Driven by the visions of Internet of Things and 5G communications, recent years have seen a paradigm shift in mobile computing, from the centralized mobile cloud computing toward mobile edge computing (MEC). The main feature of MEC is to push mobile computing, network control and storage to the network edges (e.g., base stations and access points) so as to enable computation-intensive and latency-critical applications at the resource-limited mobile devices. MEC promises dramatic reduction in latency and mobile energy consumption, tackling the key challenges for materializing 5G vision. The promised gains of MEC have motivated extensive efforts in both academia and industry on developing the technology. A main thrust of MEC research is to seamlessly merge the two disciplines of wireless communications and mobile computing, resulting in a wide-range of new designs ranging from techniques for computation offloading to network architectures. This paper provides a comprehensive survey of the state-of-the-art MEC research with a focus on joint radio-and-computational resource management. We also discuss a set of issues, challenges, and future research directions for MEC research, including MEC system deployment, cache-enabled MEC, mobility management for MEC, green MEC, as well as privacy-aware MEC. Advancements in these directions will facilitate the transformation of MEC from theory to practice. Finally, we introduce recent standardization efforts on MEC as well as some typical MEC application scenarios.

A Survey on Mobile Edge Computing: The Communication Perspective

Driven by the visions of Internet of Things and 5G communications, recent years have seen a paradigm shift in mobile computing, from the centralized Mobile Cloud Computing towards Mobile Edge Computing (MEC). The main feature of MEC is to push mobile computing, network control and storage to the network edges (e.g., base stations and access points) so as to enable computation-intensive and latency-critical applications at the resource-limited mobile devices. MEC promises dramatic reduction in latency and mobile energy consumption, tackling the key challenges for materializing 5G vision. The promised gains of MEC have motivated extensive efforts in both academia and industry on developing the technology. A main thrust of MEC research is to seamlessly merge the two disciplines of wireless communications and mobile computing, resulting in a wide-range of new designs ranging from techniques for computation offloading to network architectures. This paper provides a comprehensive survey of the state-of-the-art MEC research with a focus on joint radio-and-computational resource management. We also present a research outlook consisting of a set of promising directions for MEC research, including MEC system deployment, cache-enabled MEC, mobility management for MEC, green MEC, as well as privacy-aware MEC. Advancements in these directions will facilitate the transformation of MEC from theory to practice. Finally, we introduce recent standardization efforts on MEC as well as some typical MEC application scenarios.

Mobile-edge computing (MEC) is an emerging paradigm to meet the ever-increasing computation demands from mobile applications. By offloading the computationally intensive workloads to the MEC server, the quality of computation experience, e.g., the execution latency, could be greatly improved. Nevertheless, as the on-device battery capacities are limited, computation would be interrupted when the battery energy runs out. To provide satisfactory computation performance as well as achieving green computing, it is of significant importance to seek renewable energy sources to power mobile devices via energy harvesting (EH) technologies. In this paper, we will investigate a green MEC system with EH devices and develop an effective computation offloading strategy. The execution cost , which addresses both the execution latency and task failure, is adopted as the performance metric. A low-complexity online algorithm is proposed, namely, the Lyapunov optimization-based dynamic computation offloading algorithm, which jointly decides the offloading decision, the CPU-cycle frequencies for mobile execution, and the transmit power for computation offloading. A unique advantage of this algorithm is that the decisions depend only on the current system state without requiring distribution information of the computation task request, wireless channel, and EH processes. The implementation of the algorithm only requires to solve a deterministic problem in each time slot, for which the optimal solution can be obtained either in closed form or by bisection search. Moreover, the proposed algorithm is shown to be asymptotically optimal via rigorous analysis. Sample simulation results shall be presented to corroborate the theoretical analysis as well as validate the effectiveness of the proposed algorithm.

Dynamic Computation Offloading for Mobile-Edge Computing With Energy Harvesting Devices

The Transmission Control Protocol.

Mobile-edge computing (MEC) has recently emerged as a prominent technology to liberate mobile devices from computationally intensive workloads, by offloading them to the proximate MEC server. To make offloading effective, the radio and computational resources need to be dynamically managed, to cope with the time-varying computation demands and wireless fading channels. In this paper, we develop an online joint radio and computational resource management algorithm for multi-user MEC systems, with the objective of minimizing the long-term average weighted sum power consumption of the mobile devices and the MEC server, subject to a task buffer stability constraint. Specifically, at each time slot, the optimal CPU-cycle frequencies of the mobile devices are obtained in closed forms, and the optimal transmit power and bandwidth allocation for computation offloading are determined with the Gauss-Seidel method ; while for the MEC server, both the optimal frequencies of the CPU cores and the optimal MEC server scheduling decision are derived in closed forms. Besides, a delay-improved mechanism is proposed to reduce the execution delay. Rigorous performance analysis is conducted for the proposed algorithm and its delay-improved version, indicating that the weighted sum power consumption and execution delay obey an $\left [{O\left ({1 / V}\right), O\left ({V}\right) }\right ]$ tradeoff with $V$ as a control parameter. Simulation results are provided to validate the theoretical analysis and demonstrate the impacts of various parameters.

Stochastic Joint Radio and Computational Resource Management for Multi-User Mobile-Edge Computing Systems

To cope with increasing energy consumption in mobile devices, the mobile cloud offloading has received considerable attention from its ability to offload processing tasks of mobile devices to cloud servers, and previous studies have focused on single type tasks in fixed network environments. However, real network environments are spatio-temporally varying, and typical mobile devices have not only various types of tasks, e.g., network traffic, cloud offloadable/nonoffloadable workloads but also capabilities of CPU frequency scaling and network interface selection between WiFi and cellular. In this paper, we first jointly consider the following three dynamic problems in real mobile environments: 1) cloud offloading policy, i.e., determining to use local CPU resources or cloud resources; 2) allocation of tasks to transmit through networks and to process in local CPU; and 3) CPU clock speed and network interface controls. We propose a DREAM algorithm by invoking the Lyapunov optimization and mathematically prove that it minimizes CPU and network energy for given delay constraints. Trace-driven simulation based on real measurements demonstrates that DREAM can save over 35% of total energy than existing algorithms with the same delay. We also design DREAM architecture and demonstrate the applicability of DREAM in practice.

DREAM: Dynamic Resource and Task Allocation for Energy Minimization in Mobile Cloud Systems

Most existing content caching designs require accurate estimation of content popularity, which can be challenging in the dynamic mobile network environment. Moreover, emerging hierarchical network architecture enables us to enhance the content caching performance by opportunistically exploiting both cloud-centric and edge-centric caching. In this paper, we propose a hybrid content caching design that does not require the knowledge of content popularity. Specifically, our design optimizes the content caching locations, which can be original content servers, central cloud units (CUs) and base stations (BSs) where the design objective is to support as high average requested content data rates as possible subject to the finite service latency. We fulfill this design by employing the Lyapunov optimization approach to tackle an NP-hard caching control problem with the tight coupling between CU caching and BS caching control decisions. Toward this end, we propose algorithms in three specific caching scenarios by exploiting the submodularity property of the sum-weight objective function and the hierarchical caching structure. Moreover, we prove the proposed algorithms can achieve finite content service delay for all arrival rates within the constant fraction of capacity region using Lyapunov optimization technique. Furthermore, we propose practical and heuristic CU/BS caching algorithms to address a general caching scenario by inheriting the design rationale of the aforementioned performance-guaranteed algorithms. Trace-driven simulation demonstrates that our proposed hybrid CU/BS caching algorithms outperform the general popularity based caching algorithm and the independent caching algorithm in terms of average end-to-end service latency and backhaul/fronthaul load reduction ratios.

Hybrid Content Caching in 5G Wireless Networks: Cloud Versus Edge Caching

Mobile code offloading (MCO) is a technology that offloads computing tasks from mobile devices to remote servers managed by code offload service providers (CSPs). Nowadays, it is recommended that the remote servers be placed on the edge of the network to support modern applications, e.g., augmented reality, which demand stringent and ultralow latency or high bandwidth. To date, previous studies have independently addressed code offloading policy in mobile devices and pricing/server provisioning policies in the CSP; moreover, the system models for both user side and CSP side have not adequately reflected their practical aspects. This paper designs a practical model for the both sides and takes account of them in an integrated MCO framework simultaneously. By leveraging Lyapunov drift-plus-penalty technique, we propose code offloading, local CPU clock frequency, and network interface selection policies for mobile users, and propose MCO service pricing and server provisioning policies for the CSP in each of a competition scenario and a cooperation scenario. In the competition scenario, we propose a Com-UC algorithm for mobile users and a Com-PC algorithm for the CSP with the aim to minimize each cost for each queue stability constraint. In the cooperation scenario, we propose a Coo-JC algorithm with the aim to minimize their sum cost for both mobile users and CSP. Via trace-driven simulations, we demonstrate that Com-UC saves at most 71% of its cost and Com-PC attains 82% profit gain for the same delay compared to existing algorithms; moreover, the cooperation between mobile users and CSP additionally reduces costs and delays.

Dual-Side Optimization for Cost-Delay Tradeoff in Mobile Edge Computing

Recently, as plug-in hybrid electric vehicles (PHEVs) take center stage for the eco-friendly and cost-effective transportation, commercial PHEV charging stations will be widely prevalent in the future. However, previous studies in the fields of the management of PHEV charging stations have not synthetically taken practical charging systems into account. In this paper, we study the profit-optimal management of a PHEV charging station under the realistic environment addressing not only various types of vehicles but waiting time guarantee for PHEV customers as well. This paper is first to jointly take into account pricing for charging services, scheduling of reserved vehicles to PHEV chargers, dropping of reserved vehicles, and management of the energy storage in a unified framework that contains key features of a practical PHEV charging station. Based on this framework, we develop an algorithm to find the parameters required for charging management by invoking the “Lyapunov drift-plus-penalty” technique. Through theoretical analysis, we prove that the proposed algorithm achieves close-to-optimal performance under particular conditions by exploiting opportunism of time-varying arrival of charging vehicles, price of electricity, and renewable energy generation, but it requires no probabilistic future information. Finally, we find several significant messages via trace-driven simulation of the proposed algorithm.

Dynamic Pricing, Scheduling, and Energy Management for Profit Maximization in PHEV Charging Stations

Mobile computation offloading is emerging as a promising technology to enhance the computation power of mobile devices by borrowing processing resources from the cloud. However, using cloud resource is a double-edged sword because of the potentially enormous network energy consumption of mobile devices. In this paper, we study the mobile device resource management problem for application throughput fairness and energy efficiency in computation offloading environment. Our problem seeks to optimize task arrival rates, scheduling for local processing and offloading, CPU clock speed, and network interface selection, so as to maximize the energy-utility efficiency defined as achievable utility per unit energy consumption. The efficiency metric has a fractional form that is hard to deal with in general. To address this difficulty, we modify a general Lyapunov optimization technique and derive a series of short-term problems that change over time with respect to an unknown objective parameter. Then, we derive an offloading algorithm and prove that the algorithm maximizes the long-term energy-utility efficiency. Trace-driven simulations demonstrate that our algorithm achieves high-energy efficiency while maintaining throughput fairness among applications running on a mobile device.

Yeongjin Kim

Papers

DREAM: Dynamic Resource and Task Allocation for Energy Minimization in Mobile Cloud Systems

Hybrid Content Caching in 5G Wireless Networks: Cloud Versus Edge Caching

Dual-Side Optimization for Cost-Delay Tradeoff in Mobile Edge Computing

Dynamic Pricing, Scheduling, and Energy Management for Profit Maximization in PHEV Charging Stations

Mobile Computation Offloading for Application Throughput Fairness and Energy Efficiency