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A hybrid multi-objective Particle Swarm Optimization for scientific workflow scheduling

机译:用于科学工作流调度的混合多目标粒子群优化算法

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Now-a-days, Cloud computing is a technology which eludes provision cost while providing scalability and elasticity to accessible resources on a pay-per-use basis. To satisfy the increasing demand of the computing power to execute large scale scientific workflow applications, workflow scheduling is the main challenging issue in Infrastructure-as-a-Service (IaaS) clouds. As workflow scheduling belongs to NP-complete problem, so, meta-heuristic approaches are more preferred option. Users often specified deadline and budget constraint for scheduling these workflow applications over cloud resources. But these constraints are in conflict with each other, i.e., the cheaper resources are slow as compared to the expensive resources. Most of the existing studies try to optimize only one of the objectives, i.e., either time minimization or cost minimization under user specified Quality of Service (QoS) constraints. But due to the complexity of workflows and dynamic nature of cloud, a trade-off solution is required to make a balance between execution time and processing cost. To address these issues, this paper presents a non-dominance sort based Hybrid Particle Swarm Optimization (HPSO) algorithm to handle the workflow scheduling problem with multiple conflicting objective functions on IaaS clouds. The proposed algorithm is a hybrid of our previously proposed Budget and Deadline constrained Heterogeneous Earliest Finish Time (BDHEFT) algorithm and multi-objective PSO. The HPSO heuristic tries to optimize two conflicting objectives, namely, makespan and cost under the deadline and budget constraints. Along with these two conflicting objectives, energy consumed of created workflow schedule is also minimized. The proposed algorithm gives a set of Pareto Optimal solutions from which the user can choose the best solution. The performance of proposed heuristic is compared with state-of-art multi-objective meta-heuristics like NSGA-II, MOPSO, and epsilon-FDPSO. The simulation analysis substantiates that the solutions obtained with proposed heuristic deliver better convergence and uniform spacing among the solutions as compared to others. Hence it is applicable to solve a wide class of multi objective optimization problems for scheduling scientific workflows over laaS clouds. (C) 2017 Elsevier B.V. All rights reserved.
机译:如今,云计算是一种技术,它不计成本,同时按使用付费为可访问资源提供可伸缩性和弹性。为了满足执行大型科学工作流程应用程序对计算能力不断增长的需求,工作流程调度是基础架构即服务(IaaS)云中的主要挑战性问题。由于工作流调度属于NP完全问题,因此,元启发式方法是更可取的选择。用户通常指定期限和预算约束,以在云资源上调度这些工作流应用程序。但是这些约束相互冲突,即,与昂贵的资源相比,便宜的资源慢。现有的大多数研究都试图仅优化一个目标,即在用户指定的服务质量(QoS)约束下将时间最小化或将成本最小化。但是由于工作流程的复杂性和云的动态性质,需要权衡解决方案才能在执行时间和处理成本之间取得平衡。为了解决这些问题,本文提出了一种基于非优势排序的混合粒子群优化(HPSO)算法,以处理IaaS云上具有多个相互冲突的目标函数的工作流调度问题。提出的算法是我们先前提出的“预算和截止日期”约束的异构最早完成时间(BDHEFT)算法与多目标PSO的混合体。 HPSO试探法试图优化两个相互矛盾的目标,即在期限和预算约束下的制造期和成本。除了这两个相互矛盾的目标外,创建工作流程时间表所消耗的能源也被最小化。所提出的算法给出了一组帕累托最优解,用户可以从中选择最佳解。将拟议的启发式算法的性能与最新的多目标元启发式算法(如NSGA-II,MOPSO和epsilon-FDPSO)进行比较。仿真分析证实,与其他解决方案相比,通过提议的启发式方法获得的解决方案具有更好的收敛性和解决方案之间的均匀间距。因此,它适用于解决一类多目标优化问题,以便在laaS云上调度科学工作流。 (C)2017 Elsevier B.V.保留所有权利。

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