Effective Task Scheduling and Dynamic Resource Optimization based on Heuristic Algorithms in Cloud Computing Environment (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.
Journal 4 Research - J4R Journal, 2016
Cloud Computing is the latest networking technology and also popular archetype for hosting the application and delivering of services over the network. The foremost technology of the cloud computing is virtualization which enables of building the applications, dynamically sharing of resources and providing diverse services to the cloud users. With virtualization, a service provider can guarantee Quality of Service to the user at the same time as achieving higher server consumption and energy competence. One of the most important challenges in the cloud computing environment is the VM placemnt and task scheduling problem. This paper focus on Metaheuristic Swarm Optimisation Algorithms(MSOA) deals with the problem of VM placement and Task scheduling in cloud environment. The MSOA is a simple parallel algorithm that can be applied in different ways to resolve the task scheduling problems. The proposed algorithm is considered an amalgamation of the SO algorithm and the Cuckoo search (CS) algorithm; called MSOACS. The proposed algorithm is evaluated using Cloudsim Simulator. The results proves the reduction of the makespan and increase the utilization ratio of the proposed MSOACS algorithm compared with SOA algorithms and Randomised Allocation Allocation (RA).
A Hybrid Swarm Particle Optimization Algorithm for Task Scheduling in Cloud Computing
Journal of Soft Computing and Decision Support Systems , 2020
Today, cloud computing experts seek internet-based service providing to share resources using service providing techniques. This environment provides users with an image of abundant resources. The present paper recommends a combination of particle swarm optimization algorithm and simulated annealing algorithm to obtain an improvement in the performance of task scheduling to resources considering the available bandwidth allocated to each virtual machine. The performance of the proposed algorithm is investiga ted by the use of the Cloudsim Simulator. Research results show that the proposed algorithm outperforms the Swarm Particle Optimization (SPO), bat, and raven roosting optimism algorithms in terms of task execution time, response time, and performance efficiency.
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...
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...
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.
Task Scheduling using Hybrid Algorithm in Cloud Computing Environments
The cloud computing is considered the latest network infrastructure that supports the decentralization of computing. The main features of the Cloud are the possibilities for building applications and providing various services to the end user by virtualization on the internet. One of the main challenges in the field of the cloud computing is the task scheduling problem. Task scheduling problem concerns about the dynamic distribution of the tasks over the Cloud resources to achieve the best results. Many of the algorithms have been existed to resolve the task scheduling problem such as a Particle Swarm Optimization algorithm (PSO). The PSO is a simple parallel algorithm that can be applied in different ways to resolve the task scheduling problems. In this paper, a task scheduling algorithm has been proposed to the independent task over the Cloud Computing. The proposed algorithm is considered an amalgamation of the PSO algorithm and the Cuckoo search (CS) algorithm; called PSOCS. To evaluate the proposed algorithm, the cloudsim simulator has been used. The experimental results show the reduction of the makespan and increase the utilization ratio of the proposed PSOCS algorithm compared with PSO algorithms and Random Allocation (RA).
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.
International Journal of Engineering Research and Technology (IJERT), 2014
https://www.ijert.org/review-paper-on-optimized-utilization-of-resources-using-various-task-scheduling-algorithms-in-cloud-computing https://www.ijert.org/research/review-paper-on-optimized-utilization-of-resources-using-various-task-scheduling-algorithms-in-cloud-computing-IJERTV3IS041256.pdf Cloud computing is a new topic in the field of information technology, which is developing drastically. Cloud computing delivers an elastic execution environment of resources over the internet. Task scheduling is a challenging issue to gain maximum profit and to efficiently increase working of cloud computing. In this paper we are studying task scheduling algorithms and various issues related to them i.e. how to allocate resources and maximize the profit while guaranteeing quality of service (QoS). This paper surveys Particle Swarm Optimization (PSO), Particle Swarm-simulated Annealing (P-S) algorithm and improved PSO. To get maximum benefit from resources, optimized utilization of resources is important and for this scheduling plays an important role.
International Journal of Intelligent Systems and Applications
Cloud computing has its characteristics along with so me important issues that should be handled to improve the performance and increase the efficiency of the cloud platform. These issues are related to resources management, fault tolerance, and security. The purpose of this research is to handle the resource management problem, wh ich is to allocate and schedule virtual mach ines of cloud computing in a way that help providers to reduce makespan time of tasks. In this paper, a hybrid algorith m for dynamic tasks scheduling over cloud's virtual machines is introduced. This hybrid algorith m me rges the behaviors of three effective techniques from the swarm intelligence techniques that are used to find a near optimal solution to d ifficu lt co mb inatorial problems. It exp loits the advantages of ant colony behavior, the behavior of particle swarm and honeybee foraging behavior. Experimental results reinforce the strength of the proposed hybrid algorith m. They also prove that the proposed hybrid algorith m is the best and outperformed ant colony optimization, particle swarm optimization , artificial bee colony and other known algorithms.