关键词： Mobile edge computing   buffer-aided relay networks   task computation offloading   convex optimization  

摘要： This paper studies buffer-aided relay-assisted Mobile Edge Computing (MEC) architecture as the fundamental system in hierarchical multi-hop MEC. We propose a stochastic offloading scheme where a buffer-aided relay, equipped with a computing server, randomly decides to process the tasks of a source node by its own server, or to forward them to the server of the next node in the hierarchy. We analyze the proposed scheme and formulate a problem to minimize the average response time in the system. We derive the feasible set and prove that the problem is a convex optimization problem which can be solved based on the one-dimensional search methods. Simulation results confirm the analytical results and demonstrate the effectiveness of the proposed scheme.

关键词： Cellular networks   Computation capacity   Device-to-device communications   Internet of Things   Mobile edge computing  

摘要： Recently, network computing, this is a kind of cloud computing suggested to torrent wireless services and cloud hosting (IoT) frameworks. Instead of sending data to remotely distributed domains, snugly-located network associations are required. Amid computation, a neural network is built in this project by exploring the disparity in user interest, which exploits the transition of sustainability-based cooperation game to Split subscribers in an adaptive direction in countless fog societies. By interpreting the majors of user pertinence in the communication part and using multidimensional network status dimensions such as the finest cooperative user, social and physical attributes, including local centrality and interconnection scale, can be reasonably evaluated based on users. Consequently, the correct inter-community and intra-community D2D cooperative communication mechanisms are designed to significantly simplify the use of internet services. Computational findings suggest that the actual mechanism exploits the professional relationship between customer and consumer comprehensively and efficiently enhances the computer system's operational efficiency at relatively low postponement concentrations. © 2020 IJSTR.

关键词： Mobile edge computing   cell-free massive MIMO   stochastic geometry   energy consumption   queueing theory  

摘要： 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.

关键词： Millimeter wave   hybrid beamforming   mobile edge computing   resource allocation   distributed implementation  

摘要： Mobile edge computing (MEC) has been identified as a key technique of next-generation wireless networks, which supports cloud computing along with other compelling service capabilities at the network & x2019;s edge with the objective of reducing the system delay. As one of the prospective candidates for new spectrum in next-generation networks, millimeter wave (mmWave) communications has been gaining significant attention as a benefit of its high rate. Hence we conceive a joint hybrid beamforming and resource allocation algorithm for mmWave MEC. Explicitly, we jointly optimize the analog beamforming vectors at the users, the analog and digital beamforming matrices at the base station (BS), the computation task offloading ratios and resource allocation at the MEC server for minimizing the maximum system delay subject to the affordable communication and computing budget. We conceive a powerful algorithm for solving this challenging nonconvex optimization problem with coupled constraints based on the penalty dual decomposition (PDD) technique. The proposed algorithm can be implemented in a parallel and distributed fashion. Our numerical results demonstrate the superiority of the proposed algorithm by quantifying the benefits of intrinsically amalgamating MEC with mmWave communications.

关键词： Computation offloading   Deep learning   Mobile edge computing   Energy and performance optimization  

摘要： With the benefit of partially or entirely offloading computations to a nearby server, mobile edge computing gives user equipment (UE) more powerful capability to run computationally intensive applications. However, a critical challenge emerged: how to select the optimal set of components to offload considering the UE performance as well as its battery usage constraints. In this paper, we propose a novel energy and performance efficient deep learning based offloading algorithm. The optimal offloading schemes of components based on remaining energy and its performance can be determined by our proposed algorithm. All of these considerations are modeled as a cost function;then, a deep learning network is trained to compute the solution by which the optimal offloading scheme can be determined. Experimental results show that the proposed method is superior to existing methods in terms of energy and performance constraints.

关键词： ATSA algorithm   Cloud computing   Data center   Mobile cloud computing   Mobile edge computing  

摘要： Mobile Edge Computing (MEC) is a developing technology in which Cloud Computing advantages were prolonged towards the edge of networks making use of mobile base stations. In Mobile Cloud Computing (MCC), the multiple numbers of data are produced by mobile users then that data is sent to the remote cloud to make an additional process around the Internet. Due to this process delay occurs because of long-distance. In this paper, Server Efficiency based Scheduling Algorithm for Mobile Edge Computing (SESA), is proposed to reduce the time consumption of running or scheduling the tasks. The Data Center has the scheduler to schedule the task based on the priority of the incoming task. Priority can be measured using Ageing based Task Scheduling Algorithm for Mobile Edge Computing (ATSA) algorithm. © 2020 SERSC.

关键词： Mobile edge computing  

摘要： Network function Virtualization (NFV) and Mobile Edge Computing (MEC) are promising 5G technologies to support resource-demanding mobile applications. In NFV, one must process the service function chain (SFC) in which a set of network functions must be executed in a specific order. Moreover, the MEC technology enables computation offloading of service requests from mobile users to remote servers to potentially reduce energy consumption and processing delay for the mobile application. This paper considers the optimization of the computation offloading, resource allocation, and SFC placement in the multi-site MEC system. Our design objective is to minimize the weighted normalized energy consumption and computing cost subject to the maximum tolerable delay constraint. To solve the underlying mixed integer and non-linear optimization problem, we employ the decomposition approach where we iteratively optimize the computation offloading, SFC placement and computing resource allocation to obtain an efficient solution. Numerical results show the impacts of different parameters on the system performance and the superior performance of the proposed algorithm compared to benchmarking algorithms. Copyright © 2020, The Authors. All rights reserved.

关键词： Mobile edge computing  

摘要： Recently, elevated LiDAR (ELiD) has been proposed as an alternative to local LiDAR sensors in autonomous vehicles (AV) because of the ability to reduce costs and computational requirements of AVs, reduce the number of overlapping sensors mapping an area, and to allow for a multiplicity of LiDAR sensing applications with the same shared LiDAR map data. Since ELiDs have been removed from the vehicle, their data must be processed externally in the cloud or on the edge, necessitating an optimized backhaul system that allocates data efficiently to compute servers. In this paper, we address this need for an optimized backhaul system by formulating a mixed-integer programming problem that minimizes the average latency of the uplink and downlink hop-by-hop transmission plus computation time for each ELiD while considering different bandwidth allocation schemes. We show that our model is capable of allocating resources for differing topologies, and we perform a sensitivity analysis that demonstrates the robustness of our problem formulation under different circumstances. Copyright © 2020, The Authors. All rights reserved.

关键词： Mobile edge computing  

摘要： Wireless powered mobile edge computing (WP-MEC) has been recognized as a promising technique to provide both enhanced computational capability and sustainable energy supply to massive low-power wireless devices. However, its energy consumption becomes substantial, when the transmission link used for wireless energy transfer (WET) and for computation offloading is hostile. To mitigate this hindrance, we propose to employ the emerging technique of intelligent reflecting surface (IRS) in WP-MEC systems, which is capable of providing an additional link both for WET and for computation offloading. Specifically, we consider a multi-user scenario where both the WET and the computation offloading are based on orthogonal frequency-division multiplexing (OFDM) systems. Built on this model, an innovative framework is developed to minimize the energy consumption of the IRS-aided WP-MEC network, by optimizing the power allocation of the WET signals, the local computing frequencies of wireless devices, both the sub-band-device association and the power allocation used for computation offloading, as well as the IRS reflection coefficients. The major challenges of this optimization lie in the strong coupling between the settings of WET and of computing as well as the unit-modules constraint on IRS reflection coefficients. To tackle these issues, the technique of alternative optimization is invoked for decoupling the WET and computing designs, while two sets of locally optimal IRS reflection coefficients are provided for WET and for computation offloading separately relying on the successive convex approximation method. The numerical results demonstrate that our proposed scheme is capable of monumentally outperforming the conventional WP-MEC network without IRSs. Quantitatively, about 80% energy consumption reduction is attained over the conventional MEC system in a single cell, where 3 wireless devices are served via 16 sub-bands, with the aid of an IRS comprising of 50 elements. C

关键词： autonomous vehicles   tactile internet   mobile edge computing   5G   mobility  

摘要： With the recent advances and development of autonomous control systems of cars, the design and development of reliable infrastructure and communication networks become a necessity. The recent release of the fifth-generation cellular system (5G) promises to provide a step towards reliability or a panacea. However, designing autonomous vehicle networks has more requirements due to the high mobility and traffic density of such networks and the latency and reliability requirements of applications run over such networks. To this end, we proposed a multilevel cloud system for autonomous vehicles which was built over the Tactile Internet. In addition, base stations at the edge of the radio-access network (RAN) with different technologies of antennas are used in our system. Finally, simulation results show that the proposed system with multilevel clouding can significantly reduce the round-trip latency and the network congestion. In addition, our system can be adapted in the mobility scenario.