Technological evolution of mobile user equipment (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. A suitable solution extending the battery life-time of the UEs is to offload the applications demanding huge processing to a conventional centralized cloud. Nevertheless, this option introduces significant execution delay consisting of delivery of the offloaded applications to the cloud and back plus time of the computation at the cloud. Such a delay is inconvenient and makes 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 it 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: 1) decision on computation offloading; 2) allocation of computing resource within the MEC; and 3) 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.

Mobile Edge Computing: A Survey on Architecture and Computation Offloading

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.

Mobile-edge computing (MEC) and wireless power transfer (WPT) have been recognized as promising techniques in the Internet of Things era to provide massive low-power wireless devices with enhanced computation capability and sustainable energy supply. In this paper, we propose a unified MEC-WPT design by considering a wireless powered multiuser MEC system, where a multiantenna access point (AP) (integrated with an MEC server) broadcasts wireless power to charge multiple users and each user node relies on the harvested energy to execute computation tasks. With MEC, these users can execute their respective tasks locally by themselves or offload all or part of them to the AP based on a time-division multiple access protocol. Building on the proposed model, we develop an innovative framework to improve the MEC performance, by jointly optimizing the energy transmit beamforming at the AP, the central processing unit frequencies and the numbers of offloaded bits at the users, as well as the time allocation among users. Under this framework, we address a practical scenario where latency-limited computation is required. In this case, we develop an optimal resource allocation scheme that minimizes the AP’s total energy consumption subject to the users’ individual computation latency constraints. Leveraging the state-of-the-art optimization techniques, we derive the optimal solution in a semiclosed form. Numerical results demonstrate the merits of the proposed design over alternative benchmark schemes.

pdf/joint-offloading-and-computing-optimization-in-wireless-2rk6zye6nl.pdf

Joint Offloading and Computing Optimization in Wireless Powered Mobile-Edge Computing Systems

Migrating computational intensive tasks from mobile devices to more resourceful cloud servers is a promising technique to increase the computational capacity of mobile devices while saving their battery energy. In this paper, we consider an MIMO multicell system where multiple mobile users (MUs) ask for computation offloading to a common cloud server. We formulate the offloading problem as the joint optimization of the radio resources—the transmit precoding matrices of the MUs—and the computational resources—the CPU cycles/second assigned by the cloud to each MU—in order to minimize the overall users’ energy consumption, while meeting latency constraints. The resulting optimization problem is nonconvex (in the objective function and constraints). Nevertheless, in the single-user case, we are able to compute the global optimal solution in closed form. In the more challenging multiuser scenario, we propose an iterative algorithm, based on a novel successive convex approximation technique, converging to a local optimal solution of the original nonconvex problem. We then show that the proposed algorithmic framework naturally leads to a distributed and parallel implementation across the radio access points, requiring only a limited coordination/signaling with the cloud. Numerical results show that the proposed schemes outperform disjoint optimization algorithms.

Joint Optimization of Radio and Computational Resources for Multicell Mobile-Edge Computing

http://www.parkjonghyuk.net/lecture/2015-1st-lecture/networksecurity/Paper2.pdf

Security in cloud computing

Mobile cloud computing, with its promise to meet the urgent need for richer applications and services of resource-constrained mobile devices, is emerging as a new computing paradigm and has recently attracted significant attention. However, there is no clear definition and no well defined scope for mobile cloud computing due to commercial hype, and diverse ways of combining cloud computing and mobile applications. This article makes the first attempt to present a survey of mobile cloud computing from the perspective of its intended usages. Specifically, we introduce three common mobile cloud architectures and classify comprehensive existing work into two fundamental categories: computation offloading and capability extending. Considering the energy bottleneck and user context of mobile devices, we discuss the research challenges and opportunities of introducing cloud computing to assist mobile devices, including energy-efficient interactions, virtual machine migration overhead, privacy, and security. Moreover, we demonstrate three real-world applications enabled by mobile cloud computing, in order to stimulate further discussion and development of this emerging field.

Gearing resource-poor mobile devices with powerful clouds: architectures, challenges, and applications

Mobile cloud computing, promising to extend the capabilities of resource-constrained mobile devices, is emerging as a new computing paradigm which has fostered a wide range of exciting applications. In this new paradigm, efficient data transmission between the cloud and mobile devices becomes essential. This, however, is highly unreliable and unpredictable due to several uncontrollable factors, particularly the instability and intermittency of wireless connections, fluctuation of communication bandwidth, and user mobility. Consequently, this puts a heavy burden on the energy consumption of mobile devices. Confirmed by our experiments, significantly more energy is consumed during “bad” connectivity. Inspired by the feasibility to schedule data transmissions for prefetching-friendly or delay-tolerant applications, in this paper, we present eTime, a novel Energy-efficient data Transmission strategy between cloud and Mobile dEvices, based on Lyapunov optimization. It aggressively and adaptively seizes the timing of good connectivity to prefetch frequently used data while deferring delay-tolerant data in bad connectivity. To cope with the randomness and unpredictability of wireless connectivity, eTime only relies on the current status information to make a global energy-delay tradeoff decision. Our evaluations from both trace-driven simulation and realworld implementation show that eTime can be applied to various popular applications while achieving 20%-35% energy saving.

eTime: Energy-efficient transmission between cloud and mobile devices

Many mobile applications require frequent wireless transmissions between the content provider and mobile devices, consuming much energy in mobile devices. Motivated by the popularity of prefetch-friendly or delay-tolerant apps (e.g., social networking, app updates, cloud storage), we design and implement an application-layer transmission protocol, AppATP, which leverages cloud computing to manage data transmissions for mobile apps, transferring data to and from mobile devices in an energy-efficient manner. Measurements show that significantly amount of energy is consumed by mobile devices during poor connectivity. Based on this observation, AppATP adaptively seizes periods of good bandwidth condition to prefetch frequently used data with minimum energy consumption, while deferring delay-tolerant data during poor network connectivity. Using the stochastic control framework, AppATP only relies on the current network information and data queue sizes to make an online decision on transmission scheduling, and performs well under unpredictable wireless network conditions. We implement AppATP on Samsung Note 2 smartphones and Amazon EC2. Results from both trace-driven simulations and extensive real-world experiments show that AppATP can be applied to a variety of application scenarios while achieving $30$ - $50$ percent energy savings for mobile devices.

AppATP : An Energy Conserving Adaptive Mobile-Cloud Transmission Protocol

The invention discloses an energy-saving transmission method oriented to mobile cloud computing and a corresponding middleware system. A cloud end hosting request management module is used for receiving and processing a mirror image hoisting request of a mobile end, a cloud end application mirror image data management module manages data updating and deleting of a mobile application mirror image, a cloud end-mobile end data transmission module achieves energy-saving data transmission between a cloud platform and the mobile end, a mobile end data storage and distribution module stores the data transmitted through the cloud end and distributes the data to different mobile applications, and a mobile end application request analysis module analyzes data requests of the mobile applications, and feeds back corresponding data from the storage module. The energy-saving transmission method oriented to mobile cloud computing and the corresponding middleware system can well lower high transmission energy consumption under a wireless network bandwidth unstable environment, improves the performance of a mobile end battery and improves user experience of a data pre-taking program at the same time.

Energy-saving transmission method oriented to mobile cloud computing and middleware system

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Papers

Gearing resource-poor mobile devices with powerful clouds: architectures, challenges, and applications

eTime: Energy-efficient transmission between cloud and mobile devices

AppATP : An Energy Conserving Adaptive Mobile-Cloud Transmission Protocol

Energy-saving transmission method oriented to mobile cloud computing and middleware system