Query: edge computing samll cell

title: Edge Computing Enabled Cell Free Massive MIMO Systems
abstract: Mobile edge computing (MEC) has been introduced to provide additional computing capabilities at network edges in order to improve performance of latency critical applications. In this paper, we consider the cell free (CF) massive MIMO framework with implementing MEC functionalities. We consider multiple types of users with different average time requirements for computing/processing the tasks, and consider access points (APs) with MEC servers and a central server (CS) with the cloud computing capability. After deriving successful communication and computing probabilities using stochastic geometry and queueing theory, we present the successful edge computing probability (SECP) for a target computation latency. Through numerical results, we also analyze the impact of the AP coverage and the offloading probability to the CS on the SECP. It is observed that the optimal probability of offloading to the CS in terms of the SECP decreases with the AP coverage. Finally, we numerically characterize the minimum required energy consumption for guaranteeing a desired level of SECP. It is observed that for any desired level of SECP, it is more energy efficient to have larger number of APs as compared to having more number of antennas at each AP with smaller AP density.

title: Joint Service Placement and Request Routing in Multi cell Mobile Edge Computing Networks
abstract: The proliferation of innovative mobile services such as augmented reality, networked gaming, and autonomous driving has spurred a growing need for low latency access to computing resources that cannot be met solely by existing centralized cloud systems. Mobile Edge Computing (MEC) is expected to be an effective solution to meet the demand for low latency services by enabling the execution of computing tasks at the network periphery, in proximity to end users. While a number of recent studies have addressed the problem of determining the execution of service tasks and the routing of user requests to corresponding edge servers, the focus has primarily been on the efficient utilization of computing resources, neglecting the fact that non trivial amounts of data need to be stored to enable service execution, and that many emerging services exhibit asymmetric bandwidth requirements. To fill this gap, we study the joint optimization of service placement and request routing in MEC enabled multi cell networks with multidimensional (storage computation communication) constraints. We show that this problem generalizes several problems in literature and propose an algorithm that achieves close to optimal performance using randomized rounding. Evaluation results demonstrate that our approach can effectively utilize the available resources to maximize the number of requests served by low latency edge cloud servers.

title: Distributed Green Offloading and Power Optimization in Virtualized Small Cell Networks With Mobile Edge Computing
abstract: Virtualized small cell networks (SCNs) integrated with mobile edge computing (MEC) is a promising paradigm to provide both wideband access and intensive computation economically for user equipments (UEs) in the scenario of multiple mobile virtual network operators and infrastructure providers. However, the model of offloading in this case is often of high complexity and lacks effective solution. In this paper, by jointly considering offloading, time slice and power allocation, we formulate the energy consumption reduction of the UEs in virtualized SCNs with MEC as a mixed integer nonlinear programming. Our aim is to minimize the total energy consumption of the UEs subject to minimum overall throughput of the network. To solve the problem efficiently, we convert it into a biconvex problem by adding auxiliary variable, which enables the derivation of an efficient iterative algorithm by two subproblems. Towards the first subproblem, we introduce local variables to handle the coupling constraint, and propose an alternating direction method of multipliers (ADMM) based distributed algorithm, where the closed form expressions of the optimal solutions in variables updating are derived. For the second subproblem, the closed form expressions of the optimal solution is also derived. Finally, the effectiveness of the proposed algorithm is demonstrated by extensive simulations with different system configurations.

title: Collaborative Task Offloading for Overloaded Mobile Edge Computing in Small Cell Networks
abstract: Mobile Edge Computing (MEC) enhances mobile cloud computing capabilities by fetching services close to the edge of the network, which adds 4C (Computing, Communication, Control and Caching) to the edges. MEC enabled small cell network is regarded as the key technology in future 5G networks where for rapid task execution, a user offloads their tasks to the nearest small BS (SBS) The research work regarding MEC enabled small cell network is still in its infancy. Recently, some researchers are trying to integrate MEC with small cell networks (SCNs) while ignoring the unlimited computation resource in a remote cloud and the computational capability of a single SBS MEC server, which has the limited capacity for handling huge number of user request. To effectively handle latency sensitive tasks and resources hungry mobile applications in small cell networks, two collaborative task offloading schemes of our proposed model is introduced in this paper. Our proposed collaborative model can make decision dynamically where the SBS MEC server collaborate with mobile devices or remote cloud for executing the computation tasks. The simulation results confirm that collaborative task offloading between mobile with SBS MEC scheme will reduce the average number of task failure more efficiently than other schemes and the collaborative task offloading between SBS MEC with cloud scheme will provide lower task execution latency than others in small cell networks.

title: Energy Efficient Resource Allocation for Mobile Edge Computing in NOMA Enabled Small Cell Networks
abstract: Mobile edge computing (MEC) has been regarded as a promising technology to improve the quality of service (QoS) of users by offloading their computation intensive and delay sensitive tasks to the wireless network edge. In this paper, we investigate the resource allocation problem for MEC in non orthogonal multiple access (NOMA) enabled small cell networks (SCNs) where the small cell users (SCUs) can offload their computational tasks to the associated small base station (SBS) via NOMA protocol. To minimize the total energy consumption of all users, we jointly consider power control, computation resource and subchannel resource allocation. The optimization problem can be solved by a two stage approach that consists of a heuristic computation resource allocation process and a subchannel allocation matching game coupled with power control. Simulation results reveal that our proposed algorithm can achieve higher performance than other simplified schemes in terms of saving system energy consumption.

title: HIQCO: A Hierarchical Optimization Method for Computation Offloading and Resource Optimization in Multi Cell Mobile Edge Computing Systems
abstract: Mobile Edge Computing (MEC) addresses the shortcomings of mobile devices in terms of computing performance and energy efficiency by offloading computation tasks to the edge of the network. In this paper, a multi cell MEC system is considered, where each small cell connects to a common MEC server, and the problem of minimizing system overhead in the MEC system is solved by jointly optimizing the computation offloading decisions, communication and computational resources. This problem is a mixed integer nonlinear programming problem (MINLP) Owning to the combined nature and complexity of this problem, we adopt a method to transform the original problem into two sub problems: (i) joint optimization computation offloading decision and channel assignment problem (JO CODCA) and (ii) joint optimization transmission power and computational resource allocation problem (JO TPCRA) For the transformed sub problems, we propose a hierarchical optimization method (HIQCO) that combines immune algorithm (IA) quasi convex optimization and convex optimization techniques. Experimental results show that the proposed HIQCO give better performance comparing with the other compared algorithms in terms of system overhead.

title: Two Tier Matching Game in Small Cell Networks for Mobile Edge Computing
abstract: Mobile edge computing (MEC) enables computing services at the network edge closer to mobile users (MUs) to reduce network transmission latency and energy consumption. Deploying edge computing servers in small base stations (SBSs) operators make profit by offering MUs with computing services, while MUs purchase services to solve their own computation tasks quickly and energy efficiently. In this context, it is of particular importance to optimize computing resource allocation and computing service pricing in each SBS, subject to its limited computing and communication resources. To address this issue, we formulate an optimization problem of computing resource management and trading in small cell networks and tackle this problem using a two tier matching. Specifically, the first tier targets at the association algorithm between MUs and SBSs to achieve maximum social welfare, and the second tier focuses on the collaboration algorithm among SBSs to make efficient usage of limited computing resources. We further show that the two proposed algorithms contribute to stable matchings and achieve weak Pareto optimality. In particular, we verify that the first algorithm arrives at a competitive equilibrium. Simulation results demonstrate that our proposed algorithms can achieve a better network social welfare than baseline algorithms while retaining a close optimal performance.

title: Dynamic Spectrum Sharing for Load Balancing in Multi Cell Mobile Edge Computing
abstract: Large scale mobile edge computing (MEC) systems require scalable solutions to allocate communication and computing resources to the users. In this letter we address this challenge by applying dynamic spectrum sharing among the base stations (BSs) together with local resource allocation in the cells. We show that the network wide resource allocation can be transformed into a convex optimization problem, and propose a distributed, hierarchical solution with limited information exchange among the BSs. Numerical results demonstrate that the proposed solution is superior to other baseline algorithms, when wireless and computing resource allocation is not jointly optimized, or the wireless resources allocated to the BSs are fixed.

title: Towards Joint Cell Selection and Task Offloading in Cellular IoT Systems with Edge Computing
abstract: Mobile Edge Computing (MEC) in cellular networks aims to bring computational capabilities close to end users to reduce the latency of applications on the Internet of Things (IoT) This is particularly crucial to computation intensive IoT broadband applications (e.g. video analytics, augmented reality, etc. demanding a data processing task to be performed within a given time threshold. In this regard, the task offloading problem has been investigated in the literature in order to achieve an appropriate trade off between energy and latency. However, there is a need for the joint design of task offloading mechanisms and cell selection algorithm as a mean to select the most appropriate to each device in order to meet delay requirements and fulfill resource constraints at the MEC server site. In this paper, we present the foreseen framework to tackle such a challenging problem.  Delay sensitive applications, internet of things, mobile edge computing, offloading.

title: Multi cell Collaborative Caching Based on Game Theory in Mobile Edge Computing
abstract: Mobile edge computing cache and computing services are pushed to the edge network closer to user, separating network data from the remote cloud network to provide better service to users. At present, most of the researches on edge computing barely consider cooperation between the cells. In order to study the benefits brought by the cooperation between multiple cells, this paper proposes a multi cell cellular network model developed as a Stackelberg game problem for the multi cell cache optimization problem. The cell control center and the base station groups are regarded as the dominant and follower of the game model, respectively, and their respective revenue functions are formulated. Because their income function is non continuous function, the traditional game theory solution can't be used to obtain the Nash equilibrium solution. This paper proposes an iterative alternating algorithm to solve the problem. The control center and the base station group respectively use the improved hybrid frog hopping algorithm (SMSA) and the greedy exchange algorithm (GSA) to solve the problem, and the two alternately iterate and finally obtain the approximate solution of the optimal solution of the model. Through numerical simulation experiments, we verify that the proposed algorithm outperforms the greedy algorithm proposed by other researches.