Energy-and-Time-Saving Task Scheduling Based on Improved Genetic Algorithm in Mobile Cloud Computing

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.

Download Full-text

Workflow Scheduling Based on Mobile Cloud Computing Machine Learning

Wireless Communications and Mobile Computing ◽

10.1155/2021/9923326 ◽

2021 ◽

Vol 2021 ◽

pp. 1-13

Author(s):

Fanghai Gong

Keyword(s):

Machine Learning ◽

Cloud Computing ◽

Task Scheduling ◽

Completion Time ◽

Mobile Cloud Computing ◽

Task Completion ◽

Workflow Scheduling ◽

Mobile Cloud ◽

Task Completion Time ◽

Computing Machine

In recent years, cloud workflow task scheduling has always been an important research topic in the business world. Cloud workflow task scheduling means that the workflow tasks submitted by users are allocated to appropriate computing resources for execution, and the corresponding fees are paid in real time according to the usage of resources. For most ordinary users, they are mainly concerned with the two service quality indicators of workflow task completion time and execution cost. Therefore, how cloud service providers design a scheduling algorithm to optimize task completion time and cost is a very important issue. This paper proposes research on workflow scheduling based on mobile cloud computing machine learning, and this paper conducts research by using literature research methods, experimental analysis methods, and other methods. This article has deeply studied mobile cloud computing, machine learning, task scheduling, and other related theories, and a workflow task scheduling system model was established based on mobile cloud computing machine learning from different algorithms used in processing task completion time, task service costs, task scheduling, and resource usage The situation and the influence of different tasks on the experimental results are analyzed in many aspects. The algorithm in this paper speeds up the scheduling time by about 7% under a different number of tasks and reduces the scheduling cost by about 2% compared with other algorithms. The algorithm in this paper has been obviously optimized in time scheduling and task scheduling.

Download Full-text

An improved genetic algorithm for task scheduling in cloud computing

2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA) ◽

10.1109/iciea.2018.8397773 ◽

2018 ◽

Cited By ~ 2

Author(s):

Shuang Yin ◽

Peng Ke ◽

Ling Tao

Keyword(s):

Genetic Algorithm ◽

Cloud Computing ◽

Task Scheduling ◽

Improved Genetic Algorithm

Download Full-text

Energy Efficient Task Scheduling in Mobile Cloud Computing

Lecture Notes in Computer Science - Network and Parallel Computing ◽

10.1007/978-3-642-40820-5_29 ◽

2013 ◽

pp. 344-355 ◽

Cited By ~ 14

Author(s):

Dezhong Yao ◽

Chen Yu ◽

Hai Jin ◽

Jiehan Zhou

Keyword(s):

Cloud Computing ◽

Task Scheduling ◽

Energy Efficient ◽

Mobile Cloud Computing ◽

Mobile Cloud

Download Full-text

Cooperative Runtime Offloading Decision Algorithm for Mobile Cloud Computing

Mobile Information Systems ◽

10.1155/2019/8049804 ◽

2019 ◽

Vol 2019 ◽

pp. 1-17 ◽

Cited By ~ 2

Author(s):

Xiaomin Jin ◽

Zhongmin Wang ◽

Wenqiang Hua

Keyword(s):

Genetic Algorithm ◽

Cloud Computing ◽

Data Transmission ◽

Mobile Cloud Computing ◽

Mobile Cloud ◽

Wireless Data ◽

Decision Algorithm ◽

Wireless Data Transmission ◽

Cloud Environments ◽

New Challenges

Mobile cloud computing (MCC) provides a platform for resource-constrained mobile devices to offload their tasks. MCC has the characteristics of cloud computing and its own features such as mobility and wireless data transmission, which bring new challenges to offloading decision for MCC. However, most existing works on offloading decision assume that mobile cloud environments are stable and only focus on optimizing the consumption of offloaded applications but ignore the consumption caused by offloading decision algorithms themselves. This paper focuses on runtime offloading decision in dynamic mobile cloud environments with the consideration of reducing the offloading decision algorithm’s consumption. A cooperative runtime offloading decision algorithm, which takes advantage of the cooperation of online machine learning and genetic algorithm to make offloading decisions, is proposed to address this problem. Simulations show that the proposed algorithm helps offloaded applications save more energy and time while consuming fewer computing resources.

Download Full-text