An ACO-LB Algorithm for Task Scheduling in the Cloud Environment

Xue, Shengjun; Li, Mengying; Xu, Xiaolong; Chen, Jingyi

doi:10.4304/jsw.9.2.466-473

Cited by 43 publications

(14 citation statements)

References 20 publications

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“…This attribute of metaheuristic algorithms make them perform better than mathematical and heuristic techniques. Some of the popular metaheuristic methods for solving task scheduling problems in cloud computing environment are Genetic Algorithm (GA) [ 13 ], Particle Swarm Optimization (PSO) [ 14 ], Ant Colony Optimization (ACO) [ 15 , 16 ], League Championship Algorithm (LCA) [ 11 ], BAT algorithm [ 17 , 18 ], Symbiotic Organisms Search (SOS) [ 10 ]. The idea of SOS as a metaheuristic algorithm was introduced in [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Symbiotic Organisms Search Optimization Algorithm for Scheduling of Tasks on Cloud Computing Environment

Mohammed

Ngadi

2016

PLoS ONE

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Cloud computing has attracted significant attention from research community because of rapid migration rate of Information Technology services to its domain. Advances in virtualization technology has made cloud computing very popular as a result of easier deployment of application services. Tasks are submitted to cloud datacenters to be processed on pay as you go fashion. Task scheduling is one the significant research challenges in cloud computing environment. The current formulation of task scheduling problems has been shown to be NP-complete, hence finding the exact solution especially for large problem sizes is intractable. The heterogeneous and dynamic feature of cloud resources makes optimum task scheduling non-trivial. Therefore, efficient task scheduling algorithms are required for optimum resource utilization. Symbiotic Organisms Search (SOS) has been shown to perform competitively with Particle Swarm Optimization (PSO). The aim of this study is to optimize task scheduling in cloud computing environment based on a proposed Simulated Annealing (SA) based SOS (SASOS) in order to improve the convergence rate and quality of solution of SOS. The SOS algorithm has a strong global exploration capability and uses fewer parameters. The systematic reasoning ability of SA is employed to find better solutions on local solution regions, hence, adding exploration ability to SOS. Also, a fitness function is proposed which takes into account the utilization level of virtual machines (VMs) which reduced makespan and degree of imbalance among VMs. CloudSim toolkit was used to evaluate the efficiency of the proposed method using both synthetic and standard workload. Results of simulation showed that hybrid SOS performs better than SOS in terms of convergence speed, response time, degree of imbalance, and makespan.

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Section: Introductionmentioning

confidence: 99%

Hybrid Symbiotic Organisms Search Optimization Algorithm for Scheduling of Tasks on Cloud Computing Environment

Mohammed

Ngadi

2016

PLoS ONE

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“…A series of experimental results show that ant colony algorithm has strong ability to find good solution and it is not easy to fall into local optimum, because the algorithm takes advantage of the positive feedback principle to accelerate the evolution process to a certain extent, and this is a kind of parallel algorithm in nature to find a better solution by ongoing communication and transmission among individuals. Literatures [13][14][15] began to study the application of cloud computing based on the ACO algorithm.…”

Section: Figure 1 the Architecture Of Atn On Sla In Cloud Environmentmentioning

confidence: 99%

A Priority-driven ACO Algorithm for DAG Task Scheduling in Cloud Environment

Xie¹,

Chen²,

Wang³

2015

IJHIT

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Task scheduling in cloud environment is a key technical problem on how to allocate available cloud resources to cloud users. Usually, a task in cloud environment can be cut into a serials of subtasks, which have precedence and dependency relationships among themselves. We describe the problem by means of a DAG (Directed Acyclic Graph) model. Then, we propose a priority algorithm for DAG task scheduling, and a priority-driven ACO (Ant Colony Optimization) algorithm for DAG task scheduling on the basis of the DAG model. Finally, we compare these two algorithms with the greedy algorithm through simulation in the CloudSim platform. The simulation results show that the priority-driven ACO algorithm is effective to solve DAG task scheduling problem in cloud environment.

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“…Cloud computing is an on-demand resource for networks, servers, programs and services with high speed and little effort in interaction of users. One of the recent challenges in a computing platform is TS [3]. The general issue in TS is NP-hard optimization problems like travelling salesman problem and combinatorial problems like integer programming and addressing problems.…”

Section: Introductionmentioning

confidence: 99%

Multi Objective Task Scheduling Using Modified Ant Colony Optimization in Cloud Computing

Reddy¹,

Phanikumar²

2018

IJIES

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Abstract:Cloud computing is the development of distributed computing, parallel computing, and grid computing, or defined as a commercial implementation of such computer science concepts. One of the main issues in a cloud computing environment is Task scheduling (TS). In Cloud task scheduling, many Non deterministic Polynomial time-hard optimization problem, and many meta-heuristic (MH) algorithms have been proposed to solve it. A task scheduler should adapt its scheduling strategy to changing environment and variable tasks. This paper amends a cloud task scheduling policy based on Modified Ant Colony Optimization (MACO) algorithm. The main contribution of recommended method is to minimize makespan and to perform Multi Objective Task Scheduling (MOTS) process by assigning pheromone amount relative to corresponding virtual machine efficiency. MACO algorithm improves the performance of task scheduling by reducing makespan and degree of imbalance comparatively lower than a basic ACO algorithm by its multi-objective and deliberate nature. Experimental outcomes have shown that proposed MACO to have makespan 350 milliseconds and average utilization of 0.51 for a set of 100 tasks.

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An ACO-LB Algorithm for Task Scheduling in the Cloud Environment

Cited by 43 publications

References 20 publications

Hybrid Symbiotic Organisms Search Optimization Algorithm for Scheduling of Tasks on Cloud Computing Environment

Hybrid Symbiotic Organisms Search Optimization Algorithm for Scheduling of Tasks on Cloud Computing Environment

A Priority-driven ACO Algorithm for DAG Task Scheduling in Cloud Environment

Multi Objective Task Scheduling Using Modified Ant Colony Optimization in Cloud Computing

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