Parameters Analysis for PSO based Task Scheduling in Cloud Computing (original) (raw)
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International Journal of Advanced Computer Science and Applications
Since the beginning of cloud computing technology, task scheduling problem has never been an easy work. Because of its NP-complete problem nature, a large number of task scheduling techniques have been suggested by different researchers to solve this complicated optimization problem. It is found worth to employ heuristics methods to get optimal or to arrive at near-optimal solutions. In this work, a combination of two heuristics algorithms was proposed: particle swarm optimization (PSO) and genetic algorithm (GA). Firstly, we list pros and cons of each algorithm and express its best interest to maximize the resource utilization. Secondly, we conduct a performance comparison approach based on two most critical objective functions of task scheduling problems which are execution time and computation cost of tasks in cloud computing. Thirdly, we compare our results with other existing heuristics algorithms from the literatures. The experimental results was examined with benchmark functions and results showed that the particle swarm optimization (PSO) performs better than genetic algorithm (GA) but they both present a similarity because of their population based search methods. The results also showed that the proposed hybrid models outperform the standard PSO and reduces dramatically the execution time and lower the processing cost on the computing resources.
A Modified Particle Swarm Optimization for Task Scheduling in Cloud Computing
SSRN Electronic Journal, 2019
Cloud computing emerges as a powerful platform to deliver IT services through internet. Due to rapid development of cloud computing the user's dependencies on cloud have increased therefore scheduling of task is the key challenge. This research paper focused on scheduling user's requests to best suitable resources in optimal way. We propose a modified particle swarm optimization (MPSO) task scheduling algorithm in order to optimize execution time, transmission time, makespan, transmission cost and load balancing of virtual machines. The MPSO algorithm maintains inertia weight from 0.4 to 0.9 and it plays an important role to achieve best cost. The implementation result of MPSO on CloudSim produces best cost as compared to original PSO.
Pso Optimization algorithm for Task Scheduling on The Cloud Computing Environment
INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY, 2014
The Cloud computing is a most recent computing paradigm where IT services are provided and delivered over the Internet on demand. The Scheduling problem for cloud computing environment has a lot of awareness as the applications tasks could be mapped to the available resources to achieve better results. One of the main existed algorithms of task scheduling on the available resources on the cloud environment is based on the Particle Swarm Optimization (PSO). According to this PSO algorithm, the application’s tasks are allocated to the available resources to minimize the computation cost only.In this paper, a modified PSO algorithm has been introduced and implemented for solving task scheduling problem in the cloud. The main idea of the modified PSO is that the tasks are allocated on the available resources to minimize the execution time in addition to the computation cost. This modified PSO algorithm is called Modified Particle Swarm Optimization (MPOS).The MPOS evaluations have been...
Improved Task Scheduling using Effective Particle Swarm Optimization in Cloud Computing Environment
International Journal of Engineering and Advanced Technology, 2019
A vibrant on demand service of today’s era is cloud computing where one can utilize computer resources without indirect active management by user where one can use computing resources to achieve coherence in economic scale. Since cloud computing feel like Everything as a service so there should be highly scalable and reliable mechanisms to distribute the load evenly across the VMs evenly. Innumerable cloudlet mapping policies are presented in various research articles to achieve the high performance, better QOS and minimized task execution time but maximum are conventional approaches. No unconventional realistic scheduling algorithms is available which can schedule the tasks in heterogeneous manner. Since cloudlet scheduling is crucial metrics of cloud computing that has to be heightened by combining the different parameters. This paper tried to provide effectiveness and improvement in task scheduling using nature inspired Particle Swarm optimization (PSO) strategy. A powerful natur...
Solving Task Scheduling Problem in the Cloud Using a Hybrid Particle Swarm Optimization Approach
International Journal of Applied Metaheuristic Computing, 2021
Synergistic confluence of pervasive sensing, computing, and networking is generating heterogeneous data at unprecedented scale and complexity. Cloud computing has emerged in the last two decades as a unique storage and computing resource to support a diverse assortment of applications. Numerous organizations are migrating to the cloud to store and process their information. When the cloud infrastructures and resources are insufficient to satisfy end-users requests, scheduling mechanisms are required. Task scheduling, especially in a distributed and heterogeneous system, is an NP-hard problem since various task parameters must be considered for an appropriate scheduling. In this paper, the authors propose a hybrid PSO and extremal optimization-based approach to resolve task scheduling in the cloud. The algorithm optimizes makespan which is an important criterion to schedule a number of tasks on different virtual machines. Experiments on synthetic and real-life workloads show the capability of the method to successfully schedule tasks and outperforms many state-of-the-art methods.
Hybrid Task Scheduling Method for Cloud Computing by Genetic and PSO Algorithms
Cloud computing makes it possible for users to use different applications through the internet without having to install them. Cloud computing is considered to be a novel technology which is aimed at handling and providing online services. For enhancing efficiency in cloud computing, appropriate task scheduling techniques are needed. Due to the limitations and heterogeneity of resources, the issue of scheduling is highly complicated. Hence, it is believed that an appropriate scheduling method can have a significant impact on reducing makespans and enhancing resource efficiency. Inasmuch as task scheduling in cloud computing is regarded as an NP complete problem; traditional heuristic algorithms used in task scheduling do not have the required efficiency in this context. With regard to the shortcomings of the traditional heuristic algorithms used in job scheduling, recently, the majority of researchers have focused on hybrid meta-heuristic methods for task scheduling. With regard to this cutting edge research domain, we used HEFT (Heterogeneous Earliest Finish Time) algorithm to propose a hybrid meta-heuristic method in this paper where genetic algorithm (GA) and particle swarm optimization (PSO) algorithms were combined with each other. The experimental results of simulation are shown that the proposed algorithm optimizes the average makespans of the HEFT_UpRank, HEFT_DownRank, HEFT_LevelRank and MPQMA for 100 independent task graphs scheduling with 10, 50 and 100 tasks. Total optimization of makespans by the proposed algorithm against the other algorithms were 6.44, 10.41, 6.33 and 4.8 percent respectively.
Survey of Various Pso Variants Used for Cloud Computing Task Scheduling
Journal of emerging technologies and innovative research, 2018
Cloud environment requires scheduling of independent tasks with the available resources to minimize the total execution time and to optimize the resource utilization . Scheduling in cloud computing belongs to the NP-hard category of problem. Scheduling in the cloud computing is difficult because of the complex task requirement and heterogeneous, distributed and dynamic nature of the request as well as resources. As it becomes critical to find the exact solution, various meta-heuristic techniques are used to attain the approximate solution. Several research studies have been done to improve the cloud task scheduling using PSO approach. This paper presents the Particle Swarm Optimization algorithm and various variants of PSO which are used for cloud task scheduling. IndexTerms – Cloud computing, Task Scheduling, Particle Swarm Optimization, Meta-heuristic Algorithms.
A Simplified Particle Swarm Optimization for Job Scheduling in Cloud Computing
International Journal of Computer Applications
Recent advances in various areas such as networking, information and communication technologies have greatly boosted the potential capabilities of cloud computing and made it become more prevalent in recent years. Cloud computing is a promising computing paradigm that facilitates the delivery of IT infrastructure, platforms, and applications of any kind to consumers as services over the internet. Although cloud computing systems nowadays provide better ways to accomplish the job requests in terms of responsiveness and scalability under various workloads, scheduling of jobs or tasks in cloud environment is still NPcomplete and complex in nature due to the dynamicity of resources and on-demand user application requirements. In this paper, a simplified version of particle swarm optimization (PSO) algorithm is proposed to solve the job scheduling problem in cloud computing environment. To evaluate the performance of the proposed approach, this study compares the proposed PSO strategy with genetic algorithm (GA), by having both of them implemented on CloudSim toolkit. The results obtained demonstrate that the presented PSO algorithm can significantly reduce the makespan of job scheduling problem compared with the other metaheuristic algorithm evaluated in this paper.
Efficient Task Scheduling in Cloud Computing using an Improved Particle Swarm Optimization Algorithm
Proceedings of the 9th International Conference on Cloud Computing and Services Science, 2019
An improved multi-objective discrete particle swarm optimization (IMODPSO) algorithm is proposed to solve the task scheduling and resource allocation problem for scientific workflows in cloud computing. First, we use a strategy to limit the velocity of particles and adopt a discrete position updating equation to solve the multi-objective time and cost optimization model. Second, we adopt a Gaussian mutation operation to update the personal best position and the external archive, which can retain the diversity and convergence accuracy of Pareto optimal solutions. Finally, the computational complexity of IMODPSO is compared with three other state-of-the-art algorithms. We validate the computational speed, the number of solutions found and the generational distance of IMODPSO and find that the new algorithm outperforms the three other algorithms with respect to all three metrics.
EAI Endorsed Trans. Cloud Syst., 2021
Cloud computing emerges as a powerful platform to deliver IT services online. Due to the rapid development of cloud computing the user's dependence on the cloud has increased and hence user request per unit time is increases. Now scheduling and serving the user requests is a major challenge. Particle swarm optimization as a heuristic algorithm is the most suitable algorithm in such scenario to serve user requests for the most appropriate resources. Author written this research paper in continuation with previous research paper called Modified particle swarm optimization (MPSO) in which author controlled the inertia weight in PSO to find the best cost. This research paper investigates the effect of acceleration coefficient to achieve the best cost. The implementation results of PSO with different acceleration coefficient are produced and compared. Author has use MATLab to test the effect of acceleration coefficient on fitness value and also implemented in CloudSim simulator to te...