Multi-objective list scheduling of workflow applications in distributed computing infrastructures

Fard, Hamid Mohammadi; Prodan, Radu; Fahringer, Thomas

doi:10.1016/j.jpdc.2013.12.004

Cited by 56 publications

(28 citation statements)

References 22 publications

(34 reference statements)

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“…Fard et al [14] study the effectiveness of list scheduling in scientific workflow applications and propose a multi-objective list scheduling (MOLS) for scheduling scientific workflow applications in distributed computing infrastructures (DCIs). Instead of concentrating only on improving the makespan of the workflow application, their proposed approach aims to improve execution cost, energy consumption, makespan and reliability.…”

Section: Literature Reviewmentioning

confidence: 99%

Catfish-PSO based scheduling of scientific workflows in IaaS cloud

Nirmala

Bhanu

2016

Computing

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Cloud computing is a technology wherein a network of remote servers is used to process large amount of data in real-time. The servers and data sources may be located in geographically distant regions. Scheduling of workflows is one of the major challenging issues in cloud computing. Workflows are used to express a wide variety of applications including scientific computing and multi-tier web applications. The Workflow scheduling problem is known to be NP-complete. No known traditional scheduling algorithm is able to provide an optimal solution in polynomial time for NPcomplete problems. So, researchers rely on heuristics and meta-heuristics to achieve the most efficient solution. In this paper, a workflow scheduling algorithm is proposed to schedule large scientific workflows that are to be executed on IaaS clouds. The workflow scheduling algorithm generates a schedule with the task-to-resource mapping. The metaheuristic Catfish particle swarm optimization (C-PSO) technique is used to select the best schedule with the least makespan and execution cost. The performance of C-PSO is then compared with traditional PSO. The algorithm is simulated on the WorkFlowSim Simulator, an extension of CloudSim simulator. The solution is tested for different types of scientific workflows like Montage, Epigenome, CyberShake and Inspiral. It is observed from the experimental results that C-PSO gives better performance than traditional PSO in terms of execution cost and makespan.

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Section: Literature Reviewmentioning

confidence: 99%

Catfish-PSO based scheduling of scientific workflows in IaaS cloud

Nirmala

Bhanu

2016

Computing

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“…In this regard, the objective function i.e Q is defined as (16). Here, 0 ≤ v(G; M) ≤ 1 is the aggregated violation of deadline and budget as computed by (17). In this formula, max {|t L B (G) − t max | , |t U B (G) − t max |} illustrate the maximum possible distance between makespan and deadline and is used for normalization.…”

Section: Objective Functionmentioning

confidence: 99%

“…However, few studies have been done in the area of constrained workflow scheduling [13][14][15][16][17]. In this problem, the goal is to find scheduling such that the execution of workflow is completed within the user defined deadline and/or the cost of execution be within the predefined budget.…”

Section: Introductionmentioning

confidence: 99%

Ant colony based constrained workflow scheduling for heterogeneous computing systems

2016

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The problem of constrained workflow scheduling on heterogeneous computing systems has been of major interest in the recent years. The user requirements are described by defining constraints on the workflow makespan and/or its execution cost. The uncertainty in the activity execution path and the dynamicity in the resource workload may cause some run-time changes of the makespan or cost. To prohibit runtime constraint violation, the system needs robust schedules. In this paper, probability of violation (POV) of constraints is proposed as a criterion for the schedule robustness. An ant colony system is then used to minimize an aggregation of violation of constraints and the POV. Simulation results on real world workflows show the effectiveness of the proposed method in finding feasible schedules. The results also indicate that the proposed method decreases the POV, as well as the expected penalty at run-time.

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“…There has been much work on the workflow scheduling problem in heterogeneous computing environments [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22]. Heterogeneous Earliest-Finish-Time (HEFT) and Critical-Pathon-a-Processor (CPOP) [23] are two best-known listbased heuristics addressing the performance-effective workflow scheduling problem, which are widely used in popular workflow management tools.…”

Section: Introductionmentioning

confidence: 99%

Evolutionary Multi-Objective Workflow Scheduling in Cloud

Zhu

Zhang

et al. 2016

IEEE Trans. Parallel Distrib. Syst.

347

170

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Cloud computing provides promising platforms for executing large applications with enormous computational resources to offer on demand. In a Cloud model, users are charged based on their usage of resources and the required Quality of Service (QoS) specifications. Although there are many existing workflow scheduling algorithms in traditional distributed or heterogeneous computing environments, they have difficulties in being directly applied to the Cloud environments since Cloud differs from traditional heterogeneous environments by its service-based resource managing method and pay-per-use pricing strategies. In this paper, we highlight such difficulties, and model the workflow scheduling problem which optimizes both makespan and cost as a Multi-objective Optimization Problem (MOP) for the Cloud environments. We propose an Evolutionary Multi-objective Optimization (EMO)-based algorithm to solve this workflow scheduling problem on an Infrastructure as a Service (IaaS) platform. Novel schemes for problemspecific encoding and population initialization, fitness evaluation and genetic operators are proposed in this algorithm. Extensive experiments on real world workflows and randomly generated workflows show that the schedules produced by our evolutionary algorithm present more stability on most of the workflows with the instance-based IaaS computing and pricing models. The results also show that our algorithm can achieve significantly better solutions than existing state-of-the-art QoS optimization scheduling algorithms in most cases. The conducted experiments are based on the on-demand instance types of Amazon EC2; however, the proposed algorithm are easy to be extended to the resources and pricing models of other IaaS services.

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Multi-objective list scheduling of workflow applications in distributed computing infrastructures

Cited by 56 publications

References 22 publications

Catfish-PSO based scheduling of scientific workflows in IaaS cloud

Catfish-PSO based scheduling of scientific workflows in IaaS cloud

Ant colony based constrained workflow scheduling for heterogeneous computing systems

Evolutionary Multi-Objective Workflow Scheduling in Cloud

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