Research On Replication Policy For Clustered Streaming Media System

Recent advances in high-speed network technology,dynamic video data compression and data decompression technology,bulk storage technology,and the increasing demand of network multimedia,accelerates the birth and development of streaming media.Streaming media is the multimedia transferred over network by stream technology. Stream technology,which compressing continuous audio or video program and placing them to the web server,can make the clints have no use for downloading the whole program from the server.The program can be listened and watched when it is being downloaded.The clustered server system,consisting a group of distributed streaming servers,is superior to the centralized structure in three aspects:good scalability, high availability,and competitive performance-to-price ratio.Replica Placement Problem(RPP),which involving how to produce the replicas of data or files,how to distribute the replicas to the servers and how to replace the old replicas with demanding replicas,et.,has the goal of enhancing the performance of the system.Typically, RPP formulations fall into two categories:static and dynamic.Our research focuses on replication policy or content distribution for clustered server system.Firstly,the optimizing problem of content distribution which minimizes the blocking probability and storage consumption on clustered streaming media system is discussed, in the case of knowing every program' s unchanged popularity.The queuing theory is adopted to analysis the relationship between the server' s access probability and the optimizing goal.The ideal access probability of every server can be obtained by some numerical methods,under the circumstance of minimal blocking probability. Content distribution determining each server' s access probability,has been proved to be NP-Hard.The whole content distribution process consists of three strategies,i.e. initial allocating,duplicate swapping and peer duplicate' s access probability adjusting. All the heuristic arithmetic is designed to perform the content distribution in order to minimize the distance between the result of optimization and the ideal one,minimize the storage consumption and reduce the blocking probability.Lastly the correctness of system modeling and the efficiency of proposed arithmetic are verified by numerical analysis and discrete event simulation.Secondly,the problem of dynamic content deployment for clustered streaming media system consisting of homogeneous servers is also discussed in this paper.We propose a dynamic storage balancing(DSB) based content updating strategy to lower the service blocking probability,to balance the load of media servers and to reduce the update cost.A relatively short time window is adopted to predict the changing popularity of all multimedia files.Unlike other exiting policies applying the predicted result to replicating and de-replicating,we assign it properly to each replica in the cluster for the purpose of unifying every server' s access probability(AP),in other words,making the system be in a condition of DSB.Then we gradually replicate the newly popular objects to enhance the ability of DSB.Meanwhile,we adopt a dereplication strategy with zero in-service request migration cost to avoid storage space being exhausted by replicas of previously popular objects.The simulation analysis shows that the proposed policy can achieve an outstanding system performance.Lastly,a novel simulation platform for server cluster is brought forward which is based on queueing theory.The component elements of the platform may be assembled easily to execute various simulation because of their relatively independence.Since the function of each sanction is subdivided strictly,the whole platform which is in accordance with the actual system can guarantee the clarity and fairness of different policies.