Cloud-Link: April-June-2017 Issue

Issue: April-June 2017

Cloud-Link: Special Issue on Mobile Edge Computing

Mobile Edge Computing

Mobile Edge Computing (MEC) is emerging as a very promising computation architecture by pushing computation and storage closer to end users with both strategically deployed and opportunistic processing and storage resources. Such mechanism is essentially different from the traditional definition of cloud computing. It can provide solutions to problems faced by mobile cloud computing and agile support to a large number of different mobile and Internet of Things (IoT) applications and services, e.g., transport, smart grid, and healthcare.

This issue is devoted to the topic of MEC, for which seven recent articles have been selected to cover different aspects. The first article “A Survey on Mobile Edge Networks: Convergence of Computing, Caching and Communications” has a literature review of the recent studies considering computation, caching and communications. The second article “Mobile Edge Computing: A Survey on Architecture and Computation Offloading" have a survey on computation offloading in mobile edge computing. “Mobile Edge Cloud Network Design Optimization” tackles the edge cloud network design problem for mobile access networks. “Computing with Nearby Mobile Devices: a Work Sharing Algorithm for Mobile Edge-Clouds” has an interesting observation that nearby mobile devices can efficiently be utilized as a crowd-powered resource cloud to complement the remote clouds. This observation has been extended to complement edge computing concepts. Offloading is very important technique in mobile edge computing. Both articles "Efficient Multi-User Computation Offloading for Mobile-Edge Cloud Computing" and “Dynamic Computation Offloading for Mobile-Edge Computing with Energy Harvesting Devices" study the offloading technique in the context of computation tasks. In addition, in the future 5G wireless networks, energy efficiency and interference management are two critical requirements. The last article "Joint Computation Offloading and Interference Management in Wireless Cellular Networks with Mobile Edge Computing" focus on offloading in wireless communications environments.

We hope that this issue of Cloud-Link can provide you with useful references to explore this important and interesting topic further. Articles have been selected based on various considerations (for example, variety, relevancy, and anticipated readers’ interests) and unavoidably there are many other useful and insightful articles that have not been included. You are also encouraged to search through IEEE Xplore and other databases for further reading. We are looking for topics for upcoming issues. If you have any suggestions, please email them to the editors.

Yan Zhang, University of Oslo, Norway. Email:

Jie Li, University of Tsukuba, Japa. Email:

Ching-Hsien Hsu, Chung Hua University, Taiwan. Email:

Articles in this issue:

A Survey on Mobile Edge Networks: Convergence of Computing, Caching and Communications

Shuo Wang ; Xing Zhang ; Yan Zhang ; Lin Wang ; Juwo Yang ; Wenbo Wang

Published in IEEE Access, 2017


As the explosive growth of smart devices and the advent of many new applications, traffic volume has been growing exponentially. The traditional centralized network architecture cannot accommodate such user demands due to heavy burden on the backhaul links and long latency. Therefore, new architectures which bring network functions and contents to the network edge are proposed, i.e., mobile edge computing and caching. Mobile edge networks provide cloud computing and caching capabilities at the edge of cellular networks. In this survey, we make an exhaustive review on the state-of-the-art research efforts on mobile edge networks. We first give an overview of mobile edge networks including definition, architecture and advantages. Next, a comprehensive survey of issues on computing, caching and communication techniques at the network edge is presented respectively. The applications and use cases of mobile edge networks are discussed. Subsequently, the key enablers of mobile edge networks such as cloud technology, SDN/NFV and smart devices are discussed. Finally, open research challenges and future directions are presented as well.

Read the full article at IEEE Xplore

Mobile Edge Computing: A Survey on Architecture and Computation Offloading

Pavel Mach, Zdenek Becvar



Technological evolution of mobile user equipments (UEs), such as smartphones or laptops, goes hand-in-hand with evolution of new mobile applications. However, running computationally demanding applications at the UEs is constrained by limited battery capacity and energy consumption of the UEs. Suitable solution extending the battery life-time of the UEs is to offload the applications demanding huge processing to a conventional centralized cloud (CC). Nevertheless, this option introduces significant execution delay consisting in delivery of the offloaded applications to the cloud and back plus time of the computation at the cloud. Such delay is inconvenient and make the offloading unsuitable for real-time applications. To cope with the delay problem, a new emerging concept, known as mobile edge computing (MEC), has been introduced. The MEC brings computation and storage resources to the edge of mobile network enabling to run the highly demanding applications at the UE while meeting strict delay requirements. The MEC computing resources can be exploited also by operators and third parties for specific purposes. In this paper, we first describe major use cases and reference scenarios where the MEC is applicable. After that we survey existing concepts integrating MEC functionalities to the mobile networks and discuss current advancement in standardization

of the MEC. The core of this survey is, then, focused on user-oriented use case in the MEC, i.e., computation offloading. In this regard, we divide the research on computation offloading to

three key areas: i) decision on computation offloading, ii) allocation of computing resource within the MEC, and iii) mobility management. Finally, we highlight lessons learned in area of the MEC and we discuss open research challenges yet to be addressed in order to fully enjoy potentials offered by the MEC.

Read the full article at IEEE Xplore

Mobile Edge Cloud Network Design Optimization

Alberto Ceselli; Marco Premoli; Stefano Secci

IEEE/ACM Transactions on Networking, early access


Major interest is currently given to the integration of clusters of virtualization servers, also referred to as 'cloudlets' or 'edge clouds', into the access network to allow higher performance and reliability in the access to mobile edge computing services. We tackle the edge cloud network design problem for mobile access networks. The model is such that the virtual machines (VMs) are associated with mobile users and are allocated to cloudlets. Designing an edge cloud network implies first determining where to install cloudlet facilities among the available sites, then assigning sets of access points, such as base stations to cloudlets, while supporting VM orchestration and considering partial user mobility information, as well as the satisfaction of service-level agreements. We present link-path formulations supported by heuristics to compute solutions in reasonable time. We qualify the advantage in considering mobility for both users and VMs as up to 20% less users not satisfied in their SLA with a little increase of opened facilities. We compare two VM mobility modes, bulk and live migration, as a function of mobile cloud service requirements, determining that a high preference should be given to live migration, while bulk migrations seem to be a feasible alternative on delay-stringent tiny-disk services, such as augmented reality support, and only with further relaxation on network constraints.

Read the full article at IEEE Xplore

Computing with Nearby Mobile Devices: a Work Sharing Algorithm for Mobile Edge-Clouds

Niroshinie Fernando ; Seng W. Loke ; Wenny Rahayu

IEEE Transactions on Cloud Computing, early access


As mobile devices evolve to be powerful and pervasive computing tools, their usage also continues to increase rapidly. However, mobile device users frequently experience problems when running intensive applications on the device itself, or offloading to remote clouds, due to resource shortage and connectivity issues. Ironically, most users’ environments are saturated with devices with significant computational resources. This paper argues that nearby mobile devices can efficiently be utilised as a crowd-powered resource cloud to complement the remote clouds. Node heterogeneity, unknown worker capability, and dynamism are identified as essential challenges to be addressed when scheduling work among nearby mobile devices. We present a worksharing model, called Honeybee, using an adaptation of the well-known work stealing method to load balance independent jobs among heterogeneous mobile nodes, able to accommodate nodes randomly leaving and joining the system. The overall strategy of Honeybee is to focus on short-term goals, taking advantage of opportunities as they arise, based on the concepts of proactive workers and opportunistic delegator. We evaluate our model using a prototype framework built using Android and implement two applications. We report speedups of up to 4 with seven devices and energy savings up to 71% with eight devices.

Read the full article at IEEE Xplore

Efficient multi-user computation offloading for mobile-edge cloud computing,

X. Chen, L. Jiao, W. Li and X. Fu,

IEEE/ACM Trans. Networking, vol. 24, no. 5, pp. 2795 -2808, Oct. 2016.


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.

Read the full article at IEEE Xplore

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

Yuyi Mao ; Jun Zhang ; Khaled B. Letaief

IEEE Journal on Selected Areas in Communications, vol.34, no.12, Dec. 2016


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.

Read the full article at IEEE Xplore

Joint Computation Offloading and Interference Management in Wireless Cellular Networks With Mobile Edge Computing

Chenmeng Wang ; F. Richard Yu ; Chengchao Liang ; Qianbin Chen ; Lun Tang

IEEE Transactions on Vehicular Technology, early access


Mobile edge computing (MEC) has attracted great interests as a promising approach to augment computational capabilities of mobile devices. An important issue in the MEC paradigm is computation offloading. In this paper, we propose an integrated framework for computation offloading and interference management in wireless cellular networks with mobile edge computing. In this integrated framework, we formulate the computation offloading decision, physical resource block (PRB) allocation, and MEC computation resource allocation as optimization problems. The MEC server makes the offloading decision according to the local computation overhead estimated by all user equipments (UEs) and the offloading overhead estimated by the MEC server itself. Then, the MEC server performs the PRB allocation using graph coloring method. The outcomes of the offloading decision and PRB allocation are then used to distribute the computation resource of the MEC server to the UEs. Simulation results are presented to show the effectiveness of the proposed scheme with different system parameters.

Read the full article at IEEE Xplore

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