An Adaptive Cloud Downloading Service
Abstract
An Adaptive Cloud Downloading Service.Video content downloading using the P2P approach is scalable, but does not always give good performance. Recently, subscription-based premium services have emerged, referred to as cloud downloading. In this service, the cloud storage and server caches user-interested content and updates the cache based on user downloading requests. If a requested video is not in the cache, the request is held in a waiting state until the cache is updated. We call this design server mode. An alternative design is to let the cloud server serve all downloading requests as soon as they arrive, behaving as a helper peer. We call this design helper mode. Our model and analysis show that both these designs are useful for certain operating regimes.
Conclusion
In this An Adaptive Cloud Downloading Service work, we build a theoretical model to analyze different strategies for a cloud downloading system. In particular, helper mode and server mode are used as abstraction of two different design philosophies—using the cloud as peer or as server. Our analysis reveals that each strategy can be advantages, for certain operating scenarios. Helper mode wastes some server bandwidth, but is best at leveraging P2P capacity when request load is high. On the other hand, server mode is most efficient for dealing with large video population relative to the cache size. We design an automatic mode selection (AMS) algorithm to choose the suitable service mode for different scenarios.
