Joint Optimization of Resource Allocation and Workload Scheduling for Cloud based Multimedia Services

With the development of cloud technology, cloud computing has been increasingly used as distributed platforms for multimedia services. However there are two fundamental challenges for service providers: one is resource allocation, and the other is workload scheduling. Due to the rapidly varying workload and strict response time requirement, it is difficult to optimally allocate virtual machines (VMs) and assign workload. In this paper, we study the resource allocation and workload scheduling problem for cloud based multimedia services. Specifically, we introduce a queuing model to quantify the resource demands and service performance, and a directed acyclic graph (DAG) model to characterize the precedence constraints among jobs. Based on the proposed models, we jointly optimize the allocated VMs and the assigned workload to minimize the total resource cost under the response time constraints. Since the formulated problem is mixed integer non-linear programming, a heuristic is proposed to efficiently allocate resources for practical services. Experimental results show that the proposed scheme can effectively allocate VMs and schedule workload to achieve the minimal resource cost.