Efficient Task Scheduling Multi-Objective Particle Swarm Optimization in Cloud Computing

Alkayal, Entisar S.; Jennings, Nicholas R.; Abulkhair, Maysoon F.

doi:10.1109/lcn.2016.024

Cited by 62 publications

(35 citation statements)

References 22 publications

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“…Ramakrishna Goddu, Research scholar, Department of computer science, Krishna University, Machilipatanam, Andhra Pradesh, India Kiran Kumar Reddi, Assistant Professor, Department of computer science, Krishna University, Machilipatanam, Andhra Pradesh, India necessities like CPU power and cost, after that the task scheduler allocates task to the selected virtual machine [3].…”

Section: Fig1 Flow Process Of Task Scheduling In Cloud Environmentmentioning

confidence: 99%

“…Krishnasamy [18] proposed a hybrid particle swarm optimization task scheduling algorithm which decreases the average operation time and increases the usage of resources and provides the resources according to the user tasks. Alkayal et al [3] developed an PSO based multi object task scheduling by introducing a new approach of ranking. Here, the tasks are allocated to virtual machines according to the rank, which decreases the waiting time and increase system performance.…”

Section: Fig1 Flow Process Of Task Scheduling In Cloud Environmentmentioning

confidence: 99%

“…Results shown that when compared to BRS and RSA the MOPSO is efficient with multi objectives. Entisar S. Alkayal et al [21] developed a model for optimized task scheduling based on particle swarm optimization with multi objective optimization for resource allocation. In this they introduced a new concept of ranking strategy, based on the results obtained from this ranking strategy the scheduling is done that finds the best virtual machine which suits the task.…”

Section: Fig1 Flow Process Of Task Scheduling In Cloud Environmentmentioning

confidence: 99%

“…Virtualization divides single computational resource into multiple independent execution environments, also known as virtual machines [2]. An inefficient virtual machine is nothing but a resource which decrease the performance and increases cost of the system [3]. The main objective of task scheduling is to increase performance and to decrease the task completion time by developing an efficient scheduling algorithm.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Teaching-Learning Based Task Scheduling Optimization in Cloud Computing Environments

Goddu¹,

Reddi²

2019

IJRTE

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 Abstract: Generating optimal task scheduling plans in cloud environments is a tedious task as it is a np-hard problem. The optimal resource allocation in cloud environments involves more search space and time consuming. Therefore, recent researchers are focused on implementation of artificial intelligence to solve task scheduling problem. In this paper, a new and efficient evolutionary algorithm named teaching-learning based algorithm has been implemented first time to solve the task scheduling problem in cloud environments. The current research work considers the task scheduling problem as a multi-objective optimization problem. The proposed algorithm finds the best solution by minimizing the execution time and response time while maximizing the throughput of all resources to complete the assigned tasks.Index Terms: teaching-learning based optimization, task scheduling, cloud computing, multi-objective optimization, resource allocation and throughput.environments. The results showed that the proposed TLBO algorithm obtains the best solution in less number of iterations even for the cloud environments with huge search space.

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Section: Fig1 Flow Process Of Task Scheduling In Cloud Environmentmentioning

confidence: 99%

Section: Fig1 Flow Process Of Task Scheduling In Cloud Environmentmentioning

confidence: 99%

Section: Fig1 Flow Process Of Task Scheduling In Cloud Environmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Teaching-Learning Based Task Scheduling Optimization in Cloud Computing Environments

Goddu¹,

Reddi²

2019

IJRTE

View full text Add to dashboard Cite

show abstract

“…An improved heuristic algorithm is the most common solution to the scheduling problems of cloud computing [5]. Alkayal et al [6] combined multi-objective optimization (MOO) and particle swarm optimization (PSO) to optimize resource allocation, aiming to schedule jobs to virtual machines (VMs) with minimal waiting time and maximum system throughput. Hu et al [7] devised a scientific workflow multi-objective scheduling algorithm for the reliability of workflow scheduling in a multi-cloud environment, with a goal to minimize the completion time and cost of workflow under reliability constraints.…”

Section: Introductionmentioning

confidence: 99%

A Two-Stage Framework for the Multi-User Multi-Data Center Job Scheduling and Resource Allocation

et al. 2020

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With the development of artificial intelligence and the Internet of things, the prospects of cloud computing applications have become broader, and the number of users and cloud data centers (CDCs) has exploded. It is a challenge to realize efficient job scheduling and resource allocation of multiple users and data centers. However, the traditional scheduling model based on heuristic algorithm has some limitations in the complex and changeable cloud environment. In addition, many existing single-agent models rarely consider the multi-objective global optimization of the system. Therefore, this paper proposes a two-stage job scheduling and resource allocation framework that adopts multiple intelligent schedulers to solve the cooperative scheduling problem between job scheduling and resource allocation. A heterogeneous distributed deep learning (HDDL) model is used in the job scheduling stage to schedule multiple jobs to multiple cloud data centers. The deep Q-network (DQN) model is a resource scheduler to deploy virtual machine to physical servers for execution. Extensive numerical results show that both HDDL-baesd job scheduler and DQN-based resource allocator outperform the benchmark algorithm in terms of optimizing energy consumption and job delay. Furthermore, the proposed framework not only can achieve a global near-optimum by achieving local optimization at each stage but also has good scalability and low computation delay.

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An efficient task scheduling approach using moth‐flame optimization algorithm for cyber‐physical system applications in fog computing

Ghobaei‐Arani

Souri

Safara

et al. 2019

Trans Emerging Tel Tech

144

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With the rapid development of in modern technologies has led to a novel generation of cyberspace equipment and secure analysis methods such as cyber‐physical system (CPS). The CPS system refers to an intelligent dependable system of embedded smart devices used to monitor and control the cyberspace conditions in a fog computing environment. The fog computing environment connects resources even human beings together to enhance the quality of life through performing of CPS applications using computation resources at the network edge. The optimization of task scheduling for the CSP applications as a challenging issue has been considered as the NP‐hard problem in fog computing environment which is not trivial. On the one hand, reasonable tasks scheduling can increase resource utilization, which can avoid less idle resource. This paper presents a task scheduling algorithm based on moth‐flame optimization algorithm to assign an optimal set of tasks to fog nodes to meet the satisfaction of quality of service requirements of CPS applications in such a way that the total execution time of tasks is minimized. The minimization of task execution and transfer time in the proposed algorithm are considered as objective functions. The experimental testing of the proposed solution is carried out in iFogSim toolkit. As a result of the simulation based on proposed algorithm found, the optimal solution for the scheduling of tasks and equal distribution of tasks to fog nodes has been provided, and less total execution time consumption has been achieved compared with other algorithms.

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Efficient Task Scheduling Multi-Objective Particle Swarm Optimization in Cloud Computing

Cited by 62 publications

References 22 publications

Teaching-Learning Based Task Scheduling Optimization in Cloud Computing Environments

Teaching-Learning Based Task Scheduling Optimization in Cloud Computing Environments

A Two-Stage Framework for the Multi-User Multi-Data Center Job Scheduling and Resource Allocation

An efficient task scheduling approach using moth‐flame optimization algorithm for cyber‐physical system applications in fog computing

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