Abstract
Due to the heterogeneity, distribution, autonomy and diversity of services in cloud computing environment, higher requirements are put forward for cloud platform scheduling mechanism, so the research on cloud architecture and its scheduling mechanism has attracted more and more attention from the industry. A cloud computing task scheduling algorithm based on calculus mathematical equation is proposed. Through the double boundary convergence control of the partial differential classification mathematical model, the partial differential classification data model is integrated into the data set, and the fuzzy control of the data is completed through the increment and decrement support vector. The membership function is used to transform the multi-QoS(quality of service) objective constraint problem into a single objective constraint solving problem. Compared with traditional methods, the method proposed in this paper can effectively reduce the deadline baseline violation rate of user task scheduling, and reduce its average task execution time and average task execution cost on the premise of meeting the user task multi-QoS target constraints.
Publisher
Darcy & Roy Press Co. Ltd.
Reference10 articles.
1. Ma T, Pang S, Zhang W, et al. Virtual Machine Based on Genetic Algorithm Used in Time and Power Oriented Cloud Computing Task Scheduling [J]. Intelligent automation and soft computing, 2019, 25(3):603-611.
2. Pang S, Li W, He H, et al. An EDA-GA Hybrid Algorithm for Multi-objective Task Scheduling in Cloud Computing [J]. IEEE Access, 2019, PP(99):1-1.
3. Mukherjee P, Pattnaik P K, Swain T, et al. Task scheduling algorithm based on multi criteria decision making method for cloud computing environment: TSABMCDMCCE [J]. Open Computer Science, 2019, 9(1):279-291.
4. Fan L, Dong M, Jing C. An Efficient Task Scheduling Algorithm Based on Particle Swarm Optimization with Self-Learning Strategy and Neighbor Heuristic Mechanism on the Cloud [J]. Computer journal, 2020, 31(2):180-196.
5. Hung P P, Alam G, Hai N, et al. A Dynamic Scheduling Method for Collaborated Cloud with Thick Clients [J]. The international arab journal of information technology, 2019, 16(4):633-643.