| dc.description.abstract | Process migration is a technique used in the remapping of a process to a faster processor or in the approaching from the processes which already have some communication among themselves. This essay describes the MigBSP++, a rescheduling process model that uses the technique of migration to perform load balancing in parallel systems. Directed to the BulkSynchronous Parallel (BSP) applications, the model redistributes the processes with the purpose of reducing the time of each super-step. Similar to MigBSP way, MigBSP++ combines multiple metrics to decide which migrations should be chosen in order to balance the entire system without the user intervention. The metrics used by the model are: computing, communication and extra costs of migration. Through its decision function, called Potential Migration (PM), these metrics are used to choose the most appropriate processes that will balance the system. MigBSP++ answers the questions about the policy process migration issues: when to perform the migration process, which processes are candidates for migration and where to migrate the selected processes. As scientific contribution, MigBSP++ introduces the solutions to two issues that were missing at MigBSP: (a) the detection of imbalance load when there are more processes than processors, and (b) the definition of how many processes will migrate indeed. On the question (a), a change of the mode of detection of imbalance is proposed, noting the total computation time for each processor. On the second question (b) an algorithm called the Prediction Algorithm BSP (PABSP) is presented. The input data of PABSP are elected process by the PM technique and the output is a list of processes that will, indeed, migrate providing a time reduction of the next super-step. To demonstrate the results of applying this model, two BSP applications have been developed with the assistance of Adaptive Message Passing Interface (AMPI) library. This tool provides a uniform framework that, through the migration process, allows a transparent load balancing to the user. Based on MigBSP and MigBSP++, load balancing strategies have been developed for the performance and comparison among new strategies and among the ones which were already in the system.The results indicate that, in cases where the granularity of the task, the gains in runtime are more evident, reaching up to 46% compared to the application without balancing, and 37% when compared to native strategies AMPI. These numbers suggest that the model MigBSP++ has practical application and can produce satisfactory results. | |
