scholarly journals Multijob Associated Task Scheduling for Cloud Computing Based on Task Duplication and Insertion

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Lei Shi ◽  
Jing Xu ◽  
Lunfei Wang ◽  
Jie Chen ◽  
Zhifeng Jin ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Computing Systems ◽  
Use Of Resources ◽  
Task Scheduling Algorithm ◽  
Task Duplication ◽  
Insertion Algorithm ◽  
Speed Up ◽  
Execution Order

With the emergence and development of various computer technologies, many jobs processed in cloud computing systems consist of multiple associated tasks which follow the constraint of execution order. The task of each job can be assigned to different nodes for execution, and the relevant data are transmitted between nodes to complete the job processing. The computing or communication capabilities of each node may be different due to processor heterogeneity, and hence, a task scheduling algorithm is of great significance for job processing performance. An efficient task scheduling algorithm can make full use of resources and improve the performance of job processing. The performance of existing research on associated task scheduling for multiple jobs needs to be improved. Therefore, this paper studies the problem of multijob associated task scheduling with the goal of minimizing the jobs’ makespan. This paper proposes a task Duplication and Insertion algorithm based on List Scheduling (DILS) which incorporates dynamic finish time prediction, task replication, and task insertion. The algorithm dynamically schedules tasks by predicting the completion time of tasks according to the scheduling of previously scheduled tasks, replicates tasks on different nodes, reduces transmission time, and inserts tasks into idle time slots to speed up task execution. Experimental results demonstrate that our algorithm can effectively reduce the jobs’ makespan.

scholarly journals SLA-DQTS: SLA Constrained Adaptive Online Task Scheduling Based on DDQN in Cloud Computing

2021 ◽  
Vol 11 (20) ◽  
pp. 9360
Author(s):  
Kaibin Li ◽  
Zhiping Peng ◽  
Delong Cui ◽  
Qirui Li
Keyword(s):  
Cloud Computing ◽  
Task Scheduling ◽  
Performance Optimization ◽  
Heuristic Algorithms ◽  
Scheduling Algorithm ◽  
Computing Systems ◽  
Q Learning ◽  
Reward Function ◽  
Task Scheduling Algorithm ◽  
Comparison Algorithms

Task scheduling is key to performance optimization and resource management in cloud computing systems. Because of its complexity, it has been defined as an NP problem. We introduce an online scheme to solve the problem of task scheduling under a dynamic load in the cloud environment. After analyzing the process, we propose a server level agreement constraint adaptive online task scheduling algorithm based on double deep Q-learning (SLA-DQTS) to reduce the makespan, cost, and average overdue time under the constraints of virtual machine (VM) resources and deadlines. In the algorithm, we prevent the change of the model input dimension with the number of VMs by taking the Gaussian distribution of related parameters as a part of the state space. Through the design of the reward function, the model can be optimized for different goals and task loads. We evaluate the performance of the algorithm by comparing it with three heuristic algorithms (Min-Min, random, and round robin) under different loads. The results show that the algorithm in this paper can achieve similar or better results than the comparison algorithms at a lower cost.

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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