Mobile edge computing (MEC) has recently emerged as a promising technology to release the tension between computation-intensive applications and resource-limited mobile terminals (MTs). In this paper, we study the delay-optimal computation offloading in computation-constrained MEC systems. We consider the computation task queue at the MEC server due to its constrained computation capability. In this case, the task queue at the MT and that at the MEC server are strongly coupled in a cascade manner, which creates complex interdependencies and brings new technical challenges. We model the computation offloading problem as an infinite horizon average cost Markov decision process (MDP), and approximate it to a virtual continuous time system (VCTS) with reflections. Different to most of the existing works, we develop the dynamic instantaneous rate estimation for deriving the closed-form approximate priority functions in different scenarios. Based on the approximate priority functions, we propose a closed-form multi-level water-filling computation offloading solution to characterize the influence of not only the local queue state information (LQSI) but also the remote queue state information (RQSI). Furthermore, we discuss the extension of our proposed scheme to multi-MT multi-server scenarios. Finally, the simulation results show that the proposed scheme outperforms the conventional schemes.

Cache-enabled network architecture has great potential for enhancing the efficiency of content distribution as well as reducing the network congestion. This, in turn, has called for joint optimization of traffic engineering and caching strategies while considering both network congestion and content demands. In this paper, we present a distributed framework for joint request/data forwarding and dynamic cache placement in cache-enabled networks. Specifically, to retrieve the information about content demands and network congestion over the network, we establish a dual queue system for both requests and data, and define a dynamic mapping between the two queues with the help of dummy data such that the nodes can determine packet forwarding and caching strategies based only on local information. As the local objective function associated with Lyapunov optimization is time-varying due to the stochastic evolution of request/data queues, we develop a low-complexity distributed forwarding and caching algorithm via stochastic network utility maximization. We also prove the proposed algorithm achieves queue stability, and derive its region stability property for time-varying local optimization to demonstrate the convergence behavior. The simulation results verify queue stability and shows the proposed algorithm outperforms the existing ones.

To alleviate the local computation demands from the ever-increasing computation-intensive mobile applications, Mobile Edge Computing (MEC) has proved promising. Especially, by opportunistically offloading these computation tasks to the MEC server, the delay of computing could be significantly improved through communication. In this paper, we develop an analytical framework for joint communication and computation resources allocation for multi-user MEC systems. Specifically, to retrieve the combined effect of communication and computation capabilities, we establish a dual queue system, including a data queue sub-system and a computation queue sub-system. To address the associated stochastic resource optimization problem, we propose a low-complexity resource allocation algorithm by Lyapunov optimization to stabilize all the sub-queue systems. As the practical buffers are finite, the conventional delay analysis of Lyapunov optimization becomes inaccurate. Alternatively, we model the stochastic queue lengthes as discrete time controlled random walk processes, which are transformed to continuous time Stochastic Differential Equations (SDEs) with reflections by strong approximation. According to the steady state analysis on the SDEs, we derive closed-form steady state distributions of the queue lengths, and then obtain the average delay performance with finite buffers. Finally, the accuracy of the proposed delay analysis is verified through simulation.

Offloading the computationally intensive workloads to the edge and cloud not only improves the quality of computation, but also creates an extra degree of diversity by collecting information from devices in service. Nevertheless, significant concerns on privacy are raised as the aggregated information could be misused without the permission by the third party. Sparse coding, which has been successful in computer vision, is finding application in this new domain. In this paper, we develop a secured face recognition framework to orchestrate sparse coding in edge and cloud networks. Specifically, 1). To protect the privacy, a low-complexity encrypting algorithm is developed based on random unitary transform, where its influence on dictionary learning and sparse representation is analysed. Furthermore, it is proved that such influence will not affect the accuracy of face recognition. 2). To fully utilize the multi-device diversity and avoid big data transmission between edge and cloud, a distributed learning framework is established, which extracts deeper features in an intermediate space, expanded according to the dictionaries from each device. Classification is performed in this new feature space to combat the noise and modeling error. Finally, the efficiency and effectiveness of the proposed framework is demonstrated through simulation results.

Mobile Edge Computing (MEC) provides a new approach to satisfy the requirements of delay-sensitive and computation-intensive applications by trading communicational capability for computational capability. Besides seeking for additional physical resources for improving performance, such as Device-to-Device (D2D) paradigm and Intelligent Reflecting Surface (IRS), with Machine Learning (ML) and Artificial Intelligence (AI), information forecast brings a new dimension to unleash the potential of MEC. However, most existing literature does not tailor information forecast algorithm to the MEC context, which diminishes the utility of the forecast information. In this work, we propose a framework for adaptive task offloading based on channel forecast for MEC system. Specifically, we first propose an online resource allocation algorithm with forecast information through Lyapunov optimization. Then, the performance in terms of delay and power consumption is analyzed in closed-form, based on which critical insight towards the requirements on forecast algorithm is obtained. Next, based on Sparse Gaussian Process and Linear Model of Coregionalization (SGP-LMC), we propose a channel forecast algorithm to find a desired trade-off between forecast accuracy and model complexity in terms of maximizing the utility of forecast information. Finally, simulation is performed to verify the performance improvements.