Task Scheduling in the Cloud Computing Based on the Cuckoo Search Algorithm

Navimipour, Nima Jafari; Milani, Farnaz Sharifi

doi:10.7763/ijmo.2015.v5.434

Cited by 92 publications

(39 citation statements)

References 29 publications

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“…The authors claim that it uses two constraints in order to select the most suitable VM and guarantee the load balancing of the system. An evolutionary algorithm for scheduling tasks in Cloud computing is proposed in (Navimipour and Milani, 2015). It is based on the obligate brood parasitic behavior of some cuckoo species in combination with the Lévy flight behavior of some birds and fruit flies and focuses on minimizing the total waiting time of tasks.…”

Section: Related Workmentioning

confidence: 99%

Application of Viral System Algorithm in Load Balancing of Cloud Environment

Tiwari¹,

Singh²,

Sharma³

2018

Journal of Computer Science

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Abstract:As the cloud computing technology is gaining popularity with time, more and more users and applications are shifting towards it. This is why clouds are experiencing high load, which demands for load balancing of user tasks submitted to cloud for execution. This makes load balancing of non-preemptive tasks a key issue in cloud computing. Superior task scheduling leads to balanced loads among cloud nodes, which results in faster execution of tasks. Task scheduling in cloud environment is an instance of NP-hard optimization problem. When few nodes in a cloud are overloaded whereas other nodes are under loaded then in such situation the performance of overloaded VMs is diminished. It demands a task scheduling so that the incoming tasks can be distributed uniformly across virtual machines (VMs) for proper utilization of available resources. In this study, we propose a novel load balancing algorithm named Viral System Based Load Balancing (VSB-LB) algorithm, which is based on bio-inspired viral system algorithm that distributes the tasks uniformly among VMs. The proposed algorithm is compared with basic load balancing algorithms such as First Come First Serve (FCFS), Weighted Round Robin (WRR) as well as newer bio-inspired Load balance Aware Genetic Algorithm (LAGA) to show its effectiveness. Simulation results proved that VSBLB outperforms FCFS and WRR and LAGA for performing load balancing.

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Section: Related Workmentioning

confidence: 99%

Application of Viral System Algorithm in Load Balancing of Cloud Environment

Tiwari¹,

Singh²,

Sharma³

2018

Journal of Computer Science

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“…how well it adapts to its environment. For a maximization problem, the fitness of a solution can be proportional to the value of the objective function [21]. We uses three terms including total execution time (Ttime), average of waiting time (Wtime) and numbers of missed tasks because of their deadline (Missed) for designing fitness function as follow:…”

Section: A Solution Representationmentioning

confidence: 99%

“…The scheduling of a task workflow in a distributed computing platform is a well-known NP-hard problem [21]. The problem is even more complex and challenging when the virtualized clusters are used to execute a large number of tasks in a Cloud computing platform [21,22]. For this reason, many heuristics have been proposed, from low level execution of tasks in multiple processors to high level execution of tasks in Grid and Cloud environments [23].…”

Section: Introductionmentioning

confidence: 99%

“…For this reason, many heuristics have been proposed, from low level execution of tasks in multiple processors to high level execution of tasks in Grid and Cloud environments [23]. Recently many papers are published which used evolutionary algorithms like genetic, ant colony, bee colony and PSO (Particle Swarm Optimization) for optimization problems [21]. Because of the success of the PSO algorithms, in this paper we propose a new method based on the PSO algorithm for task scheduling in Cloud computing.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multi-Objective Task Scheduling in the Cloud Computing based on the Patrice Swarm Optimization

Milani¹,

Navin²

2015

IJITCS

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Abstract-Cloud computing is the latest emerging trend in distributed computing, where shared resources are provided to end-users in an on demand fashion that brings many advantages, including data ubiquity, flexibility of access, high availability of resources, and flexibility. In this type of systems many challenges are existed that the task scheduling problem is one of them. The task scheduling problem in Cloud computing is an NP-hard problem. Therefore, many heuristics have been proposed, from low level execution of tasks in multiple processors to high level execution of tasks. In this paper, we propose a new algorithm based on PSO to schedule the tasks in the Cloud. The results demonstrated that the proposed algorithm has a better operation in terms of task execution time, waiting time and missed tasks in comparison of First Come First Served (FCFS), Shortest Process Next (SPN) and Highest Response Ratio Next (HRRN).

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An energy‐aware method for data replication in the cloud environments using a Tabu search and particle swarm optimization algorithm

Ebadi

Navimipour

2018

Concurrency and Computation

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Cloud computing is a type of parallel, configurable, and flexible system, which refers to the provision of applications on virtual data centers. However, reducing the energy consumption and also maintaining high computation capacity have become timely and important challenges. The concept of replication is used to face these challenges. By increasing the number of data replicas, the energy consumption, the performance, and also the cost of creating and maintaining new replicas also are increased. Deciding on the number of required replicas and their location on the cloud system is an NP-hard problem. In this paper, the problem is formulated as an optimization problem and a hybrid metaheuristic algorithm is offered to solve it. The algorithm uses the global search capability of the Particle Swarm Optimization (PSO) algorithm and the local search capability of the Tabu Search (TS) to get high-quality solutions. The efficiency of the method is shown by comparing it with simple PSO, TS, and Ant Colony Optimization (ACO) algorithm on different test cases. The obtained results indicate that the method outperforms all of them in terms of consumed energy and cost. KEYWORDS cloud computing, data replication, particle swarm optimization, replica INTRODUCTIONCloud computing is a deep revolution in providing the information technology-based services based on virtualization technology. 1-4 It considers the ideas of distributed and parallel computing to offer on-demand computing resources for computers or other devices. 5-9 It aims to offer shared services with high reliability, dynamicity, and scalability; although, it is facing many issues because of its complex architecture. [10][11][12] In the cloud data centers, scalability and complexity issues cause cost and energy become two key challenges that need attention and careful study. 13,14 As the carbon footprint and consumed energy of the cloud infrastructures have been increased, investigators tried to find applicable methods for decreasing energy consumption. 15 In the last few years, the main focus of investigators and engineers was on improving the performance in terms of energy, space, and cost. 1

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Task Scheduling in the Cloud Computing Based on the Cuckoo Search Algorithm

Cited by 92 publications

References 29 publications

Application of Viral System Algorithm in Load Balancing of Cloud Environment

Application of Viral System Algorithm in Load Balancing of Cloud Environment

Multi-Objective Task Scheduling in the Cloud Computing based on the Patrice Swarm Optimization

An energy‐aware method for data replication in the cloud environments using a Tabu search and particle swarm optimization algorithm

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