An energy-optimization-based method of task scheduling for a cloud video surveillance center

The number of cloud video surveillance (CVS) systems has been increasing rapidly over the last decade. Since CVS systems are big energy consumers, it is urgent to take the problem of optimizing the energy consumption of CVS systems into consideration. In this study, we build a task scheduling model, and present a method of scheduling that minimizes energy consumption by reducing the number of virtual machines. The optimization problem is first formulated as a multi-dimensional bin-packing problem due to the constrains on the resources (sizes of the bandwidth, the memory, the hard disk, the CPU utilization, etc.). We convert the problem into a one-dimensional bin-packing problem by making use of the relationships between the resources, and solve it using the greedy best-fit search algorithm. This method greatly reduces the computational expense and can be used in a real-time fashion. An experimental system is designed to evaluate the method, and four experiments are carried out to demonstrate the validity of the method. Experimental results show that the method not only largely improved the resource utilization and reduces energy consumption but also the scheduling time was significantly decreased when handling the same number of video tasks. And it is obviously superior to the common approach and First Fit Decreasing (FFD) algorithm.