GA-Based Task Scheduling Algorithm for Efficient Utilization of Available Resources in Computational Grid

2018 ◽  
pp. 119-126
Author(s):  
Shipra Singh ◽  
Anuradha Aggarwal ◽  
Harendera Kumar ◽  
Pradeep Kumar Yadav
Keyword(s):  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Computational Grid ◽  
Efficient Utilization ◽  
Task Scheduling Algorithm ◽  
Available Resources

scholarly journals Deadline Aware Prioritized Task Scheduling Algorithm in Cloud Computing

2019 ◽  
Vol 8 (2S11) ◽  
pp. 815-818
Keyword(s):  
Cloud Computing ◽  
Task Scheduling ◽  
Service Providers ◽  
Scheduling Algorithm ◽  
Challenging Issue ◽  
Computing Paradigm ◽  
Task Scheduling Algorithm ◽  
Heuristics Algorithm ◽  
Available Resources

Cloud Computing is a computing Paradigm in which services are provided by service providers on pay-per-use. Task Scheduling is the challenging issue in cloud computing. Task scheduling refers to allocating tasks to available resources to achieve better performance of the system. Here we have proposed a Heuristics algorithm to schedule tasks in given resources which satisfies the QoS of system taking Priority and Deadline of tasks as parameters. Our algorithm is compared with existing algorithms like EDF and TLD algorithms. Our algorithm provides better makespan, increases throughput and utilize resources well compared to existing algorithms

Task Scheduling Algorithm based on Resources Segregation in Cloud Environment

2018 ◽  
pp. 96-101
Author(s):  
Shailendra Raghuvanshi ◽  
Priyanka Dubey
Keyword(s):  
Load Balancing ◽  
Task Scheduling ◽  
Waiting Time ◽  
Virtual Machines ◽  
Scheduling Algorithm ◽  
Cloud Environment ◽  
Task Scheduling Algorithm ◽  
Machine Utilization ◽  
Independent Tasks ◽  
Optimal Machine

Load balancing of non-preemptive independent tasks on virtual machines (VMs) is an important aspect of task scheduling in clouds. Whenever certain VMs are overloaded and remaining VMs are under loaded with tasks for processing, the load has to be balanced to achieve optimal machine utilization. In this paper, we propose an algorithm named honey bee behavior inspired load balancing, which aims to achieve well balanced load across virtual machines for maximizing the throughput. The proposed algorithm also balances the priorities of tasks on the machines in such a way that the amount of waiting time of the tasks in the queue is minimal. We have compared the proposed algorithm with existing load balancing and scheduling algorithms. The experimental results show that the algorithm is effective when compared with existing algorithms. Our approach illustrates that there is a significant improvement in average execution time and reduction in waiting time of tasks on queue using workflowsim simulator in JAVA.

Task scheduling algorithm based on improved genetic algorithm in cloud computing environment

2020 ◽  
Vol 13 ◽  
Author(s):  
Ge Weiqing ◽  
Cui Yanru
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Task Completion ◽  
Improved Genetic Algorithm ◽  
Fitness Functions ◽  
Task Scheduling Algorithm ◽  
Simulation Results

Background: In order to make up for the shortcomings of the traditional algorithm, Min-Min and Max-Min algorithm are combined on the basis of the traditional genetic algorithm. Methods: In this paper, a new cloud computing task scheduling algorithm is proposed, which introduces Min-Min and Max-Min algorithm to generate initialization population, and selects task completion time and load balancing as double fitness functions, which improves the quality of initialization population, algorithm search ability and convergence speed. Results: The simulation results show that the algorithm is superior to the traditional genetic algorithm and is an effective cloud computing task scheduling algorithm. Conclusion: Finally, this paper proposes the possibility of the fusion of the two quadratively improved algorithms and completes the preliminary fusion of the algorithm, but the simulation results of the new algorithm are not ideal and need to be further studied.

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