An Enhanced Task Scheduling Algorithm on Cloud Computing Environment (original) (raw)
Related papers
Comparative Analysis of PSO Algorithm in Cloud Computing
International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2022
Cloud computing makes it possible to access applications and data from anywhere so this has become new technology. The goals of the paper are to provide additional insights to suggest ways in which performance might be improved by incorporating features from one paradigm into the other. The Reasearch Paper focus on particle swarm optimization (PSO) heuristic-based algorithm based on Scheduling. By scheduling, applications are scheduled to the cloud resources that are used for computation of cost and transmission cost of data in the cloud. by using PSO total cost of execution is minimized. Since the inception of Inertia Weight in PSO, a large number of variations of the Inertia Weight strategy have been proposed. In order to propose one or more than one Inertia Weight strategies that are efficient than others, this paper studies popular Inertia Weight strategies and compares their performance on optimization test problems.
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...
An Energy Efficient Hybrid PSO Algorithm in Cloud Environment
International Journal of Engineering and Advanced Technology, 2020
Algorithms exist to schedule various tasks in real time cloud environment. Nowadays many researchers are trying to schedule heavily loaded situations in real time cloud environment using swarming technique. For such studies many parameters need to be considered like cost of the system, processor latency, number of tasks and so on. With the increase in the number of tasks in the set, processing time also increases. In this situation, processor latency is at peak as the number of tasks increases and system costs increase. So the above mentioned problem is handled by proposing a task scheduler that uses a PSO algorithm to remove the limitations of past studies in a heavily loaded situation. The Particle Swarm optimization (PSO) and Invasive Weed Optimization (IWO) are combined to propose a new technique called the HWO algorithm. The proposed algorithm is recommended for preventive tasks in the single-processor in real-time environment systems.
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.
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.
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.
An Overview of Variants and Advancements of PSO Algorithm
Applied Sciences
Particle swarm optimization (PSO) is one of the most famous swarm-based optimization techniques inspired by nature. Due to its properties of flexibility and easy implementation, there is an enormous increase in the popularity of this nature-inspired technique. Particle swarm optimization (PSO) has gained prompt attention from every field of researchers. Since its origin in 1995 till now, researchers have improved the original Particle swarm optimization (PSO) in varying ways. They have derived new versions of it, such as the published theoretical studies on various parameters of PSO, proposed many variants of the algorithm and numerous other advances. In the present paper, an overview of the PSO algorithm is presented. On the one hand, the basic concepts and parameters of PSO are explained, on the other hand, various advances in relation to PSO, including its modifications, extensions, hybridization, theoretical analysis, are included.
Parameters Analysis for PSO based Task Scheduling in Cloud Computing
SSRN Electronic Journal, 2019
In today's world of information technology, cloud computing emerges as a new computing paradigm due to its economical and operational benefits. Cloud computing is able to perform the process of the enormous amount of data using high computing capacity and distributed services. When a user needs to fluctuate, cloud server capacity scales up and down to fit. It is highly flexible, reduce capital expenditure, robust disaster recovery and can operate from anywhere through the internet. The User can avail these facilities by submitting their computing task to the cloud system. So scheduling tasks to reduce the task completion time is the main purpose of task scheduling algorithm. The objective of this paper is to analyse the various parameters of particle swarm optimization (PSO) algorithm to highlight effectiveness, strength and weakness compare to other evolutionary algorithms. This paper also analyses effective virtualization of cloud infrastructure and suitability of these parameters towards efficient task scheduling in a cloud computing environment.
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.
TOPSIS–PSO inspired non-preemptive tasks scheduling algorithm in cloud environment
Cluster Computing, 2019
Cloud computing is an emerging paradigm that offers various services for both users and enterprisers. Scheduling of user tasks among data centers, host and virtual machines (VMs) becomes challenging issues in cloud due to involvement of vast number of users. To address such issues, a new multi-criteria approach i.e., technique of order precedence by similarity to ideal solution (TOPSIS) algorithm is introduced to perform task scheduling in cloud systems. The task scheduling is performed in two phases. In first phase, TOPSIS algorithm is applied to obtain the relative closeness of tasks with respect to selected scheduling criteria (i.e., execution time, transmission time and cost). In second phase the particle swarm optimization (PSO) begins with computing relative closeness of the given three criteria for all tasks in all VMs. A weighted sum of execution time, transmission time and cost used as an objective function by TOPSIS to solve the problem of multiobjective task scheduling in cloud environment. The simulation work has been done in CloudSim. The performance of proposed work has been compared with PSO, dynamic PSO (DPSO), ABC, IABC and FUGE algorithms on the basis of MakeSpan, transmission time, cost and resource utilization. Experimental results show approximate 75% improvement on average utilization of resources than PSO. Processing cost of TOPSIS-PSO reduced at approximate 23.93% and 55.49% than IABC and ABC respectively. The analysis also shows that TOPSIS-PSO algorithm reduces 3.1, 29.1 and 14.4% MakeSpan than FUGE, ant colony optimization (ACO) and multiple ACO respectively. Plotted graphs and calculated values show that the proposed work is very innovative and effective for task scheduling. This TOPSIS method to calculate relative closeness for PSO has been remarkable.