Efficient algorithms of video replication and placement on a cluster of streaming servers

A cost-effective approach to building up scalable video streaming servers is to couple a number of streaming servers together in a cluster so as to alleviate the inherent storage and networking constraints of streaming services. In this article, we investigate a crucial problem of video replication and placement on a distributed storage cluster of streaming servers for high quality and high availability services. We formulate it as a combinatorial optimization problem with objectives of maximizing the encoding bit rate and the number of replicas of each video and balancing the workload of the servers. The objectives are subject to the constraints of the storage capacity and the outgoing network-I/O bandwidth of the servers. Under the assumption of single fixed encoding bit rate for all video objects with different popularity values, we give an optimal replication algorithm and a bounded placement algorithm, respectively. We further present an efficient replication algorithm that utilizes the Zipf-like video popularity distributions to approximate the optimal solutions, which can reduce the complexity of the optimal replication algorithm. For video objects with scalable encoding bit rates, we propose a heuristic algorithm based on simulated annealing. We conduct a comprehensive performance evaluation of the algorithms and demonstrate their effectiveness via simulations over a synthetic workload set.