关键词： Fog computing   mobile edge cloud (MEC)   mobile edge computing   service model   survey   system architecture  

摘要： Mobile edge cloud (MEC) is a model for enabling on-demand elastic access to, or an interaction with a shared pool of reconfigurable computing resources such as servers, storage, peer devices, applications, and services, at the edge of the wireless network in close proximity to mobile users. It overcomes some obstacles of traditional central clouds by offering wireless network information and local context awareness as well as low latency and bandwidth conservation. This paper presents a comprehensive survey of MEC systems, including the concept, architectures, and technical enablers. First, the MEC applications are explored and classified based on different criteria, the service models and deployment scenarios are reviewed and categorized, and the factors influencing the MEC system design are discussed. Then, the architectures and designs of MEC systems are surveyed, and the technical issues, existing solutions, and approaches are presented. The open challenges and future research directions of MEC are further discussed.

关键词： Mobile edge computing   cloudlet   scalability   resource management   virtualization   transmission power control   particle swarm optimization  

摘要： Mobile devices have several restrictions due to design choices that guarantee their mobility. A way of surpassing such limitations is to utilize cloud servers called cloudlets on the edge of the network through Mobile Edge Computing. However, as the number of clients and devices grows, the service must also increase its scalability in order to guarantee a latency limit and quality threshold. This can be achieved by deploying and activating more cloudlets, but this solution is expensive due to the cost of the physical servers. The best choice is to optimize the resources of the cloudlets through an intelligent choice of configuration that lowers delay and raises scalability. Thus, in this paper we propose an algorithm that utilizes Virtual Machine Migration and Transmission Power Control, together with a mathematical model of delay in Mobile Edge Computing and a heuristic algorithm called Particle Swarm Optimization, to balance the workload between cloudlets and consequently maximize cost-effectiveness. Our proposal is the first to consider simultaneously communication, computation, and migration in our assumed scale and, due to that, manages to outperform other conventional methods in terms of number of serviced users.

关键词： Mobile Edge Computing   Mobile cloudlets   Load balancing   Extensive game model  

摘要： Mobile Edge computing (MEC) and caching are new forms of computing architecture which brings network functions to the network edge or physical proximity of the users. In MEC, the mobile cloudlets are smart phones which offer computational services to other smart phones available in the physical proximity. Selection of the device for offloading computationally intensive tasks is a very important criterion for end-users Quality of Service. This work focuses on optimal selection of devices which receives offloaded computational tasks. This paper presents a non-cooperative extensive game model where players maximize their pay-offs which leads to minimization of response time. Further, we make our proposed model to implicitly depend upon the battery life of the computational task receivers. The game model achieves Nash Equilibrium by using backward induction technique. This work also takes care of the device availability by clustering the previous availability data. Finally, we evaluate the performance of the proposed model upon response time, end-users utility and memory utilizations. We also see that the proposed work out performs the different schemes which we used for comparisons. (C) 2018 Elsevier Inc. All rights reserved.

关键词： Virtualized network function   Mobile edge computing   Random walk   Boundary value problem  

摘要： In this paper, we analyse a system composed of two data centres with limited capacity in terms of computing (namely virtual cores). When one request for a single core is blocked at the first data centre, this request is forwarded to the second one. To protect the requests originally assigned to the second data centre, a trunk reservation policy is introduced (i.e., a redirected request is accepted only if the number of free cores at the second data centre is greater than a given threshold). After rescaling the system when there are many cores in both data centres and high request arrival rates, we are led to analyse a random walk in the quarter plane, which has the particularity of having nonconstant reflecting conditions on one boundary of the quarter plane. Computing the stationary distribution of the present random walk requires the determination of three unknown functions, one polynomial and two infinite generating functions. We show that the coefficients of the polynomial are solutions to a linear system. After numerically solving this linear system, we are able to compute the two other unknown functions and the blocking probabilities at both data centres. Numerical experiments are eventually performed to estimate the gain achieved by the trunk reservation policy. (C) 2018 Elsevier B.V. All rights reserved.

关键词： mobile edge computing   Internet of Things   hybrid energy harvesting   Lyapunov optimization   energy efficiency   delay  

摘要： Mobile Edge Computing (MEC) has evolved into a promising technology that can relieve computing pressure on wireless devices (WDs) in the Internet of Things (IoT) by offloading computation tasks to the MEC server. Resource management and allocation are challenging because of the unpredictability of task arrival, wireless channel status and energy consumption. To address such a challenge, in this paper, we provide an energy-efficient joint resource management and allocation (ECM-RMA) policy to reduce time-averaged energy consumption in a multi-user multi-task MEC system with hybrid energy harvested WDs. We first formulate the time-averaged energy consumption minimization problem while the MEC system satisfied both the data queue stability constraint and energy queue stability constraint. To solve the stochastic optimization problem, we turn the problem into two deterministic sub-problems, which can be easily solved by convex optimization technique and linear programming technique. Correspondingly, we propose the ECM-RMA algorithm that does not require priori knowledge of stochastic processes such as channel states, data arrivals and green energy harvesting. Most importantly, the proposed algorithm achieves the energy consumption-delay trade-off as [O(1/V), O(V)]. V, as a non-negative weight, which can effectively control the energy consumption-delay performance. Finally, simulation results verify the correctness of the theoretical analysis and the effectiveness of the proposed algorithm.

关键词： Mobile Edge Computing   Task assignment   User mobility   Offloading  

摘要： Mobile Edge Computing (MEC) has emerged as a prospective computing paradigm to provide pervasive computing and storage services for mobile and big data applications. In MEC, many small cell base stations (sBSs) are deployed to establish a mobile edge network (MEN). These sBSs can be usually accessed directly by mobile users. The computational tasks are first offloaded from mobile users to the MEN and then executed in one or several specific sBSs in the MEN. While the offloading decision has been well studied, the task execution delay on the MEN side is overlooked. This paper aims at reducing the task execution delay by task scheduling in MENs. Specifically, we jointly consider the task properties, the user mobility and network constraints. The problem is formalized as a constraint satisfaction problem and a lightweight heuristic solution is proposed for fast scheduling. We conduct simulation experiments to study the performance of the proposed work. The results show that our work is able to significantly reduce the task execution delay in MENs and thus reduces the end-to-end delay for MEC tasks. (C) 2018 Elsevier B.V. All rights reserved.

关键词： HTTP live streaming   mobile edge computing   network function virtualization   quality of experience   video caching  

摘要： HTTP-based live streaming has become increasingly popular in recent years, and more users have started generating 4K live streams from their devices (e.g., mobile phones) through social-media service providers like Facebook or YouTube. If the audience is located far from a live stream source across the global Internet, TCP throughput becomes substantially suboptimal due to slow start and congestion control mechanisms. This is especially the case when the end-to-end content delivery path involves radio access network at the last mile. As a result, the data rate perceived by a mobile receiver may not meet the high requirement of 4K video streams, which causes deteriorated quality-of-experience (QoE). In this paper, we propose a scheme named edge-based transient holding of live segment (ETHLE), which addresses the abovementioned issue by performing context-aware transient holding of video segments at the mobile edge with virtualized content caching capability. Through holding the minimum number of live video segments at the mobile edge cache in a context-aware manner, the ETHLE scheme is able to achieve seamless 4K live streaming experiences across the global Internet by eliminating buffering and substantially reducing initial startup delay and live stream latency. It has been deployed as a virtual network function at an LTE-A network, and its performance has been evaluated using real live stream sources that are distributed around the world. The significance of this paper is that by leveraging virtualized caching resources at the mobile edge, we address the conventional transport-layer bottleneck and enable QoE-assured Internet-wide live streaming services with high data rate requirements.

关键词： Mobile edge computing   RAN slicing   two-level problem   information prediction  

摘要： In 5G networks, mobile edge computing (MEC) and radio access network (RAN) slicing can support the evolving services and satisfy the diverse service requirements efficiently, thereby, being promising technologies. In this letter, the work is focused on the RAN slicing between MEC services and traditional services. An information prediction and dynamic programming based RAN slicing algorithm is proposed. It guarantees the inter-slice isolation and realizes the intra-slice customization. Furthermore, it can optimize the network utility with high fairness in polynomial time complexity.

关键词： Mobile edge computing   computation offloading   radio access networks   parallel computing   multiple access   stochastic geometry   queueing  

摘要： Next-generation wireless networks will provide users ubiquitous low-latency computing services using devices at the network edge, called mobile edge computing (MEC). The key operation of MEC is to offload computation intensive tasks from users. Since each edge device comprises an access point (AP) and a computer server (CS), an MEC network can be decomposed as a radio access network cascaded with a CS network. Based on the architecture, we investigate network-constrained latency performance, namely communication latency and computation latency, under the constraints of radio-access connectivity and CS stability. To this end, a spatial random network is modeled featuring random node distribution, parallel computing, non-orthogonal multiple access, and random computation-task generation. Given the model and the said network constraints, we derive the scaling laws of communication latency and computation latency with respect to network-load parameters (density of mobiles and their task-generation rates) and network-resource parameters (bandwidth, density of APs/CSs, and CS computation rate). Essentially, the analysis involves the interplay of the theories of stochastic geometry, queueing, and parallel computing. Combining the derived scaling laws quantifies the tradeoffs between the latencies, network connectivity, and network stability. The results provide useful guidelines for MEC-network provisioning and planning by avoiding either of the cascaded radio access network or CS network being a performance bottleneck.

关键词： Auction   economic model   mobile edge computing   resource allocation  

摘要： Energy harvesting techniques extend the lifetime of Internet of Things (IoT), whereas yield an unprecedented paradigm shift in network design. Base stations (BSs), being powered by self-contained energy harvesting modules, may keep OFF during recharging, leading to super-frequent handovers for nodes and high network dynamics. Under such dynamics, multiple cooperating BSs for the service of a "smart thing," referred to as coordinated multipoint (CoMP), become a feasible solution by effectively improving communication reliability. To the best of knowledge, this is the first work to consider CoMP uplink transmission to alleviate outage caused by energy harvesting of BSs in IoT. A simple CoMP uplink transmission scheme is proposed for each node of IoT to transmit to two cooperating BSs in a K-tier heterogeneous network. The performance of the proposed scheme is analyzed using tools of stochastic geometry, in terms of availability and average rate, considering the effect of energy capacity, energy charging rate, in a K-tier heterogeneous network. Performance evaluation through extensive simulations are conducted and it is shown that the simulation results fit well with the analytical ones. The performance of the proposed scheme is then compared with that of the non-CoMP scheme, and it is found that the proposed scheme can significantly improve availability and average rate.