Optimization of Radio and Computational Resources for Energy Efficiency in Latency-Constrained Application Offloading

Providing femto-access points (FAPs) with computational capabilities will allow (either total or partial) offloading of highly demanding applications from smart-phones to the so called femto-cloud. Such offloading promises to be beneficial in terms of battery saving at the mobile terminal (MT) and/or latency reduction in the execution of applications, whenever the energy and/or time required for the communication process are compensated by the energy and/or time savings that result from the remote computation at the FAPs. For this problem, we provide in this paper a framework for the joint optimization of the radio and computational resource usage exploiting the tradeoff between energy consumption and latency, and assuming that multiple antennas are available at the MT and the serving FAP. As a result of the optimization, the optimal communication strategy (e.g., transmission power, rate, precoder) is obtained, as well as the optimal distribution of the computational load between the handset and the serving FAP. The paper also establishes the conditions under which total or no offloading are optimal, determines which is the minimum affordable latency in the execution of the application, and analyzes as a particular case the minimization of the total consumed energy without latency constraints.Comment: Accepted to be published at IEEE Transactions on Vehicular Technology (acceptance: November 2014

Pushing AI to Wireless Network Edge: An Overview on Integrated Sensing, Communication, and Computation towards 6G

Pushing artificial intelligence (AI) from central cloud to network edge has reached board consensus in both industry and academia for materializing the vision of artificial intelligence of things (AIoT) in the sixth-generation (6G) era. This gives rise to an emerging research area known as edge intelligence, which concerns the distillation of human-like intelligence from the huge amount of data scattered at wireless network edge. In general, realizing edge intelligence corresponds to the process of sensing, communication, and computation, which are coupled ingredients for data generation, exchanging, and processing, respectively. However, conventional wireless networks design the sensing, communication, and computation separately in a task-agnostic manner, which encounters difficulties in accommodating the stringent demands of ultra-low latency, ultra-high reliability, and high capacity in emerging AI applications such as auto-driving. This thus prompts a new design paradigm of seamless integrated sensing, communication, and computation (ISCC) in a task-oriented manner, which comprehensively accounts for the use of the data in the downstream AI applications. In view of its growing interest, this article provides a timely overview of ISCC for edge intelligence by introducing its basic concept, design challenges, and enabling techniques, surveying the state-of-the-art development, and shedding light on the road ahead

The edge cloud: A holistic view of communication, computation and caching

The evolution of communication networks shows a clear shift of focus from just improving the communications aspects to enabling new important services, from Industry 4.0 to automated driving, virtual/augmented reality, Internet of Things (IoT), and so on. This trend is evident in the roadmap planned for the deployment of the fifth generation (5G) communication networks. This ambitious goal requires a paradigm shift towards a vision that looks at communication, computation and caching (3C) resources as three components of a single holistic system. The further step is to bring these 3C resources closer to the mobile user, at the edge of the network, to enable very low latency and high reliability services. The scope of this chapter is to show that signal processing techniques can play a key role in this new vision. In particular, we motivate the joint optimization of 3C resources. Then we show how graph-based representations can play a key role in building effective learning methods and devising innovative resource allocation techniques.Comment: to appear in the book "Cooperative and Graph Signal Pocessing: Principles and Applications", P. Djuric and C. Richard Eds., Academic Press, Elsevier, 201

Joint Data compression and Computation offloading in Hierarchical Fog-Cloud Systems

Data compression has the potential to significantly improve the computation offloading performance in hierarchical fog-cloud systems. However, it remains unknown how to optimally determine the compression ratio jointly with the computation offloading decisions and the resource allocation. This joint optimization problem is studied in the current paper where we aim to minimize the maximum weighted energy and service delay cost (WEDC) of all users. First, we consider a scenario where data compression is performed only at the mobile users. We prove that the optimal offloading decisions have a threshold structure. Moreover, a novel three-step approach employing convexification techniques is developed to optimize the compression ratios and the resource allocation. Then, we address the more general design where data compression is performed at both the mobile users and the fog server. We propose three efficient algorithms to overcome the strong coupling between the offloading decisions and resource allocation. We show that the proposed optimal algorithm for data compression at only the mobile users can reduce the WEDC by a few hundred percent compared to computation offloading strategies that do not leverage data compression or use sub-optimal optimization approaches. Besides, the proposed algorithms for additional data compression at the fog server can further reduce the WEDC

Latency Minimization for Multiuser Computation Offloading in Fog-Radio Access Networks

This paper considers computation offloading in fog-radio access networks (F-RAN), where multiple user equipments (UEs) offload their computation tasks to the F-RAN through a number of fog nodes. Each UE can choose one of the fog nodes to offload its task, and each fog node may simultaneously serve multiple UEs. Depending on the computation burden at the fog nodes, the tasks may be computed by the fog nodes or further offloaded to the cloud via capacity-limited fronthaul links. To compute all UEs tasks as fast as possible, joint optimization of UE-Fog association, radio and computation resources of F-RAN is proposed to minimize the maximum latency of all UEs. This min-max problem is formulated as a mixed integer nonlinear program (MINP). We first show that the MINP can be reformulated as a continuous optimization problem, and then employ the majorization minimization (MM) approach to finding a solution for it. The MM approach that we develop herein is unconventional in that---each MM subproblem is inexactly solved with the same provable convergence guarantee as the conventional exact MM. In addition, we also consider a cooperative offloading model, where the fog nodes compress-and-forward their received signals to the cloud. Under this model, a similar min-max latency optimization problem is formulated and tackled again by the inexact MM approach. Simulation results show that the proposed algorithms outperform some heuristic offloading strategies, and that the cooperative offloading is generally better than the non-cooperative one.Comment: 11 pages, 8 figure

Power-constrained edge computing with maximum processing capacity for IoT networks

Mobile edge computing (MEC) plays an important role in next-generation networks. It aims to enhance processing capacity and offer low-latency computing services for Internet of Things (IoT). In this paper, we investigate a resource allocation policy to maximize the available processing capacity (APC) for MEC IoT networks with constrained power and unpredictable tasks. First, the APC which describes the computing ability and speed of a served IoT device is defined. Then its expression is derived by analyzing the relationship between task partitioning and resource allocation. Based on this expression, the power allocation solution for the single-user MEC system with a single subcarrier is studied and the factors that affect the APC improvement are considered. For the multiuser MEC system, an optimization problem of APC with a general utility function is formulated and several fundamental criteria for resource allocation are derived. By leveraging these criteria, a binarysearch water-filling algorithm is proposed to solve the power allocation between local CPU and multiple subcarriers, and a suboptimal algorithm is proposed to assign the subcarriers among users. Finally, the validity of the proposed algorithms is verified by Monte Carlo simulation

A Scalable Energy vs Latency Trade-off in Full Duplex Mobile Edge Computing Systems

In this paper, we investigate the offloading energy and latency trade-off in a multiuser full-duplex (FD) system. We consider a multi-user FD system where a FD base station (BS), equipped with a mobile-edge computing (MEC) server, carries out data transmission in the downlink, while at the same time receiving computational tasks from mobile devices in the uplink. Our main aim is to study the trade-off between the offloading energy and latency, which are known to be very important and desirable system objectives for both the system operator and users. In practice, there always exist a trade-off between these two objectives. Towards this aim, we formulate two weighted multi-objective optimization problems (MOOPs), one, where the multi-user interference (MUI) is suppressed and the other, where MUI is rather exploited. As a result, our proposed MOOPs allow for a scalable tradeoff between the two objectives. To tackle the non-convexity of the formulations, we design an iterative algorithm through Lagrangian method. We also, address the scenario of imperfect channel state information (CSI) at the FD BS. For the imperfect CSI case, we apply convex relaxations and transformation using the S-procedure to tackle the non-convexity of the formulations. Simulation results show the effectiveness of the proposed FD schemes compared with the existing baseline half duplex schemes, and the superiority of MUI exploitation over suppression