WITHDRAWN: Real-time Multiple-Workflow Scheduling in Cloud Environment

Abstract

With the development of cloud computing, an increasing number of applications in different fields have been deployed to the cloud. In this process, the real-time scheduling of multiple workflows composed of tasks from these different applications must consider various influencing factors which strongly affect scheduling performance. This paper proposes a real-time multiple-workflow scheduling (RMWS) scheme to schedule workflows dynamically with minimum cost under different deadline constraints. Due to the uncertainty of workflow arrival time and specification, RMWS dynamically allocates tasks and divides the scheduling process into three stages. First, when a new workflow arrives, the latest start time and the latest finish time of each task are calculated according to the deadline, and the subdeadline of each task is obtained by probabilistic upward ranking. Then, each ready task is allocated according to its subdeadline and the increased cost of the virtual machine (VM). Meanwhile, only one waiting task can be assigned to each VM to reduce delay fluctuations. Finally, when the task is completed on the assigned VM, all the parameters of the relevant tasks are updated before allocating them to appropriate VMs. The experimental results based on four real-world workflow traces show that the proposed algorithm is superior to two state-of-the-art algorithms in terms of total rental cost, resource utilization, success rate and deadline deviation under different conditions.

A Comparative Analysis of Passenger Flow Forecasting in Trams Using Machine Learning Algorithms

“…Gao et al [39] proposed a method to increase the efficacy of software-defined devices, while they also introduced a technology to transform business process execution language (BPEL) into timed automata for formal verification, bridging BPEL and IoT data in support of prediction tasks [40]. Huang et al [41] optimized virtual machine allocation strategies for cloud data centers, and Ma et al [42] proposed a real-time multiple workflow scheduling method in a cloud environment, enhancing the processing efficacy of large data sets for passenger flow source data analysis.…”