scholarly journals Cloud Service Scheduling Algorithm Research and Optimization

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Hongyan Cui ◽  
Xiaofei Liu ◽  
Tao Yu ◽  
Honggang Zhang ◽  
Yajun Fang ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Ant Colony Optimization ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Cloud Service ◽  
Ant Colony ◽  
General Distribution ◽  
Process Time ◽  
Service Scheduling ◽  
Output Performance

We propose a cloud service scheduling model that is referred to as the Task Scheduling System (TSS). In the user module, the process time of each task is in accordance with a general distribution. In the task scheduling module, we take a weighted sum of makespan and flowtime as the objective function and use an Ant Colony Optimization (ACO) and a Genetic Algorithm (GA) to solve the problem of cloud task scheduling. Simulation results show that the convergence speed and output performance of our Genetic Algorithm-Chaos Ant Colony Optimization (GA-CACO) are optimal.

scholarly journals Ant Colony Optimization Task Scheduling Algorithm for SWIM Based on Load Balancing

2019 ◽  
Vol 11 (4) ◽  
pp. 90 ◽  
Author(s):  
Gang Li ◽  
Zhijun Wu
Keyword(s):  
Load Balancing ◽  
Ant Colony Optimization ◽  
Task Scheduling ◽  
Large Scale ◽  
Scheduling Algorithm ◽  
Pso Algorithm ◽  
Ant Colony ◽  
Experimental Simulation ◽  
Task Completion ◽  
Task Scheduling Algorithm

This paper focuses on the load imbalance problem in System Wide Information Management (SWIM) task scheduling. In order to meet the quality requirements of users for task completion, we studied large-scale network information system task scheduling methods. Combined with the traditional ant colony optimization (ACO) algorithm, using the hardware performance quality index and load standard deviation function of SWIM resource nodes to update the pheromone, a SWIM ant colony task scheduling algorithm based on load balancing (ACTS-LB) is presented in this paper. The experimental simulation results show that the ACTS-LB algorithm performance is better than the traditional min-min algorithm, ACO algorithm and particle swarm optimization (PSO) algorithm. It not only reduces the task execution time and improves the utilization of system resources, but also can maintain SWIM in a more load balanced state.

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

Grid Task Scheduling Strategy Based on Particle Swarm Optimizationand Ant Colony Optimization Algorithm

2010 ◽  
Vol 108-111 ◽  
pp. 392-397
Author(s):  
Peng Cheng Wei ◽  
Xi Shi
Keyword(s):  
Particle Swarm Optimization ◽  
Ant Colony Optimization ◽  
Task Scheduling ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Scheduling Algorithm ◽  
Particle Swarm ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Swarm Optimization

Based on particle swarm optimization algorithm, this paper presents a grid scheduling optimization algorithm combing the advantages of Ant Colony optimization algorithm. The algorithm processes task scheduling through particle swarm optimization algorithm to get a group of relatively optimal solutions, and then conducts small-area local search with Ant Colony optimization algorithm. Theoretical analysis and results of the simulation experiments show that this scheduling algorithm effectively achieves load balancing of resources with comprehensive advantages in time efficiency and solution accuracy compared to the traditional Ant Colony optimization algorithm and particle swarm optimizationalgorithm, and can be applied to task scheduling in grid computing.

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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