Abstract
As one of the latest market-oriented resource provisioning paradigms, cloud computing has been widely adopted by a growing number of consumers due to its powerful computing ability and storage ability. Although cloud computing can achieve effective cost reduction and convenience enhancement in the development of large-scale applications, it results in a complex cost optimization problem for data-dependent tasks represented by a workflow. All tasks in a workflow should be scheduled according to a proper strategy such that the cost is minimized and the precedence constraints and timing requirements are satisfied. In this paper, we study the cost optimization problem of deadline constrained workflows on cloud computing, and propose two list scheduling algorithms named Look-back Workflow Scheduling (LBWS) and Structure Aware Workflow Scheduling (SAWS) to solve the problem. LBWS distributes the deadline over the workflow as sub-deadlines to tasks in different levels, and schedules the tasks according to their priorities to the resources which meet their sub-deadlines and the best time-cost trade off requirements. Compared with LBWS, SAWS considers tasks allocated to the same level at a time and provisions resources with minimum cost to these tasks. Experiments on scientific workflow applications with different data and computational characteristics are conducted to show that, the proposed approaches can achieve better performance in terms of success rate and monetary cost.
Highlights
Cloud computing, benefited from the availability of powerful computers and high-speed networks, has realized the long-held dream in delivering resources at levels of infrastructure, platform, and software [1]
Wang et al [3] proposed Deadline-constrained Probabilistic List Scheduling (ProLiS) algorithm which distributed workflow deadlines based on probabilistic upward rank and set virtual machine (VM) to faster levels when the finish time of the newly scheduled task is beyond its sub-deadline
The results obtained for the LIGO workflow is quite similar to Montage, except that the success rate of ProLiS and Look-back Workflow Scheduling (LBWS) is less than 15 percent when β is 1.0 and IC-Partial Critical Path (PCP) misses a tiny fraction of the other deadlines
Summary
Cloud computing, benefited from the availability of powerful computers and high-speed networks, has realized the long-held dream in delivering resources (e.g. server, network, storage) at levels of infrastructure, platform, and software [1]. The cost optimization problem of workflows tries to obtain the minimal cost while preserving the dependencies and satisfying the Quality-of-Service (QoS) requirements of tasks. Since grids and clusters usually provide definite resource capacities in a best-effort and free of charge manner, traditional workflow scheduling algorithms mainly concentrate on optimizing the performance of applications in terms of task execution time. We study the cost optimization problem of deadline constrained workflows on cloud computing, and propose two list scheduling algorithms LBWS and SAWS to solve the problem. Based on sub-deadlines, we present two list scheduling algorithms LBWS and SAWS to solve the workflow optimization problem. The algorithms proposed can VOLUME 8, 2020 work in different situations, and do not require any iteration They are effective and can guide the development of practical applications on cloud computing platforms.
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