Cloud Assisted Traffic Redundancy Elimination for Power Efficiency in Smartphones

Abstract

The exceptional increase in the usage of smartphones has contributed to a massive increase in data traffic from application servers to the smartphones, which not only strains their computation capacities and batteries but also bogs down the last hop in data transmission. For this problem, traffic redundancy elimination (TRE) is an effective solution, in which a chunk to be transmitted could be directly fetched from the receiver's cache. However, existing TRE solutions either cannot be directly applied to or are not suitable for smartphones due to high computing and energy overhead imposed on smartphones. To address this problem, in this paper, we propose a novel TRE system, called TailoredRE, which consists of three components. First, each smartphone has a clone in the cloud that is responsible for computation intensive tasks including parsing traffic and detecting redundancy. Second, considering that each mobile user has certain applications (e.g., YouTube) to use in daily life, each smartphone's clone selectively chooses the applications that are most frequently used by the user and also have high redundancy ratios to cache data. Third, considering that some users always have common favorite applications, TailoredRE clusters their clones together to cooperatively conduct the redundancy detection task in order to reduce the cache resource consumption in the cloud. We collected traces from eleven applications including Web Browser, YouTube, CNN, Quora, Instagram and Facebook, and used the traces in simulation. We also implemented and open-sourced TailoredRE and conducted prototype-based experiments. Experiment results show that TailoredRE can achieve much higher cache hit rate, end-to-end throughput, bandwidth saving and energy efficiency compared with previous TRE methods.