关键词： Mobile edge computing   computation offloading   Q-learning   exact potential game   unknown noisy payoff game   strategy learning  

摘要： Mobile edge computing (MEC) is expected to provide cloud-like capacities for mobile users (MUs) at the edge of wireless networks. However, deploying MEC systems faces many challenges, one of which is to achieve an efficient distributed offloading mechanism for multiple users in time-varying wireless environments. In this paper, we study a multi-user multi-edgenode computation offloading problem. Since edge nodes' communication and computing capacities are limited which leads resource contention when many MUs offload to the same edge node at the same time, we formulate this problem as a noncooperative exact potential game (EPG), where each MU, in each time slot, selfishly maximizes its number of processed central processor unit (CPU) cycles and reduces its energy consumption. Assuming that channel information is static and available to MUs, we show that MUs could achieve a Nash equilibrium via a best response-based offloading mechanism. Next, we extend the problem to a practical scenario, where the number of processed CPU cycles is time-varying and unknown to MUs because of the uncertain channel information. In this case, we adopt an unknown payoff game framework and prove that the EPG properties still hold. Then, we propose a model-free reinforcement learning offloading mechanism which helps MUs learn their long-term offloading strategies to maximize their long-term utilities. Numerical results illustrate that our proposed algorithm for unknown CSI outperforms other schemes, such as local processing and random assignment, and achieves up to 87.87% average long-term payoffs compared to the perfect CSI case.

关键词： Mobile edge computing   small cell networks   computation offloading   Nash equilibrium   potential game  

摘要： Mobile edge computing is conceived as an appealing technology to enhance cloud computing capability of mobile devices (MDs) at the edge of the networks. Although some researchers use the technology to address the intensive tasks' high computation needs of MDs in small-cell networks (SCNs), most of them ignore considering the interests interaction between small cells and MDs. In this paper, we study a distributed computation offloading strategy for a multi-device and multi-server system based on orthogonal frequency-division multiple access in SCNs. First, to satisfy the interest requirements of different MDs and analyze the interactions among multiple small cells, we formulate a distributed overhead minimization problem, aiming at jointly optimizing energy consumption and latency of each MD. Second, to ensure the individuals of different MDs, we formulate the proposed overhead minimization problem as a strategy game. Then, we prove the strategy game is a potential game by the feat of potential game theory. Moreover, the potential game-based offloading algorithm is proposed to reach a Nash equilibrium. In addition, to guarantee the performance of the designed algorithm, we consider the lower bound of iteration times to derive the worst case performance guarantee. Finally, the simulation results corroborate that the proposed algorithm can effectively minimize the overhead of each MD compared with different other existing algorithms.

关键词： Heterogeneous networks   mobile edge computing   NOMA   power allocation   task offloading  

摘要： A joint radio and computational resource allocation problem is investigated for non-orthogonal multiple access (NOMA)-based mobile edge computing (MEC) in heterogeneous networks (HetNets). The aim is to minimize the energy consumption of all users under the task execution latency constraint with partial task offloading. Due to the non-convexity of the formulated problem, we propose an iterative algorithm by decomposing the optimization problem into two subproblems, where the computational resource allocation is a convex optimization problem, and the uplink power allocation is solved by the sequential convex programming. Simulation results reveal that the partial offloading is superior to local computing-only and full offloading-only and NOMA-based MEC outperforms the orthogonal multiple access-based MEC in HetNets.

关键词： Mobile edge computing   resilient facility location   software controller   virtual function placement   IoT  

摘要： Replacing hardware controllers with software-based virtual process control functions (VPFs) is a promising approach for improving the operational efficiency and flexibility of industrial control systems. VPFs can be executed in edge clouds in 5G mobile networks or in the wireless backhaul, which can further improve efficiency. Nonetheless, for the acceptance of virtualization in industrial control systems, a fundamental challenge is to ensure that the placement of VPFs be resilient to component failures and cyber-attacks, besides being efficient. In this paper we address this challenge by considering that VPF placement costs are incurred by reserving mobile edge computing (MEC) resources, executing VPF instances, and by data communication. We formulate the VPF placement problem as an integer programming problem, considering resilience as a constraint. We propose a solution based on generalized Benders decomposition and based on linear relaxation of the resulting sub-problems, which effectively reduces the number of integer variables to the number of MEC nodes. We evaluate the proposed solution with respect to operational cost, efficiency, and scalability in a simulated metropolitan area. Our results show that the proposed solution reduces the total cost significantly compared to a greedy baseline algorithm and a local search heuristic, and can scale to moderate problem instances.

关键词： Mobile edge computing   small cell networks   computation offloading management   genetic algorithm   particle swarm optimization  

摘要： To tackle the contradiction between the computation intensive applications and the resource-hungry mobile user equipments (UEs), mobile edge computing (MEC) has been provisioned as a promising solution, which enables the UEs to offload the tasks to the MEC servers. Considering the characteristics of small cell networks (SCNs), integrating MEC into SCNs is natural. But in terms of the high interference, multi-access property, and limited resources of small cell base stations (SBSs), an efficient computation offloading scheme is essential. However, there still lack comprehensive studies on this problem in the densely deployed SCNs. In this paper, we study the energy-efficient computation offloading management scheme in the MEC system with SCNs. The aim of this paper is to minimize the energy consumption of all UEs via jointly optimizing computation offloading decision making, spectrum, power, and computation resource allocation. Specially, the UEs need not only to decide whether to offload but also to determine where to offload. First, we present the computation offloading model and formulate this problem as a mix integer non-linear programming problem, which is NP-hard. Taking advantages of genetic algorithm (GA) and particle swarm optimization (PSO), we design a suboptimal algorithm named as hierarchical GA and PSO-based computation algorithm to solve this problem. Finally, the convergence of this algorithm is studied by simulation, and the performance of the proposed algorithm is verified by comparing with the other baseline algorithms.

关键词： Mobile edge computing   Mobility management   Dynamic offloading   Task size assignment  

摘要： Mobile Edge Computing (MEC) brings computation closer to user equipment (UE) and reduces the latency as well as energy consumption for computation offloading. However, with the development of Ultra Dense Network (UDN), traditional offloading algorithms and mobility management (MM) approaches are not sufficient anymore with a frequent handover between base stations. It becomes far from optimal to execute offloading algorithms only at the beginning of a task when UE moves frequently or in the environment of UDN. Also, simple dynamic adjustment for offloading proportion of data input during UE's movement cannot solve this problem very well, because this approach executes global decision with information from only one position. Exploiting short-term mobility prediction in MM, we propose a novel dynamic mobility-aware partial offloading (DMPO) algorithm to figure out the amount of data for offloading dynamically, together with the decision of communication path in MM, minimizing the energy consumption while satisfying the delay constraint. This proposed algorithm predicts the time to next handover as well as the moving of UE in this phase and assigns data size to each time slot in this phase to achieve our goal of minimizing energy consumption while satisfying the delay constraint. In the simulation, we obverse the average delay and energy consumption with different delay constraint, moving speed of UE and base station density. The results demonstrate that our proposed algorithm saves up to 70% of energy while having a better performance in satisfying the delay constraint compared to traditional approaches. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Mobile cloud Computing   mobile edge computing   cloud computing   5G systems   parallelism   mobile storage  

摘要： The realization of mobile edge computing (MEC) over emerging fifth (5G) generation of wireless systems arises as a driving-force in the future of cloud computing. In order to cope with the volume, variety, and velocity of the IoT traffic while making optimal use of the network infrastructure, a synergistic exploitation of MEC and 5G should be put forward to support advanced resource management applications. In this paper, we propose the use of joint parallelism between wireless and cloud domains to efficiently respond to mobile data deluge. We review the literature, discuss the enabling network architecture, potentials. challenges, and open issues related to the realization of such level of parallelism. We present and evaluate two design examples - parallel computation offload method (PCOM) and parallel transmission and storage method (PTSM)-which outline the benefits of parallelism for computation-hungry and storage-hungry applications, respectively. Results of our optimization formulation show that PCOM and PTSM are able to make an efficient use of the network resources and support a heavy instantaneous workload by means of the parallelism.

关键词： Mobile edge computing  

摘要： Multiple access mobile edge computing is an emerging technique to bring computation resources close to end mobile users. By deploying edge servers at WiFi access points or cellular base stations, the computation capabilities of mobile users can be extended. Existing works mostly assume the remote cloud server can be viewed as a special edge server or the edge servers are willing to cooperate, which is not practical. In this work, we propose an edge-cloud cooperative architecture where edge servers can rent for the remote cloud servers to expedite the computation of tasks from mobile users. With this architecture, the computation offloading problem is modeled as a mixed integer programming with delay constraints, which is NP-hard. The objective is to minimize the total energy consumption of mobile devices. We propose a greedy algorithm as well as a simulated annealing algorithm to effectively solve the problem. Extensive simulation results demonstrate that, the proposed greedy algorithm and simulated annealing algorithm can achieve the near optimal performance. On average, the proposed greedy algorithm can achieve the same application completing time budget performance of the Brute Force optional algorithm with only 31% extra energy cost. The simulated annealing algorithm can achieve similar performance with the greedy algorithm. Copyright © 2018, The Authors. All rights reserved.

关键词： mobile edge computing   task off- loading   greedy choice   energy   resource allo- cation  

摘要： Mobile edge computing （MEC） is a novel technique that can reduce mobiles＇ com- putational burden by tasks offioading, which emerges as a promising paradigm to provide computing capabilities in close proximity to mobile users. In this paper, we will study the scenario where multiple mobiles upload tasks to a MEC server in a sing cell, and allocating the limited server resources and wireless chan- nels between mobiles becomes a challenge. We formulate the optimization problem for the energy saved on mobiles with the tasks being dividable, and utilize a greedy choice to solve the problem. A Select Maximum Saved Energy First （SMSEF） algorithm is proposed to realize the solving process. We examined the saved energy at different number of nodes and channels, and the results show that the proposed scheme can effectively help mobiles to save energy in the MEC system.

关键词： Mobile edge computing   blockchain   computation offloading   content caching   stochastic geometry  

摘要： Blockchain technology has been applied in a variety of fields due to its capability of establishing trust in a decentralized fashion. However, the application of blockchain in wireless mobile networks is hindered by a major challenge brought by the proof-of-work puzzle during the mining process, which sets a high demand for the computational capability and storage availability in mobile devices. To address this problem, we propose a novel mobile edge computing (MEC) enabled wireless blockchain framework where the computation-intensive mining tasks can be offloaded to nearby edge computing nodes and the cryptographic hashes of blocks can be cached in the MEC server. Particularly, two offloading modes are considered, i.e., offloaded to the nearby access point or a group of nearby users. First, we conduct the performance analysis of each mode with stochastic geometry methods. Then, the joint offloading decision and caching strategy is formulated as an optimization problem. Furthermore, an alternating direction method of multipliers based algorithm is utilized to solve the problem in a distributed manner. Finally, simulation results demonstrate the effectiveness of our proposed scheme.