Multi-Task Scheduling Based on QoS Evaluation in Cloud Manufacturing System

2017 ◽  
Author(s):  
Feng Li ◽  
Lin Zhang ◽  
Yuanjun Laili
Keyword(s):  
Genetic Algorithm ◽  
Task Scheduling ◽  
Manufacturing System ◽  
Comprehensive Evaluation ◽  
Effective Means ◽  
Cloud Manufacturing ◽  
Optimal Scheduling ◽  
Distributed Resources ◽  
Scheduling Strategies ◽  
Qos Evaluation

Cloud manufacturing (CMfg) mode provides an effective means to intensely utilize distributed resources and manufacturing capability for personalized production. Increasing personalized customization implies more and more heterogeneous tasks and hence more sorts of requirements for services. As the granularity of tasks vary with changing users and products, the solution (or scheme) of task scheduling should be different. In order to efficiently provide the most suitable solution for each kind of tasks, different scheduling ways should be adopted under different circumstances. In this paper, we study scheduling issues for heterogeneous tasks with variable granularity and present two kinds of optimal scheduling mode based on user-oriented comprehensive evaluation. Then different encoding schemes relied on the genetic algorithm are proposed according to different scheduling strategies.

Task Scheduling Mechanism Based on Multi-QoS Genetic Algorithm in Cloud Data Center

2013 ◽  
Vol 846-847 ◽  
pp. 1468-1471
Author(s):  
De Wen Wang ◽  
Yang Liu
Keyword(s):  
Genetic Algorithm ◽  
Electric Power ◽  
Task Scheduling ◽  
Data Center ◽  
Evaluation Model ◽  
Operating Efficiency ◽  
Cloud Data Center ◽  
Cloud Data ◽  
Computing Platform ◽  
Qos Evaluation

A multi-QoS evaluation model for electric power users is defined, combined with the characteristics of data center in electric power corporation, based on the research of cloud computing platform of data center in electric power corporation and task scheduling strategies of cloud data center. And a genetic algorithm based on multi-QoS, which fitness functions are QoS utility value and completion time, is put forward. Tests in Cloudsim platform and the result shows that the genetic algorithm based on multi-QoS can satisfy the requirements of multi-QoS of electric power users and improve the operating efficiency of data center in electric power corporation.

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.

Task Scheduling in cloud using ACO

2020 ◽  
Vol 13 ◽  
Author(s):  
Yuvaraj Natarajan ◽  
Srihari Kannan ◽  
Gaurav Dhiman
Keyword(s):  
Load Balancing ◽  
Task Scheduling ◽  
Limited Resource ◽  
Cloud Environment ◽  
Clear Cut ◽  
Strategic Approach ◽  
User Demand ◽  
Scheduling Strategies ◽  
Cloud Users ◽  
And Task

Background: Cloud computing is a multi-tenant model for computation that offers various features for computing and storage based on user demand. With increasing cloud users, the usage increases that highlights the problem of load balancing with limited resource availability based on dynamic cloud environment. In such cases, task scheduling creates fundamental issue in cloud environment. Introduction: Certain problems such as, inefficiencies in load balancing latency, throughput ratio, proper utilization of the cloud resources, better energy consumption and response time have been observed. These drawbacks can be efficiently resolved through the incorporation of efficient load balancing and task scheduling strategies. Method: In this paper, we develop an efficient co-operative method to solve the most recent approaches against load balancing and task scheduling have been proposed using Ant Colony Optimization (ACO). These approaches enables in the clear cut identification of the problems associated with the load balancing and task scheduling strategies in the cloud environment. Results: The simulation is conducted to find the efficacy of the improved ACO system for load balancing in cloud than the other methods. The result shows that the proposed method obtains reduced execution time, reduced cost and delay. Conclusion: A unique strategic approach is developed in this paper, Load Balancing, which works with the ACO in relation to the cloud workload balancing task through the incorporation of the ACO technique. The strategy for determining the applicant nodes is based on which the load balancing approach would essentially depend. By incorporating two different approaches: the maximum minute rules and the forward-backward ant, this reliability task can be established. This method is intended to articulate the initialization of the pheromone and thus upgrade the relevant cloud-based physical properties.

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