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Interactive mobile applications attract lots of attentions recently. They utilize complex algorithms (e.g., machine learning) to provide advanced functions (e.g., object recognition), thus lead to long response time while running on mobile devices. To reduce the response time, researchers propose offloading some compute-intensive parts of mobile applications onto cloud. Existing works aim to optimize general performance (e.g., response time), but ignore the enhancement of application quality (e.g., recognition accuracy), which is also critical to user experience. In this paper, we develop AppBooster, a mobile cloud platform which boosts both general performance and application quality for interactive mobile applications. AppBooster jointly leverages the quality adaptation, computation offloading and parallel speedup to boost the comprehensive performance, which is defined by developers based on the metrics of application quality and general performance. Through combining history-based platform-learned knowledge, developer-provided information and the platform-monitored environment conditions (e.g., workload, network), AppBooster manages applications with optimal computation partitioning scheme and tunable parameter setting thus obtain high comprehensive performance. We evaluate AppBooster with an object recognition application in various network conditions and show AppBooster can significantly boost application performance and obtain 1.3 to 3.5 times better performance than existing strategies. [ABSTRACT FROM AUTHOR]

The pay-as-you-go service model impels cloud customers to reduce the usage cost of bandwidth. Traffic Redundancy Elimination (TRE) has been shown to be an effective solution for reducing bandwidth costs, and thus has recently captured significant attention in the cloud environment. By studying the TRE techniques in a trace driven approach, we found that both short-term (time span of seconds) and long-term (time span of hours or days) data redundancy can concurrently appear in the traffic, and solely using either sender-based TRE or receiver-based TRE cannot simultaneously capture both types of traffic redundancy. Also, the efficiency of existing receiver-based TRE solution is susceptible to the data changes compared to the historical data in the cache. In this paper, we propose a Cooperative end-to-end TRE solution (CoRE) that can detect and remove both short-term and long-term redundancy through a two-layer TRE design with cooperative operations between layers. An adaptive prediction algorithm is further proposed to improve TRE efficiency through dynamically adjusting the prediction window size based on the hit ratio of historical predictions. Besides, we enhance CoRE to adapt to different traffic redundancy characteristics of cloud applications to improve its operation cost. Extensive evaluation with several real traces show that CoRE is capable of effectively identifying both short-term and long-term redundancy with low additional cost while ensuring TRE efficiency from data changes. [ABSTRACT FROM PUBLISHER]