scholarly journals Optimization model for QoS based task scheduling in cloud computing environment

2020 ◽  
Vol 18 (2) ◽  
pp. 1081
Author(s):  
Sirisha Potluri ◽  
Katta Subba Rao
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Task Scheduling ◽  
Optimization Model ◽  
Execution Time ◽  
Scheduling Algorithm ◽  
Cloud Services ◽  
Allocation Model ◽  
Task Scheduling Algorithm ◽  
And Task

Shortest job first task scheduling algorithm allocates task based on the length of the task, i.e the task that will have small execution time will be scheduled first and the longer tasks will be executed later based on system availability. Min- Min algorithm will schedule short tasks parallel and long tasks will follow them. Short tasks will be executed until the system is free to schedule and execute longer tasks. Task Particle optimization model can be used for allocating the tasks in the network of cloud computing network by applying Quality of Service (QoS) to satisfy user’s needs. The tasks are categorized into different groups. Every one group contains the tasks with attributes (types of users and tasks, size and latency of the task). Once the task is allocated to a particular group, scheduler starts assigning these tasks to accessible services. The proposed optimization model includes Resource and load balancing Optimization, Non-linear objective function, Resource allocation model, Queuing Cost Model, Cloud cost estimation model and Task Particle optimization model for task scheduling in cloud computing environement. The main objectives identified are as follows. To propose an efficient task scheduling algorithm which maps the tasks to resources by using a dynamic load based distributed queue for dependent tasks so as to reduce cost, execution and tardiness time and to improve resource utilization and fault tolerance. To develop a multi-objective optimization based VM consolidation technique by considering the precedence of tasks, load balancing and fault tolerance and to aim for efficient resource allocation and performance of data center operations. To achieve a better migration performance model to efficiently model the requirements of memory, networking and task scheduling. To propose a QoS based resource allocation model using fitness function to optimize execution cost, execution time, energy consumption and task rejection ratio and to increase the throughput. QoS parameters such as reliability, availability, degree of imbalance, performance and SLA violation and response time for cloud services can be used to deliver better cloud services.

An Efficient Kernel FCM and Artificial Fish Swarm Optimization-Based Optimal Resource Allocation in Cloud

2020 ◽  
Vol 29 (16) ◽  
pp. 2050253
Author(s):  
Pravin Albert ◽  
Manikandan Nanjappan
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Task Scheduling ◽  
Virtual Machines ◽  
Optimal Resource Allocation ◽  
Allocation Model ◽  
Swarm Optimization ◽  
Artificial Fish Swarm ◽  
Optimal Resource ◽  
And Task

Cloud computing model allows service-oriented system that fulfills the needs of the consumers. Capable resource management and task allocation are the important issues in cloud computing. Performance of the task scheduling method directly interrupts the utilization of cloud computing resources and the quality of experience of users. For that reason, reasonable virtual machine (VM) allocation and task scheduling are extremely important. In this paper, an efficient resource allocation model is proposed. Initially, the virtual machines are clustered with the help of Kernel Fuzzy [Formula: see text]-Means Clustering (KFCM) algorithm to reduce the complexity. After the clustering process, the user tasks are allocated to the particular VM using artificial fish swarm optimization (AFSO) algorithm. A multi-objective function is designed to achieve an optimal resource allocation. The performance of the suggested technique is tested in terms of different metrics.

Research on Multi-QoS and Trusted Task Scheduling in Cloud Computing Environment

2012 ◽  
Vol 263-266 ◽  
pp. 1892-1896
Author(s):  
Fu Fang Li ◽  
Dong Qing Xie ◽  
Ying Gao ◽  
Guo Wen Xie ◽  
Qiu Ye Guo
Keyword(s):  
Cloud Computing ◽  
Resource Management ◽  
Task Scheduling ◽  
Fuzzy Clustering ◽  
Scheduling Algorithm ◽  
Management Model ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Task Scheduling Algorithm ◽  
And Task

Multi-QoS and Trusted resource management and Task Scheduling are key problem in cloud computing. This paper presented an effective Multi-QoS and Trusted resource management model and Task Scheduling algorithm in Cloud Computing Environment. By using the idea of fuzzy clustering for reference, the proposed approach can subtly schedule the Cloud tasks to appropriate resources that exactly meets its’ multi-QoS needs of resources. Considering the credibility of cloud resources, the credibility and trusty of task scheduling has been dramatically improved.

Task scheduling algorithm based on reliability perception in cloud computing

2020 ◽  
Vol 13 ◽  
Author(s):  
Kuang Yuejuan ◽  
Luo Zhuojun ◽  
Ouyang Weihao
Keyword(s):  
Cloud Computing ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Resource Scheduling ◽  
Pso Algorithm ◽  
Cloud Services ◽  
Task Scheduling Algorithm ◽  
Cloud Resource ◽  
Communication Links ◽  
The Impact

Background: In order to obtain reliable cloud resources, reduce the impact of resource node faults in cloud computing environment and reduce the fault time perceived by the application layer, a task scheduling model based on reliability perception is proposed. Methods: The model combines the two-parameter weibull distribution and analyzes various interaction relations between parallel tasks to describe the local characteristics of the failure rules of resource nodes and communication links in different periods.The model is added into the particle swarm optimization (pso) algorithm, and an adaptive inertial weighted pso resource scheduling algorithm based on reliability perception is obtained. Results: Simulation results show that when A increases to 0.3, the average scheduling length of the task increases rapidly.When it is 0.4-0.6, the growth rate is relatively slow.When greater than 0.8, the average scheduling length increases sharply.It can be seen that the r-pso algorithm proposed in this paper can accurately estimate the relevant parameters of cloud resource failure rule, and the generated resource scheduling scheme has better fitness, and the optimization effect is more significant with the increase of the number of tasks. Conclusion: With only a small amount of time added, the reliability of cloud services is greatly improved.

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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