discuss some implementation details of SGPlan4, which include the resolution of violated global constraints, techniques for handling producible resources, landmark analysis, path finding and optimization, search-space reduction, and modifications of Metric-FF when used as a basic planner in SGPlan4. Last, we show results on the sensitivity of each of these techniques in quality-time trade-offs and experimentally demonstrate that SGPlan4 is effective for solving the IPC3 and IPC4 benchmarks. [ABSTRACT FROM AUTHOR]