Mobile-edge cloud computing is a new paradigm to provide cloud computing capabilities at the edge of pervasive radio access networks in close proximity to mobile users. In this paper, we first study the multi-user computation offloading problem for mobile-edge cloud computing in a multi-channel wireless interference environment. We show that it is NP-hard to compute a centralized optimal solution, and hence adopt a game theoretic approach for achieving efficient computation offloading in a distributed manner. We formulate the distributed computation offloading decision making problem among mobile device users as a multi-user computation offloading game. We analyze the structural property of the game and show that the game admits a Nash equilibrium and possesses the finite improvement property. We then design a distributed computation offloading algorithm that can achieve a Nash equilibrium, derive the upper bound of the convergence time, and quantify its efficiency ratio over the centralized optimal solutions in terms of two important performance metrics. We further extend our study to the scenario of multi-user computation offloading in the multi-channel wireless contention environment. Numerical results corroborate that the proposed algorithm can achieve superior computation offloading performance and scale well as the user size increases.

Breakthroughs from the field of deep learning are radically changing how sensor data are interpreted to extract the high-level information needed by mobile apps. It is critical that the gains in inference accuracy that deep models afford become embedded in future generations of mobile apps. In this work, we present the design and implementation of DeepX, a software accelerator for deep learning execution. DeepX significantly lowers the device resources (viz. memory, computation, energy) required by deep learning that currently act as a severe bottleneck to mobile adoption. The foundation of DeepX is a pair of resource control algorithms, designed for the inference stage of deep learning, that: (1) decompose monolithic deep model network architectures into unit-blocks of various types, that are then more efficiently executed by heterogeneous local device processors (e.g., GPUs, CPUs); and (2), perform principled resource scaling that adjusts the architecture of deep models to shape the overhead each unit-blocks introduces. Experiments show, DeepX can allow even large-scale deep learning models to execute efficiently on modern mobile processors and significantly outperform existing solutions, such as cloud-based offloading.

Content-Centric Networks (CCN) provide substantial flexibility for users to obtain information without regard to the source of the information or its current location. Publish/ subscribe (pub/sub) systems have gained popularity in society to provide the convenience of removing the temporal dependency of the user having to indicate an interest each time he or she wants to receive a particular piece of related information. Currently, on the Internet, such pub/sub systems have been built on top of an IP-based network with the additional responsibility placed on the end-systems and servers to do the work of getting a piece of information to interested recipients. We propose Content-Oriented Pub/Sub System (COPSS) to achieve an efficient pub/sub capability for CCN. COPSS enhances the heretofore inherently pullbased CCN architectures proposed by integrating a push based multicast capability at the content-centric layer.We emulate an application that is particularly emblematic of a pub/sub environment-Twitter-but one where subscribers are interested in content (e.g., identified by keywords), rather than tweets from a particular individual. Using trace-driven simulation, we demonstrate that our architecture can achieve a scalable and efficient content centric pub/sub network. The simulator is parameterized using the results of careful microbenchmarking of the open source CCN implementation and of standard IP based forwarding. Our evaluations show that COPSS provides considerable performance improvements in terms of aggregate network load, publisher load and subscriber experience compared to that of a traditional IP infrastructure.

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Abstract-Socially aware services often have a large user base and data of users have to be partitioned and replicated over multiple geographically distributed clouds. Choosing in which cloud to place data, however, is difficult. Effective data placements entail meeting multiple system objectives, including reducing the usage of cloud resources, providing good service quality to users, and even minimizing the carbon footprint, while facing critical challenges such as the interconnection of social data, the conflicting requirements of different objectives, and the customized multi-cloud data access policies.In this paper, we study multi-objective optimization for placing users' data over multiple clouds for socially aware services. We build a model framework that can accommodate a range of different objectives, and based on this model we formulate the optimization problem. Leveraging graph cuts, we propose an optimization approach that decomposes our original problem into two simpler subproblems and solves them alternately in multiple rounds. We carry out evaluations using a large group of realworld geographically distributed users with realistic interactions, and place users' data over 10 clouds all across the US. We demonstrate results that are significantly superior to standard and de facto methods in all objectives, and also show that our approach is capable of exploring trade-offs among objectives, converges fast and scales to a huge user base.

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