A Particle Swarm Optimization-Based Heuristic for Scheduling Workflow Applications in Cloud Computing Environments

Pandey, Suraj; Wu, Linlin; Guru, Siddeswara Mayura; Buyya, Rajkumar

doi:10.1109/aina.2010.31

Cited by 706 publications

(446 citation statements)

References 20 publications

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“…As shown in (Frey et al, ) the cloud computing domain is an appropriate real-world case study where MOEAs are used for scheduling and scaling tasks. Scheduling applications on the cloud is a non trivial task (Pandey et al, 2010). First of all, several different cloud computing providers exist on the market.…”

Section: Real-world Case Studymentioning

confidence: 99%

Polymer - A Model-driven Approach for Simpler, Safer, and Evolutive Multi-objective Optimization Development

Moawad

Hartmann

Fouquet

et al. 2015

Proceedings of the 3rd International Conference on Model-Driven Engineering and Software Development

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Abstract:Multi-Objective Evolutionary Algorithms (MOEAs) have been successfully used to optimize various domains such as finance, science, engineering, logistics and software engineering. Nevertheless, MOEAs are still very complex to apply and require detailed knowledge about problem encoding and mutation operators to obtain an effective implementation. Software engineering paradigms such as domain-driven design aim to tackle this complexity by allowing domain experts to focus on domain logic over technical details. Similarly, in order to handle MOEA complexity, we propose an approach, using model-driven software engineering (MDE) techniques, to define fitness functions and mutation operators without MOEA encoding knowledge.Integrated into an open source modelling framework, our approach can significantly simplify development and maintenance of multi-objective optimizations. By leveraging modeling methods, our approach allows reusable optimizations and seamlessly connects MOEA and MDE paradigms. We evaluate our approach on a cloud case study and show its suitability in terms of i) complexity to implement an MOO problem, ii) complexity to adapt (maintain) this implementation caused by changes in the domain model and/or optimization goals, and iii) show that the efficiency and effectiveness of our approach remains comparable to ad-hoc implementations.

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Section: Real-world Case Studymentioning

confidence: 99%

Polymer - A Model-driven Approach for Simpler, Safer, and Evolutive Multi-objective Optimization Development

Moawad

Hartmann

Fouquet

et al. 2015

Proceedings of the 3rd International Conference on Model-Driven Engineering and Software Development

View full text Add to dashboard Cite

show abstract

“…Pandey, Wu, Guru and Buyya (2010) [17] have used PSObased heuristic for workflow scheduling in cloud environment, which considers not only execution cost but also the cost for transmitting dependent data. Netjinda, Sirinaovakul and Achalakul (2012) [18] used PSO technique for cost optimization by converting real data in the particles into integral representation of result, showing a potential performance in both the viewpoint of the total cost and convergence and also yielding various alternatives in procuring on changes in usage behavior.…”

Section: Initialization Of Populationmentioning

confidence: 99%

An Analysis of Task Scheduling in Cloud Computing using Evolutionary and Swarm-based Algorithms

Bilgaiyan¹,

Sagnika²,

Das³

2014

IJCA

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Cloud computing is a popular computing paradigm that performs processing of huge volumes of data using highly available geographically distributed resources that can be accessed by users on the basis of Pay As per Use policy. In the modern computing environment where the amount of data to be processed is increasing day by day, the costs involved in the transmission and execution of such amount of data is mounting significantly. So there is a requirement of appropriate scheduling of tasks which will help to manage the escalating costs of data intensive applications. This paper analyzes various evolutionary and swarm based task scheduling algorithms that address the above mentioned problem.

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“…In the studies of [10], VMware based load balancing approach has been offered in order to generate the ants at hop level whenever required, and concurrently the mobile agents memorize every visited node and record their whole information for future reference. A Particle Swarm Optimization (PSO) technique for cloud computing has been developed in [11] which is being inspired by the movement of birds in flocks or fishes in school. In PSO search network, each solution is considered as "particle".…”

Section: Related Workmentioning

confidence: 99%

Performance Analysis of Load Balancing Algorithms in Cloud Computing

Kumar¹,

Prashar²

2015

IJCA

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Cloud computing is a business oriented IT-technology, which is composed of multiple computing technologies accessed via internet. With the rapid increase in cloud usage, it becomes a challenge to deliver the cloud services effectively and efficiently to the cloud consumers on the pay-per usage basis. In this concern Balancing of load has become one of the essential components for the cloud computing environment to perform the effective operations. Scheduling of virtual machines or data centers has to be done properly by using an appropriate load balancing technique. Hence, several algorithms have been developed to process the client's request towards the cloud nodes.. In this present work, a hybridized swarm intelligence technique is proposed to evenly distribute the incoming task requests among the virtual machines or server. Additionally, the performance analysis has been performed using the CloudAnalyst simulator. This paper gives a comprehensive performance analysis of the proposed approach and compares its results with existing Round Robin (RR), Equally Spread Current Execution (ESCE) and ant colony optimization (ACO) techniques. Simulation results have demonstrated that the proposed technique shows a significant outcome in terms of response time, data center processing time and total cost in cloud computing.

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A Particle Swarm Optimization-Based Heuristic for Scheduling Workflow Applications in Cloud Computing Environments

Cited by 706 publications

References 20 publications

Polymer - A Model-driven Approach for Simpler, Safer, and Evolutive Multi-objective Optimization Development

Polymer - A Model-driven Approach for Simpler, Safer, and Evolutive Multi-objective Optimization Development

An Analysis of Task Scheduling in Cloud Computing using Evolutionary and Swarm-based Algorithms

Performance Analysis of Load Balancing Algorithms in Cloud Computing

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