Comparative Analysis of Deadline Constrained Task Scheduling Algorithms for Cloud Computing under Cloudsim (original) (raw)
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A Task Scheduling Algorithm Based on Task Length and Deadline in Cloud Computing
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The on-demand availability of computer system resources such as data storage and computing power is cloud computing. Scheduling is the method of allocating jobs onto resources in time. Scheduling increases the efficiency and performance of cloud environment by maximizing the resource utilization. This scheduling process has to respect constraints given by the jobs and the cloud providers. Ordering the tasks by scheduler along with maintaining the balance between Quality of Service (QoS), fairness and efficiency of jobs is difficult. Scheduling algorithms are designed and implemented considering some parameters like latency, cost, priority, etc. The aim of this paper is a study of various types of job scheduling algorithms that provide efficient cloud services.
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A Review on Scheduling Algorithms for Workflow Application in Cloud Computing
Cloud computing is a computing paradigm where platform, scalable resources, data storage and IT services are provided over the internet. Cloud Computing environment consists of large customers requesting for cloud resources. Nowadays, task scheduling problem is the current research topic in cloud computing. Due to vast availability of resources and numerous tasks being submitted to the task management becomes important for optimal scheduling which affects the efficiency of the whole cloud computing environment. Achieving deadline and reducing cost is the main focus when we schedule the tasks by using available resources. This paper presents different proposed scheduling algorithms and strategies for independent task and workflow application in cloud computing.
Cloud computing infrastructure is suitable for meeting computational needs of large task sizes. Optimal scheduling of tasks in cloud computing environment has been proved to be an NP-complete problem, hence the need for the application of heuristic methods. Several heuristic algorithms have been developed and used in addressing this problem, but choosing the appropriate algorithm for solving task assignment problem of a particular nature is difficult since the methods are developed under different assumptions. Therefore, six rule based heuristic algorithms are implemented and used to schedule autonomous tasks in homogeneous and heterogeneous environments with the aim of comparing their performance in terms of cost, degree of imbalance, makespan and throughput. First Come First Serve (FCFS), Minimum Completion Time (MCT), Minimum Execution Time (MET), Max-min, Min-min and Sufferage are the heuristic algorithms considered for the performance comparison and analysis of task scheduling in cloud computing.