MultiObjective Optimization to Workflow Grid Scheduling using Reference Point based Evolutionary Algorithm

Garg, Ritu; Singh, Awadhesh Kumar

doi:10.5120/2584-3570

Cited by 11 publications

(8 citation statements)

References 19 publications

(14 reference statements)

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“…An MOEA approach produces Pareto optimal set of solutions, which is the set consisted of all non-dominated solutions. In [24], the authors considered three conflicting objectives like execution time (makespan), total cost and reliability and proposed a multi-objective scheduling algorithm, using R-NSGA-II approach based on evolutionary computing paradigm that generates multiple scheduling solutions near the Pareto optimal front with small computation overhead. A multi-objective list scheduling (MOLS) algorithm [25] was proposed to find a dominant solution by using Pareto relation for heterogeneous environment.…”

Section: Related Workmentioning

confidence: 99%

Cost-Time Efficient Scheduling Plan for Executing Workflows in the Cloud

Verma

Kaushal

2015

J Grid Computing

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The emergence of Cloud Computing as a model of service provisioning in distributed systems instigated researchers to explore its pros and cons on executing different large scale scientific applications, i.e., Workflows. One of the most challenging problems in clouds is to execute workflows while minimizing the execution time as well as cost incurred by using a set of heterogeneous resources over the cloud simultaneously. In this paper, we present, Budget and Deadline Constrained Heuristic based upon Heterogeneous Earliest Finish Time (HEFT) to schedule workflow tasks over the available cloud resources. The proposed heuristic presents a beneficial trade-off between execution time and execution cost under given constraints. The proposed heuristic is evaluated for different synthetic workflow applications by a simulation process and comparison is done with state-of-art algorithm i.e. BHEFT. The simulation results show that our proposed scheduling heuristic can significantly decrease the execution cost while producing makespan as good as the best known scheduling heuristic under the same deadline and budget constraints.A. Verma ( ) · S. Kaushal

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Section: Related Workmentioning

confidence: 99%

Cost-Time Efficient Scheduling Plan for Executing Workflows in the Cloud

Verma

Kaushal

2015

J Grid Computing

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“…In [21], the authors proposed a solution based on the NSGA-II algorithm which aims to find a tradeoff between makespan and processing cost. In [16], the authors propose a multi-objective scheduling algorithm based on NSGA-II which deals with three conflicting objectives such as makepan, processing cost and reliability. In [15], the authors developed a multi-objective optimization system based on NSGA-II that aims to minimize three perspectives such as makespan, virtual machine deployment cost, and virtual machine failure for scientific workflow in a cloud environment.…”

Section: Literature Reviewmentioning

confidence: 99%

Multi-objective Scientific Workflows Scheduling in An Energy-aware Cloud Environment

Dahmani

Aziza

Ben-Romdhane

et al. 2023

Preprint

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The cloud has become an essential element for businesses globally, providing constant data availability while improving data center energy efficiency and reducing carbon footprints. However, data centers' energy consumption remains high, and energy reduction has become a crucial objective. In this paper, we aim to address this concern by proposing a multi-objective scientific workflow scheduling scheme on heterogeneous computing systems using a hybrid genetic algorithm approach. Our algorithm incorporates Hill Climbing to generate an initial population of chromosomes, which we further optimize using a genetic algorithm to assign each task to the most suitable virtual machine. The proposed fitness function is carefully designed to evaluate each chromosome's fitness to solve the scheduling problem. The algorithm's parameters and operators significantly affect the solution's performance, and our results show that the proposed algorithm outperforms other scheduling methods in terms of quality, reducing energy consumption, processing time, and cost. We believe that our proposed approach can play a crucial role in reducing the energy consumption and carbon footprint of cloud data centers and provide a green solution for scientific workflow scheduling.

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“…Also there are versions of (P)GA used for multi-objective optimizations in IT systems [15][16][17][18].…”

Section: Genetic Algorithm (Ga)mentioning

confidence: 99%

Multi-objective Optimizations during Parallel Processing in a Dynamic Heterogeneous Cloud Environment

Kjamilji

2014

2014 Sixth International Conference on Computational Intelligence, Communication Systems and Networks

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In the quest of fulfilling the rapidly increasing needs for processing power, while avoiding the expensive supercomputers, scientists look for cheaper parallel processing alternatives and technologies such as those found on GRID, clusters and similar ones. With the emergence of cloud, it was a matter of time when this will be done on cloud as well. However, cloud has its unique nature. On one side, its servers are hybrid in terms of their SOA as well as the maximum guaranteed processing power they offer. On the other side the Internet (the medium by which the cloud services are offered by default) is often unpredictable and dynamic. Furthermore, during our parallel processing, simultaneously we want to optimize the time and price, goals often in contradiction to each other. Three algorithms are studied and compared with the optimal solution, including our proposed cloud adopted feedback algorithm, which outperformed the others.

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MultiObjective Optimization to Workflow Grid Scheduling using Reference Point based Evolutionary Algorithm

Cited by 11 publications

References 19 publications

Cost-Time Efficient Scheduling Plan for Executing Workflows in the Cloud

Cost-Time Efficient Scheduling Plan for Executing Workflows in the Cloud

Multi-objective Scientific Workflows Scheduling in An Energy-aware Cloud Environment

Multi-objective Optimizations during Parallel Processing in a Dynamic Heterogeneous Cloud Environment

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