A Failure and Overload Tolerance Mechanism for ContinuousMedia

{Large scale clustered continuous media (CM) servers deployed in applications like video-on-demand have high availability requirements. In the event of server failure, streams from the failed servers must be reassigned to healthy servers with minimum service disruption. Such servers may also suuer from periods of transient overload resulting from a high degree of customer interactivity. For example, in a video-on-demand system if a large number of users are viewing a favorite game, many of them could simultaneously request a replay of an interesting part of the game. This requires a large number of \interactive" channels within a short period of time and can result in a transient server overload. In this paper we propose solutions for graceful recovery from overload scenarios arising out of server failure or customer interactions. Rapid resource reclamation is key to overload tolerance, and our proposed solution is based on rate adaptive stream merging and content insertion techniques. We also utilize conventional time-sharing techniques to handle transient overload. We show that while merging is necessary for achieving overload tolerance, it is not suucient, and for a complete solution, content insertion is required. Speciically, we consider a general clustered CM server architecture model where multiple servers can fail simultaneously. We develop a model for resource shortfalls that occur as a result of overload on failure. We also describe optimal polynomial time algorithms for recovering resources to the maximum extent possible, by clustering streams in real time.