Redundancy is necessary for a storage system to recover from errors. The frequent errors in large-scale systems, e.g. cloud, make it desired to reduce the recovery cost. Among all kinds of data stored in the cloud, video takes a large portion due to its large data volume. The other characteristic of video is that a certain distortion can be tolerated. This paper investigates using scalable video representation and unequal error protection scheme to reduce the storage and recovery costs in the cloud. By introducing more protection for the base layer and less on the enhancement layers, it can achieve a better tradeoff between storage and reconstruction costs although the reliability for the enhancement layer sacrifices a little. Simulation results based on local reconstruction codes (LRC) show that comparing with the existing (12, 2, 2) LRC code in Windows Azure Storage, the reconstruction cost can be reduced from 6x to 3x at the same storage cost at the expense of possible video quality loss.<\/p>\n","protected":false},"excerpt":{"rendered":"
Redundancy is necessary for a storage system to recover from errors. The frequent errors in large-scale systems, e.g. cloud, make it desired to reduce the recovery cost. Among all kinds of data stored in the cloud, video takes a large portion due to its large data volume. The other characteristic of video is that a 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