Achieving cost effective cloud video services via fine grained multicore scheduling

Abstract

Cloud computing that possesses highly accessible and elastic computing resources perfectly matches the demands of video services, which employ massive storage and intensive computational power to store, transmit, compress, enhance, and analyze the videos, uploaded from commodity devices and surveillance cameras. However, most existing video processing programs are neither designed to run on parallel environments nor able to efficiently utilize the computational power of cloud platforms, which not only wastes the computing resources but also increases the cost of using cloud platforms. In this paper, we present three strategies to enhance the multicore utilization for video processing, namely producer-consumer model, intra-process overlapping, and inter-process overlapping. We experimented our strategies on a video enhancement program, which performs decoding, dehazing, and encoding, and the results showed the CPU utilization can be improved up to 31% for an 8 core instance, which can significantly reduce the cost in a long run.