Optimal resource model and task scheduling algorithm based on dynamic load balancing strategy in grid

2009 ◽  
Vol 28 (11) ◽  
pp. 2867-2870 ◽  
Author(s):  
Shao-bo ZHONG
Keyword(s):  
Load Balancing ◽  
Task Scheduling ◽  
Dynamic Load ◽  
Scheduling Algorithm ◽  
Dynamic Load Balancing ◽  
Resource Model ◽  
Task Scheduling Algorithm ◽  
Optimal Resource ◽  
And Task

scholarly journals Dynamic Load Balancing Ant Colony Optimization (DLBACO) Algorithm for Task Scheduling in Cloud Environment

2019 ◽  
Vol 8 (12) ◽  
pp. 939-946
Keyword(s):  
Load Balancing ◽  
Ant Colony Optimization ◽  
Task Scheduling ◽  
Dynamic Load ◽  
Heuristic Algorithms ◽  
Scheduling Algorithm ◽  
Ant Colony ◽  
Dynamic Load Balancing ◽  
Cloud Environment ◽  
Task Scheduling Algorithm

Cloud computing is a framework which provides on-demand services to the user for scalability, security, and reliability based on pay as used service anytime & anywhere. For load balancing, task scheduling is the most critical issues in the cloud environment. There are so many meta-heuristic algorithms used to solve the load balancing problem. A good task scheduling algorithm should be used for optimum load balancing in cloud environment. Such scheduling algorithm must have some vital characteristic like minimum makespan, maximum throughput, and maximum resource utilization, etc. In this paper, a dynamic load balancing and task scheduling algorithm based on ant colony optimization (DLBACO) has been proposed. This algorithm assigns the task the VM which has highest probability of availability in minimum time. The proposed algorithm balances the whole system by minimizing the makespan of the task and maximizing the throughput. CloudSim simulator is used to simulate the proposed scheduling algorithm and results show that the proposed (DLBACO) algorithm is better than the existing algorithms such as FCFS, LBACO (Load balancing ACO), and primary ACO

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.

An Efficient Resources Management Model and Task Scheduling Algorithm in Grid Computing

2011 ◽  
Vol 50-51 ◽  
pp. 526-530 ◽  
Author(s):  
Xiao Bo Gao
Keyword(s):  
Grid Computing ◽  
Task Scheduling ◽  
Resource Sharing ◽  
Large Scale ◽  
Scheduling Algorithm ◽  
Management Model ◽  
Network Computing ◽  
Resources Management ◽  
Task Scheduling Algorithm ◽  
And Task

From the perspective of resource sharing, grid computing is a system ranging from small kind of network system for home using to large-scale network computing systems even to the Internet. The management of resources in the grid environment becomes very complex as these resources are distributed geographically, heterogeneous in nature, and each having their own resource management policies and different access as well as cost models. In this paper, we bring forward an efficient resources management model and task scheduling algorithm in grid computing. The simulation results show that the proposed algorithm achieves resource load balancing, and can be applied to the optimization of task scheduling successfully.

Research of Load Balancing Scheduling under Grid Computing

2014 ◽  
Vol 577 ◽  
pp. 935-938
Author(s):  
Cheng Yu Cai ◽  
Yuan Sheng Lou
Keyword(s):  
Load Balancing ◽  
Grid Computing ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
The Other ◽  
Task Scheduling Algorithm ◽  
Better Than

In order to make up for the shortage of Min-Min in load balancing, a new task scheduling algorithm T-Max-Int Under the grid computing has been proposed in this paper. In T-Max-Int, the Loss Degree of Max-Int has been brought into Min-Min. T was in the form of percentage, which represents the proportion of selected tasks that have loss degree in the total tasks. Then, experiments of T have been taken to make Makespan the minimum. Finally, T-Max-Int, Max-Min, Min-Min were compared, which proved that T-Max-Min is better than the other two algorithms in aspects of Makespan and load balancing.

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