关键词： Mobile edge computing   virtual reality   communications-computing-caching tradeoffs  

摘要： Mobile edge computing (MEC) is expected to be an effective solution to deliver virtual reality (VR) videos over wireless networks. In contrast to previous computation-constrained MEC, which reduces the computation-resource consumption at the mobile device by increasing the communication-resource consumption, we develop a communications-constrained MEC framework to reduce communication-resource consumption by fully exploiting the computation and caching resources at the mobile VR device in this paper. Specifically, according to a task modularization, the MEC server only delivers the components which have not been stored in the VR device, and then the VR device uses the received components and other cached components to construct the task, yielding low communication cost but high delay. The MEC server also computes the task by itself to reduce the delay, however, it consumes more communication-resource due to the delivery of entire task. Therefore, we propose a task scheduling strategy to decide which computation model should the MEC server operates to minimize the communication-resource consumption under the delay constraint. Finally, the tradeoffs among communications, computing, and caching are also discussed, and we analytically find that given a target communication-resource consumption, the transmission rate is inversely proportional to the computing ability of mobile VR device.

关键词： Heterogeneous networks   matching theory   mobile edge computing   resource allocation  

摘要： In this paper, we propose an optimization framework of computation offloading and resource allocation for mobile-edge computing with multiple servers. Concretely, we aim to minimize the system wide computation overhead by jointly optimizing the individual computation decisions, transmit power of the users, and computation resource at the servers. The crux of the problem lies in the combinatorial nature of multi-user offloading decisions, the complexity of the optimization objective, and the existence of inter cell interference. To overcome these difficulties, we adopt a suboptimal approach by splitting the original problem into two parts: 1) computation offloading decision and 2) joint resource allocation. To enable distributed computation offloading, two matching algorithms are investigated. Moreover, the transmit power of offloading users is found using a bisection method with approximate inter-cell interference, and the computation resources allocated to offloading users is achieved via the duality approach. Simulation results validate that the proposed framework can significantly improve the percentage of offloading users and reduce the system overhead with respect to the existing schemes. Our results also show that the proposed framework performs close to the centralized heuristic algorithm with a small optimality gap.

关键词： Mobile edge computing   offloading   energy saving   Krill herd algorithm  

摘要： Due to advances in Internet of Things (IoT) technologies, mobile devices have become an inseparable part of human *** limited executing capabilities of mobile devices along with constrained energy remain barriers in front of this expectation. To address these challenges, Mobile Edge Computing (MEC) is considered a promising computing model to offer computing ability to mobile users in fifth-generation (5G) networks. In this paper, we jointly create an optimization problem to minimize the combination of energy cost and packet congestion. By adopting a Promoted-by-probability (PBP) scheme, we efficiently control packet congestion of different priority packets transmitted to MEC. An improved krill herd metaheuristic optimization algorithm is presented to obtain optimal results for minimizing the total overhead of MEC in terms of energy consumption and queuing congestion. The evaluation study demonstrates that our proposal performs efficiently in terms of energy consumption and execution delay.

关键词： Mobile edge computing   Internet of Thing   service provisioning   service composition  

摘要： The increasing number of Internet of Thing (IoT) devices and services makes it convenient for people to sense the real world and makes optimal decisions or complete complex tasks with them. However, the latency brought by unstable wireless networks and computation failures caused by constrained resources limit the development of IoT. A popular approach to solve this problem is to establish an IoT service provision system based on a mobile edge computing (MEC) model. In the MEC model, plenty of edge servers are placed with access points via wireless networks. With the help of cached services on edge servers, the latency can be reduced, and the computation can be offloaded. The cache services must be carefully selected so that many requests can by satisfied without overloading resources in edge servers. This paper proposes an optimized service cache policy by taking advantage of the composability of services to improve the performance of service provision systems. We conduct a series of experiments to evaluate the performance of our approach. The result shows that our approach can improve the average response time of these IoT services.

关键词： Mobile edge computing  

摘要： Mobile edge computing (MEC) enables battery-powered mobile nodes to acquire information technology services at the network edge. These nodes desire to enjoy their service under power saving. The sampling rate invariant detection (SRID) is the first downclocking WiFi technique that can achieve this objective. With SRID, a node detects one packet arrival at a downclocked rate. Upon a successful detection, the node reverts to a full-clocked rate to receive the packet immediately. To ensure that a node acquires its service immediately, the detection performance (namely, the miss-detection probability and the false-alarmprobability) of SRID is of importance. This paper is the first one to theoretically study the crucial impact of SRID attributes (e.g., tolerance threshold, correlation threshold, and energy ratio threshold) on the packet detection performance. Extensive Monte Carlo experiments show that our theoretical model is very accurate. This study can help system developers set reasonable system parameters for WiFi downclocking.

关键词： Mobile edge computing   MCPTT   virtualized 5G   network function virtualization   IOPS   CUPS  

摘要： There is a growing interest in adapting mission critical public-safety communications from traditional private radio technologies toward 5G. The need for resiliency and strong delay requirements has resulted in mobile edge computing (MEC) emerging as a key technology to improve the deployment of public safety applications over the mobile network. This paper presents a non-standalone 5G ETSI MEC-based architecture for mission-critical push-to-talk (MCPTT) services. The proposal suggests a hierarchical distributed MCPTT architecture that allocates the user plane at the edge, keeping the control plane (CP) centralized for synchronization and assistance purposes. The MEC architecture enables the deployment of low latency services, due to the proximity of functional servers to the end-user, releases user equipment from heavy workloads, and benefits from horizontal scalability to provide dynamic allocation of network resources at specific locations. The proposed architecture is also beneficial in an isolated E-UTRAN operation situation, where in the case of backhaul connection loss, local MCPTT services can be straightforwardly deployed within an isolated group of eNodeBs.

关键词： Mobile edge computing  

摘要： —Mobile-edge computing (MEC) is a promising technology to enable real-time information transmission and computing by offloading computation tasks from wireless devices to network edge. In this study, we propose a price-based distributed method to manage the offloaded computation tasks from users. A Stackelberg game is formulated to model the interaction between the edge cloud and users, where the edge cloud sets prices to maximize its revenue subject to its finite computation capacity, and for given prices, each user locally makes offloading decision to minimize its own cost which is defined as latency plus payment. Depending on the edge cloud’s knowledge of the network information, we develop the uniform and differentiated pricing algorithms, which can both be implemented in distributed manners. Simulation results validate the effectiveness of the proposed schemes. Copyright © 2017, The Authors. All rights reserved.

关键词： cloud computing   fog computing   edge computing   mobile edge computing   resource management and code scheduling  

摘要： Recently, several novel computing paradigms are proposed, e.g., fog computing and edge computing. In such more decentralized computing paradigms, the location and resource for code execution and data storage of end applications could also be optionally distributed among different places or machines. In this paper, we position that this situation requires a new transparent and usercentric approach to unify the resource management and code scheduling from the perspective of end users. We elaborate our vision and propose a software-defined code scheduling framework. The proposed framework allows the code execution or data storage of end applications to be adaptively done at appropriate machines under the help of a performance and capacity monitoring facility, intelligently improving application performance for end users. A pilot system and preliminary results show the advantage of the framework and thus the advocated vision for end users.

关键词： Information-centric networking   in-network caching   mobile edge computing   resource allocation   virtualization  

摘要： In order to better accommodate the dramatically increasing demand for data caching and computing services, storage and computation capabilities should be endowed to some of the intermediate nodes within the network, therefore increasing the data throughput and reducing the network operation cost. In this paper, we design a novel information-centric heterogeneous networks framework aiming at enabling content caching and computing. Furthermore, due to the virtualization of the whole system, communication, computing, and caching resources can be shared among all users associated with different virtual service providers. We formulate the virtual resource allocation strategy as a joint optimization problem, where the gains of not only virtualization but also caching and computing are taken into consideration in the proposed information-centric heterogeneous networks virtualization architecture. In addition, a distributed algorithm based on alternating direction method of multipliers is adopted in order to solve the formulated problem. Since each base station only needs to solve its own problem without exchange of channel state information by using the distributed algorithm, the computational complexity and signaling overhead can be greatly reduced. Finally, extensive simulations are presented to show the effectiveness of the proposed scheme under different system parameters.

关键词： Computation offloading   Medium access control   Mobile edge computing   Vehicular ad-hoc networks   Vehicular cloud computing  

摘要： Vehicular Edge Computing architectures are projected recently that support expedient task offloading. However, in vehicular environments the character of the wireless channel and convenience of resources is unpredictable. Effective utilization of vehicular resources remains a challenge. To beat this challenge, the current work proposes Architecture and Channel Aware Task Offloading in Vehicular Clouds (ACATOVC) protocol for efficient task offloading in vehicular networks. Results obtained by in depth simulations are conferred to gauge and compare its performance with existing protocols. © 2017, UK Simulation Society. All rights reserved.