As cloud computing platform provides computing power as utilities, it is important to develop a mechanism to adaptively adjust the resources needed for handling cloud service. In this paper, a computing resource minimization framework for cloud-based surveillance video analysis systems is proposed. Videos streams are divided into clips and multiple processing nodes are used to handle clips. While the quality-of-service (QoS) is maintained, the proposed framework dynamically adjusts the number of processing nodes based on a proposed content-aware workload estimation mechanism. Experimental results show that the proposed mechanism successfully predicts the variability of system workload while QoS is maintained and outperforms other mechanisms in terms of average virtual machine (VM) quantity and job failure ratio.

Although random access operations are desirable for on-demand video streaming in peer-to-peer systems, they are difficult to efficiently achieve due to the asynchronous interactive behaviors of users and the dynamic nature of peers. In this paper, we propose a network coding equivalent content distribution (NCECD) scheme to efficiently handle interactive video-on-demand (VoD) operations in peer-to-peer systems. In NCECD, videos are divided into segments that are then further divided into blocks. These blocks are encoded into independent blocks that are distributed to different peers for local storage. With NCECD, a new client only needs to connect to a sufficient number of parent peers to be able to view the whole video and rarely needs to find new parents when performing random access operations. In most existing methods, a new client must search for parent peers containing specific segments; however, NCECD uses the properties of network coding to cache equivalent content in peers, so that one can pick any parent without additional searches. Experimental results show that the proposed scheme achieves low startup and jump searching delays and requires fewer server resources. In addition, we present the analysis of system parameters to achieve reasonable block loss rates for the proposed scheme.