GCRIS

As the distributed computing systems have been widely used in many research and industrial areas, the problem of allocating tasks to available processors in the system efficiently has been an important concern. Since the problem is proven to be NP-hard, heuristic-based optimization techniques have been proposed to solve the task allocation problem. Particularly, the current cloud-based systems have been grown massively requiring multiple features like lower cost, higher reliability, and higher throughput; therefore, the problem has become more challenging and approximate methods have gained more importance. Migrating birds optimization (MBO) algorithm offers successful solutions, especially for quadratic assignment problems. Inspired by the movement of the birds, it exhibits good results by its population-based approach . Since the algorithm needs to deal with many individuals in the population, and the neighbor solution generation phase takes substantial time for large problem instances, we need parallelism to have execution time improvements and make the algorithm practical for large-scale problems. In this work, we propose a scalable parallel implementation of the MBO algorithm, PMBO, for the multi-objective task allocation problem. We redesigned the implementation of the MBO algorithm so that its computationally heavy independent tasks are executed concurrently in separate threads. We compare our implementation with three parallel island-based approaches. The experimental results demonstrate that our implementation exhibits substantial solution quality improvements for difficult problem instances as the computing resources, namely parallelism, increase. Our scalability analysis also presents that higher parallelism levels offer larger solution improvement for the PMBO over the island-based parallel implementations on very hard problem instances.

We present new serial and parallel algorithms for multilevel graph partitioning. Our algorithm has coarsening, partitioning and uncoarsening phases like other multilevel partitioning methods. However, we choose fixed nodes which are at least a specified distance away from each other and coarsen them with their neighbor nodes in the coarsening phase using various heuristics. Using this algorithm, it is possible to obtain theoretically and experimentally much more balanced partitions with substantially decreased total edge costs between the partitions than other algorithms. We also developed a parallel method for the fixed centered partitioning algorithm. It is shown that parallel fixed centered partitioning obtains significant speedups compared to the serial case.

Computer Engineering / Bilgisayar Mühendisliği

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