Applications involving multifarious computational requirements take the advantage of the versatility of heterogeneous computing systems(HCS)with more than one type of parallelism.Efficient scheduling of workflow appli...Applications involving multifarious computational requirements take the advantage of the versatility of heterogeneous computing systems(HCS)with more than one type of parallelism.Efficient scheduling of workflow applications is paramount to harness high performance from HCS.In the present work,a new list-based heuristic strategy namely maximizing parallelism for minimizing earliest finish time(MPEFT)algorithm is proposed with a primary objective of minimizing the makespan.In order to minimize the makespan,the proposed scheduling policy focuses on proliferating the parallelism of the workflows by choosing the globally heaviest task with more number of successors such that more number of successors can be released.Thus,the priority policy maximizes the length of the ready queue by exploring higher degree of parallelism of the workflow.The proposed approach is designed to adapt depth-wise whenever the tasks at subsequent levels are released and continues to be level-wise otherwise.This increases the degree of parallelism and shortens the makespan.To evaluate the proposed scheduling algorithm,experimentations are conducted using randomly generated workflows and scientific workflows namely LIGO,Epigenomics,Cybershake,and Montage.The experimental results show that the proposed MPEFT algorithm surpassed the classical list based heuristic algorithms in terms of metrics viz.,makespan,speedup,efficiency and frequency of best results.展开更多
Computationally intensive applications embodied as workflows entail interdependent tasks that involve multifarious computation requirements and necessitate Heterogeneous Distributed Computing Systems(HDCS)to attain hi...Computationally intensive applications embodied as workflows entail interdependent tasks that involve multifarious computation requirements and necessitate Heterogeneous Distributed Computing Systems(HDCS)to attain high performance.The scheduling of workflows on HDCS was demonstrated as an NP-Complete problem.In the current work,a new heuristic based Branch and Bound(BnB)technique namely Critical Path_finish Time First(CPTF)algorithm is proposed for workflow scheduling on HDCS to achieve the best solutions.The primary merits of CPTF algorithm are due to the bounding functions that are tight and of less complexity.The sharp bounding functions could precisely estimate the promise of each state and aid in pruning infeasible states.Thus,the search space size is reduced.The CPTF algorithm explores the most promising states in the search space and converges to the solution quickly.Therefore,high performance is achieved.The experimental results on random and scientific workflows reveal that CPTF algorithm could effectively exploit high potency of BnB technique in realizing better quality solutions against the widely referred heuristic scheduling algorithms.The results on the benchmark workflows show that CPTF algorithm has improved schedules for 89.36%of the cases.展开更多
文摘Applications involving multifarious computational requirements take the advantage of the versatility of heterogeneous computing systems(HCS)with more than one type of parallelism.Efficient scheduling of workflow applications is paramount to harness high performance from HCS.In the present work,a new list-based heuristic strategy namely maximizing parallelism for minimizing earliest finish time(MPEFT)algorithm is proposed with a primary objective of minimizing the makespan.In order to minimize the makespan,the proposed scheduling policy focuses on proliferating the parallelism of the workflows by choosing the globally heaviest task with more number of successors such that more number of successors can be released.Thus,the priority policy maximizes the length of the ready queue by exploring higher degree of parallelism of the workflow.The proposed approach is designed to adapt depth-wise whenever the tasks at subsequent levels are released and continues to be level-wise otherwise.This increases the degree of parallelism and shortens the makespan.To evaluate the proposed scheduling algorithm,experimentations are conducted using randomly generated workflows and scientific workflows namely LIGO,Epigenomics,Cybershake,and Montage.The experimental results show that the proposed MPEFT algorithm surpassed the classical list based heuristic algorithms in terms of metrics viz.,makespan,speedup,efficiency and frequency of best results.
文摘Computationally intensive applications embodied as workflows entail interdependent tasks that involve multifarious computation requirements and necessitate Heterogeneous Distributed Computing Systems(HDCS)to attain high performance.The scheduling of workflows on HDCS was demonstrated as an NP-Complete problem.In the current work,a new heuristic based Branch and Bound(BnB)technique namely Critical Path_finish Time First(CPTF)algorithm is proposed for workflow scheduling on HDCS to achieve the best solutions.The primary merits of CPTF algorithm are due to the bounding functions that are tight and of less complexity.The sharp bounding functions could precisely estimate the promise of each state and aid in pruning infeasible states.Thus,the search space size is reduced.The CPTF algorithm explores the most promising states in the search space and converges to the solution quickly.Therefore,high performance is achieved.The experimental results on random and scientific workflows reveal that CPTF algorithm could effectively exploit high potency of BnB technique in realizing better quality solutions against the widely referred heuristic scheduling algorithms.The results on the benchmark workflows show that CPTF algorithm has improved schedules for 89.36%of the cases.
