Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/209964
Title: Deadline-Constrained Cost Optimization Approaches for Workflow Scheduling in Clouds
Authors: Quanwang Wu;Fuyuki Ishikawa;Qingsheng Zhu;Yunni Xia;Junhao Wen
Year: 2017
Publisher: IEEE
Abstract: Nowadays it is becoming more and more attractive to execute workflow applications in the cloud because it enables workflow applications to use computing resources on demand. Meanwhile, it also challenges traditional workflow scheduling algorithms that only concentrate on optimizing the execution time. This paper investigates how to minimize execution cost of a workflow in clouds under a deadline constraint and proposes a metaheuristic algorithm L-ACO as well as a simple heuristic ProLiS. ProLiS distributes the deadline to each task, proportionally to a novel definition of probabilistic upward rank, and follows a two-step list scheduling methodology: rank tasks and sequentially allocates each task a service which meets the sub-deadline and minimizes the cost. L-ACO employs ant colony optimization to carry out deadline-constrained cost optimization: the ant constructs an ordered task list according to the pheromone trail and probabilistic upward rank, and uses the same deadline distribution and service selection methods as ProLiS to build solutions. Moreover, the deadline is relaxed to guide the search of L-ACO towards constrained optimization. Experimental results show that compared with traditional algorithms, the performance of ProLiS is very competitive and L-ACO performs the best in terms of execution costs and success ratios of meeting deadlines.
URI: http://localhost/handle/Hannan/209964
volume: 28
issue: 12
More Information: 3401,
3412
Appears in Collections:2017

Files in This Item:
File SizeFormat 
8000634.pdf979.98 kBAdobe PDF
Title: Deadline-Constrained Cost Optimization Approaches for Workflow Scheduling in Clouds
Authors: Quanwang Wu;Fuyuki Ishikawa;Qingsheng Zhu;Yunni Xia;Junhao Wen
Year: 2017
Publisher: IEEE
Abstract: Nowadays it is becoming more and more attractive to execute workflow applications in the cloud because it enables workflow applications to use computing resources on demand. Meanwhile, it also challenges traditional workflow scheduling algorithms that only concentrate on optimizing the execution time. This paper investigates how to minimize execution cost of a workflow in clouds under a deadline constraint and proposes a metaheuristic algorithm L-ACO as well as a simple heuristic ProLiS. ProLiS distributes the deadline to each task, proportionally to a novel definition of probabilistic upward rank, and follows a two-step list scheduling methodology: rank tasks and sequentially allocates each task a service which meets the sub-deadline and minimizes the cost. L-ACO employs ant colony optimization to carry out deadline-constrained cost optimization: the ant constructs an ordered task list according to the pheromone trail and probabilistic upward rank, and uses the same deadline distribution and service selection methods as ProLiS to build solutions. Moreover, the deadline is relaxed to guide the search of L-ACO towards constrained optimization. Experimental results show that compared with traditional algorithms, the performance of ProLiS is very competitive and L-ACO performs the best in terms of execution costs and success ratios of meeting deadlines.
URI: http://localhost/handle/Hannan/209964
volume: 28
issue: 12
More Information: 3401,
3412
Appears in Collections:2017

Files in This Item:
File SizeFormat 
8000634.pdf979.98 kBAdobe PDF
Title: Deadline-Constrained Cost Optimization Approaches for Workflow Scheduling in Clouds
Authors: Quanwang Wu;Fuyuki Ishikawa;Qingsheng Zhu;Yunni Xia;Junhao Wen
Year: 2017
Publisher: IEEE
Abstract: Nowadays it is becoming more and more attractive to execute workflow applications in the cloud because it enables workflow applications to use computing resources on demand. Meanwhile, it also challenges traditional workflow scheduling algorithms that only concentrate on optimizing the execution time. This paper investigates how to minimize execution cost of a workflow in clouds under a deadline constraint and proposes a metaheuristic algorithm L-ACO as well as a simple heuristic ProLiS. ProLiS distributes the deadline to each task, proportionally to a novel definition of probabilistic upward rank, and follows a two-step list scheduling methodology: rank tasks and sequentially allocates each task a service which meets the sub-deadline and minimizes the cost. L-ACO employs ant colony optimization to carry out deadline-constrained cost optimization: the ant constructs an ordered task list according to the pheromone trail and probabilistic upward rank, and uses the same deadline distribution and service selection methods as ProLiS to build solutions. Moreover, the deadline is relaxed to guide the search of L-ACO towards constrained optimization. Experimental results show that compared with traditional algorithms, the performance of ProLiS is very competitive and L-ACO performs the best in terms of execution costs and success ratios of meeting deadlines.
URI: http://localhost/handle/Hannan/209964
volume: 28
issue: 12
More Information: 3401,
3412
Appears in Collections:2017

Files in This Item:
File SizeFormat 
8000634.pdf979.98 kBAdobe PDF