TailoredRE: A Personalized Cloud-based Traffic Redundancy Elimination for Smartphones

The exceptional rise in usages of mobile devices such as smartphones and tablets has contributed to a massive increase in wireless network trac both Cellular (3G/4G/LTE) and WiFi. The unprecedented growth in wireless network trac not only strain the battery of the mobile devices but also bogs down the last-hop wireless access links. Interestingly, a signicant part of this data trac exhibits high level of redundancy in them due to repeated access of popular contents in the web. Hence, a good amount of research both in academia and in industries has studied, analyzed and designed diverse systems that attempt to eliminate redundancy in the network trac. Several of the existing Trac Redundancy Elimination (TRE) solutions either does not improve last-hop wireless access links or involves inecient use of compute resources from resource-constrained mobile devices. In this research, we propose TailoredRE, a personalized cloud-based trac redundancy elimination system. The main objective of TailoredRE is to tailor TRE mechanism such that TRE is performed against selected applications rather than application agnostically, thus improving eciency by avoiding caching of unnecessary data chunks. In our system, we leverage the rich resources of the cloud to conduct TRE by ooading most of the operational cost from the smartphones or mobile devices to its clones (proxies) available in the cloud. We cluster the multiple individual user clones in the cloud based on the factors of connectedness among users such as usage of similar applications, common interests in specic web contents etc., to improve the eciency of caching in the cloud. This thesis encompasses motivation, system design along with detailed analysis of the results obtained through simulation and real implementation of TailoredRE system

The Effect of Packet Loss on Redundancy Elimination in Cellular Wireless Networks

Network-level redundancy elimination (RE) algorithms reduce traffic volume on bandwidth-constrained network paths by avoiding the transmission of repeated byte sequences. Previous work shows that RE can suppress the transmission of 20-50 % bytes when deployed at ISP access links or between routers. In this paper, we focus on the challenges of deploying RE in cellular networks. The potential benefit is substantial, since cellular networks have a growing subscriber base and network links, including wired backhaul, are often oversubscribed. Using three large traces captured at two North American and one European wireless network providers, we show that RE can reduce the bandwidth consumption of the majority of mobile users by at least 10%. However, cellular links have much higher packet loss rates than their wired counterparts, which makes applying RE much more difficult. Our experiments also show that the loss of only a few packets can disrupt RE and eliminate the bandwidth savings. We propose informed marking, a lightweight scheme that detects lost packets and prevents RE algorithms from using them for future encodings. We implement RE with informed marking and deploy it in a real-world cellular network. Our results show that with informed marking, more than 60 % of the bandwidth savings of RE are preserved, even when packet loss rates are high. Categories and Subject Descriptors C.2.1 [Computer-communication networks]: Network architecture and design; C.2.3 [Computer-communication networks]: Network operations; C.4 [Performance of systems]: Performance attribute

Characterization and Optimization of Resource Utilization for Cellular Networks.

Cellular data networks have experienced significant growth in the recent years particularly due to the emergence of smartphones. Despite its popularity, there remain two major challenges associated with cellular carriers and their customers: carriers operate under severe resource constraints, while many mobile applications are unaware of the cellular specific characteristics, leading to inefficient radio resource and handset energy utilization. My dissertation is dedicated to address both challenges, aiming at providing practical, effective, and efficient methods to monitor and to reduce the resource utilization and bandwidth consumption in cellular networks. Specifically, from carriers' perspective, we performed the first measurement study to understand the state-of-the-art of resource utilization for a commercial cellular network, and revealed that fundamental limitation of the current resource management policy is treating all traffic according to the same resource management policy globally configured for all users. On mobile applications' side, we developed a novel data analysis framework called ARO (mobile Application Resource Optimizer), the first tool that exposes the interaction between mobile applications and the radio resource management policy, to reveal inefficient resource usage due to a lack of transparency in the lower-layer protocol behavior. ARO revealed that many popular applications built by professional developers have significant resource utilization inefficiencies that are previously unknown. Motivated by the observations from both sides, we further proposed a novel resource management framework that enables the cooperation between handsets and the network to allow adaptive resource release, therefore better balancing the key tradeoffs in cellular networks. We also investigated the problem of reducing the bandwidth consumption in cellular networks by performing the first network-wide study of HTTP caching on smartphones due to its popularity. Our findings suggest that for web caching, there exists a huge gap between the protocol specification and the protocol implementation on today's mobile devices, leading to significant amount of redundant network traffic.PHDComputer Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/94024/1/fengqian_1.pd