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 signif- icantly 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 efficently on modern mobile processors and significantly outperform existing solutions, such as cloud-based offloading.

While social Virtual Reality (VR) applications such as Facebook Spaces are becoming popular, they are not compatible with classic mobile-or cloud-based solutions due to their processing of tremendous data and exchange of delay-sensitive metadata. Edge computing may fulfill these demands better, but it is still an open problem to deploy social VR applications in an edge infrastructure while supporting economic operations of the edge clouds and satisfactory quality-of-service for the users. This paper presents the first formal study of this problem. We model and formulate a combinatorial optimization problem that captures all intertwined goals. We propose ITEM, an iterative algorithm with fast and big “moves” where in each iteration, we construct a graph to encode all the costs and convert the cost optimization into a graph cut problem. By obtaining the minimum s-t cut via existing max-flow algorithms, we can simultaneously determine the placement of multiple service entities, and thus, the original problem can be addressed by solving a series of graph cuts. Our evaluations with large-scale, real-world data traces demonstrate that ITEM converges fast and outperforms baseline approaches by more than 2 × in one-shot placement and around 1.3 × in dynamic, online scenarios where users move arbitrarily in the system.

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 pull-based 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 micro benchmarking 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.

In this paper, we advocate edge caching in Cloud Radio Access Networks (C-RAN) to facilitate the ever-increasing mobile multimedia services. In our framework, central offices will cooperatively allocate cloud resources to cache popular contents and satisfy user requests for those contents, so as to minimize the system costs in terms of storage, VM reconfiguration, content access latency, and content migration. However, this joint resource allocation, content placement and request routing is nontrivial, since it needs to be continuously adjusted to accommodate system dynamics, such as user movement and content slashdot effect, while taking into account the time-correlated adjustment costs for VM reconfiguration and content migration. To this end, we build a comprehensive model to capture the key components of edge caching in C-RAN, and formulate a joint optimization problem, aiming at minimizing the system costs over time, and meanwhile satisfying the time-varying user requests and respecting various practical constraints (e.g., storage and bandwidth). Then, we propose a novel online approximation algorithm by resorting to the regularization, rounding and decomposition technique, which can be proved to have a parameterized competitive ratio with a polynomial running time. Extensive trace-driven simulations corroborate the efficiency, flexibility and lightweight of our proposed online algorithm, for instance it achieves an empirical competitive ratio around 2 – 4, gains over 30% improvement compared with many state-of-the-art algorithms in various system settings.