Research and Implementation on Multi-Core Processor Task Scheduling Algorithm

2011 ◽  
Vol 58-60 ◽  
pp. 1732-1737
Author(s):  
Fu Zhao ◽  
Yong Ping Zhang
Keyword(s):  
Experimental Data ◽  
Task Scheduling ◽  
Load Balance ◽  
Scheduling Algorithm ◽  
Task Decomposition ◽  
Software Applications ◽  
Task Scheduling Algorithm ◽  
Multi Core Processor ◽  
A Current

This paper firstly proposes one of the problems software applications faced by in the era of multi-core CPU: task decomposition and scheduling, and then analyzes a current scheduling algorithm together with its shortcomings. On the basis, an optimized algorithm is given. The optimized algorithm reduces the error and improves the accuracy. It is easier to achieve the calculation load balance of multi-core CPU. Finally, a multi-core platform is build using Simics system simulator, and the optimized algorithm is tested on this platform. Experimental data proves the superiority of the algorithm.

Application of discrete artificial bee colony algorithm for cloud task optimization scheduling

2020 ◽  
Vol 11 (04) ◽  
pp. 2050034
Author(s):  
Shuai Man ◽  
Rongjie Yang
Keyword(s):  
Task Scheduling ◽  
Waiting Time ◽  
Load Balance ◽  
Artificial Bee Colony ◽  
Scheduling Algorithm ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Task Scheduling Algorithm ◽  
Overall Performance ◽  
The Mathematical Model

The performance of task scheduling algorithm in cloud computing determines the performance of the cloud system. This study mainly analyzed the application of the artificial bee colony (ABC) algorithm in the cloud task scheduling. In order to solve the problem of cloud task scheduling, the ABC algorithm was discretized to get the discrete artificial bee colony (DABC) algorithm. Then the mathematical model of cloud task scheduling was established and solved by the DABC algorithm. Finally, the simulation experiment was carried out, and the performance of first-come-first-served (FCFS), MIN–MIN, ABC and DABC algorithms under different cloud tasks was compared to verify the performance of the proposed algorithm. The results showed that the user waiting time of the DABC algorithm was 1210s, the load balance degree was 0.01, and the user payment fee was 1688 yuan when the number of cloud tasks was 500; compared with other algorithms, the user waiting time of the DABC algorithm was shorter, the resource load balance degree was higher, and the overall performance was better. The research results verify the effectiveness of the DABC algorithm in solving the problem of cloud task optimal scheduling, and it can be further extended and applied in practice.

scholarly journals An Improved Coral Reef Optimization-Based Scheduling Algorithm for Cloud Computing

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Shuzhen Wan ◽  
Lixin Qi
Keyword(s):  
Cloud Computing ◽  
Coral Reef ◽  
Task Scheduling ◽  
Load Balance ◽  
Virtual Machines ◽  
Scheduling Algorithm ◽  
Difficult Problem ◽  
Task Scheduling Algorithm ◽  
The Cost

An important problem in cloud computing faces the challenge of scheduling tasks to virtual machines to meet the cost and time demands, while maintaining the Quality of Service (QoS). Allocating tasks into cloud resources is a difficult problem due to the uncertainty of consumers’ future requirements and the diversity of providers’ resources. Previous studies, either on modeling or scheduling approaches, can no longer offer a satisfactory solution. In this paper, we establish a resource allocation framework and propose a novel task scheduling algorithm. An improved coral reef optimization (ICRO) is proposed to deal with this task scheduling problem. In ICRO, the better-offspring and multicrossover strategies increase the convergent speed and improve the quality of solutions. In addition, a novel load balance-aware mutation enhances the load balance among virtual machines and adjusts the number of resources provided to users. Experimental results show that compared with other algorithms, ICRO can significantly reduce the makespan and cost of the scheduling, while maintaining a better load balance in the system.

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.

Sign in / Sign up

Export Citation Format

Share Document