MOHEFT: A multi-objective list-based method for workflow scheduling

Durillo, Juan J.; Fard, Hamid Mohammadi; Prodan, Radu

doi:10.1109/cloudcom.2012.6427573

Cited by 89 publications

(62 citation statements)

References 9 publications

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“…This approach requires some preferential order of the objectives for the optimization, but the specific constraint value may not be known a priori until first schedule is computed. This approach is also not suitable for all those cases where a large number of objectives are to be optimized [21,23,24].…”

Section: Literature Reviewmentioning

confidence: 95%

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An energy-efficient reliable grid scheduling model using NSGA-II

Kaushik

Vidyarthi

2015

Engineering with Computers

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parameters by effectively managing the available grid resources. In general, it is unusual to assume a single criterion for the decision making in a grid system which is governed by various related factors. The scheduling problem in the grid system may have several conflicting objectives, e.g., makespan, reliability, energy consumption, cost, security, etc., which need to be optimized simultaneously [1][2][3][4].The outcome values of different objectives, viz. execution time of any job, the energy consumption by the job and the reliability offered by the system to the job may vary from one allocation to another [5]. The distributed nature of grid resources and the dynamic nature of the grid workload make the makespan of the job unpredictable. For makespan minimization, it is warranted to effectively deal with the load balancing as well [6]. Further, the heterogeneous hardware components of the grid are more prone to failure and therefore reliability of such systems decreases with the increase in the number of geographically distributed resources [7]. The heterogeneity of the system demands varying energy consumption based on their hardware profile [8]. It necessitates choosing those nodes/machines that are able to execute the job quickly, reliably and with least energy consumption.Since these objectives are conflicting in nature, any approach adopted to optimize one objective may not result in the overall optimized solution with respect to other objectives. All the conflicting objectives need to be addressed simultaneously and the best way for this is to have the trade-off solutions with all the objectives [9, 10]. A good solution corresponding to all the objectives is desired. This solution may not offer the best of all the objectives, but certainly better when seen as a trade-off among the objectives. This approach is referred as multiobjective optimization technique [5]. As such, job scheduling in the grid is an NP-hard problem and traditional Abstract Job scheduling in computational grid is a complex problem and various heuristics and meta-heuristics have been proposed for the same. These approaches usually optimize specific characteristic parameters while allocating the jobs on the grid resources. Many a times, it is desired to optimize multiple parameters during job scheduling. Nondominated sorting genetic algorithm (NSGA-II) has been observed to be the best meta-heuristic to solve such multiobjective optimization problem. The proposed work applies NSGA-II for job scheduling in computational grid with three conflicting objectives: maximizing reliability of the system for job allocation, minimizing energy consumption and balancing the load on the system. Performance study of the proposed model is done by simulating it on some real data. The result indicates that the proposed model performs well with multiple objectives.

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Section: Literature Reviewmentioning

confidence: 95%

“…In weighted sum approach, objectives are assigned to weight parameter which is used to evaluate the fitness of each solution. This approach is not suitable in most of the cases as it incurs huge computational time and also due to its inherent limitation of non-uniform spread of points on the Pareto curve [8,23].…”

Section: Literature Reviewmentioning

confidence: 98%

An energy-efficient reliable grid scheduling model using NSGA-II

Kaushik

Vidyarthi

2015

Engineering with Computers

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“…More recently, several approaches computing the whole set of tradeoff solutions emerged grouped in two main lines: (1) genetic algorithms-based techniques for optimizing makespan and cost [23], makespan and energy consumption [13], or makespan, cost and reliability [18]; and (2) list-based heuristics for optimizing makespan and cost or makespan [5,6] and energy consumption [7]. Only few of these works targeted workflow scheduling on Clouds and none of them considers Cloud federations.…”

Section: Related Workmentioning

confidence: 99%

“…We employ a method for computing the tradeoff solutions as an extension of the MOHEFT [6] algorithm customised for dealing with the characteristics of federated Cloud environments. MOHEFT is an instance of a class of multiobjective greedy algorithms as defined in [5], based on extending the HEFT [19] algorithm to consider multiple simultaneous criteria.…”

Section: Financial Costmentioning

confidence: 99%

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Workflow Scheduling on Federated Clouds

Durillo

Prodan

2014

Lecture Notes in Computer Science

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Abstract. Federated Clouds, or the orchestration of multiple Cloud services for fulfilling applications' requirements, is receiving increasing attention. Despite their many advantages, federated Clouds also present some downsides since different services may reside in different geographically located areas. This paper focuses on evaluating the advantages and disadvantages, from the point of view of performance and financial costs, of using a federation of Clouds for executing scientific workflows. It evaluates a wide range of different workflow types with different requirements in terms of computation and communication (produced and consumed data), and discusses which kind of workflow applications can benefit from a Cloud federation and how.

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Efficient algorithm for scheduling parallel applications on hybrid multicore machines with communications delays and energy constraint

Ait

Zaourar

Munier

2020

Concurrency and Computation

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SummaryThis paper presents an efficient algorithm with performance guarantee to solve task scheduling problem on hybrid platforms with energy constraint and communication delays. The underlying platform architecture in this work is composed of two types of resources, CPU and GPU, often called hybrid parallel multicore platforms. We focus on finding a generic approach to schedule applications presented by Directed Acyclic Graph (DAG), which minimizes the makespan by considering communication delays and respecting an energy constraint. A two‐phase algorithm is proposed with a performance guarantee of 6 compared with the optimal solution; the first phase consists in solving the assignment problem to find the type of processor assigned to execute the tasks (CPU or GPU) using a linear program. In the second phase, we calculate the start execution time of each task to generate a feasible schedule. Finally, we test our algorithm on a large number of instances. These tests demonstrate that the proposed algorithm achieves a close‐to‐optimal performance.

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MOHEFT: A multi-objective list-based method for workflow scheduling

Cited by 89 publications

References 9 publications

An energy-efficient reliable grid scheduling model using NSGA-II

An energy-efficient reliable grid scheduling model using NSGA-II

Workflow Scheduling on Federated Clouds

Efficient algorithm for scheduling parallel applications on hybrid multicore machines with communications delays and energy constraint

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