Dynamic Idle Time Interval Scheduling for Hybrid Cloud Workflow Management System

Abstract

To reduce the operating cost, leasing appropriate amount of public resources becomes a popular practice among small and medium sized enterprises. Many hybrid cloud workflow management systems (HCWMSs) have been developed to provision applications on both local and rented resources. One of the critical issues in the HCWMS is the dynamic resource allocation for stochastically arriving requests. Therefore, we propose a dynamic interval scheduling based heuristic for the resource allocation problem, in which stochastic requests are taken as a set of linearly dependent tasks and distributed to idle and feasible time slots on multiple virtual machines (VMs), either local or rented VMs. The objective is to minimize the idle time slots on the rented VMs, which is relative to the renting cost of VMs, especially for the on-demand pricing structure. Requests arrive at the same time are taken as a batch of tasks to schedule. Tasks are scheduled batch by batch, obeying the precedence constraint and the deadline constraint. We develop a fast heuristic integrated with an interval scheduling to obtain feasible and effective solutions. Three interval scheduling method are proposed and compared: Max Interval Number Scheduling (MINS), Max Working Time Scheduling (MWTS) and Select-the-better Method (STBM). The experimental results show that the interval scheduling based heuristic can reduces the cost of renting VMs.