Tongmao Ma^1,2, Shanchen Pang¹, Weiguang Zhang¹, Shaohua Hao¹

Intelligent Automation & Soft Computing, Vol.25, No.3, pp. 605-613, 2019, DOI:10.31209/2019.100000115

Abstract In cloud computing, task scheduling is a challenging problem in cloud data center, and there are many different kinds of task scheduling strategies. A good scheduling strategy can bring good effectiveness, where plenty of parameters should be regulated to achieve acceptable performance of cloud computing platform. In this work, combined elitist strategy, three parameters values oriented genetic algorithms are proposed. Specifically, a model built by Generalized Stochastic Petri Nets (GSPN) is introduced to describe the process of scheduling in cloud datacenter, and then the workflow of the algorithms is showed. After that, the effectiveness of the algorithms is found to… More >

Amit Chhabra^{1, *}, Gurvinder Singh², Karanjeet Singh Kahlon²

CMC-Computers, Materials & Continua, Vol.64, No.2, pp. 813-834, 2020, DOI:10.32604/cmc.2020.010934

Abstract Cloud computing infrastructure has been evolving as a cost-effective platform for providing computational resources in the form of high-performance computing as a service (HPCaaS) to users for executing HPC applications. However, the broader use of the Cloud services, the rapid increase in the size, and the capacity of Cloud data centers bring a remarkable rise in energy consumption leading to a significant rise in the system provider expenses and carbon emissions in the environment. Besides this, users have become more demanding in terms of Quality-of-service (QoS) expectations in terms of execution time, budget cost, utilization, and makespan. This situation calls… More >