关键词： Network function virtualization   mobile edge computing   delay   placement   routing  

摘要： Mobile Edge Computing (MEC) offers a way to shorten the cloud servicing delay by building the small-scale cloud infrastructures at the network edge, which are in close proximity to the end users. Moreover, Network Function Virtualization (NFV) has been an emerging technology that transforms from traditional dedicated hardware implementations to software instances running in a virtualized environment. In NFV, the requested service is implemented by a sequence of Virtual Network Functions (VNF) that can run on generic servers by leveraging the virtualization technology. Service Function Chaining (SFC) is defined as a chain-ordered set of placed VNFs that handles the traffic of the delivery and control of a specific application. NFV therefore allows to allocate network resources in a more scalable and elastic manner, offer a more efficient and agile management and operation mechanism for network functions and hence can largely reduce the overall costs in MEC. In this paper, we study the problem of how to place VNFs on edge and public clouds and route the traffic among adjacent VNF pairs, such that the maximum link load ratio is minimized and each user's requested delay is satisfied. We consider this problem for both totally ordered SFCs and partially ordered SFCs. We prove that this problem is NP-hard, even for the special case when only one VNF is requested. We subsequently propose an efficient randomized rounding approximation algorithm to solve this problem. Extensive simulation results show that the proposed approximation algorithm can achieve close-to-optimal performance in terms of acceptance ratio and maximum link load ratio.

关键词： Task analysis   Servers   Computational modeling   Backscatter   Resource management   Integrated circuit modeling   Time-frequency analysis   Mobile edge computing   backscatter communications   partial offloading   sum computation bits  

摘要： This letter introduces a backscatter assisted wirelessly powered mobile edge computing (MEC) network, where each edge user (EU) can offload task bits to the MEC server via hybrid harvest-then-transmit (HTT) and backscatter communications. In particular, considering a practical non-linear energy harvesting (EH) model and a partial offloading scheme at each EU, we propose a scheme to maximize the weighted sum computation bits of all the EUs by jointly optimizing the backscatter reflection coefficient and time, active transmission power and time, local computing frequency and execution time of each EU. By introducing a series of auxiliary variables and using the properties of the non-linear EH model, we transform the original non-convex problem into a convex one and derive closed-form expressions for parts of the optimal solutions. Simulation results demonstrate the advantage of the proposed scheme over benchmark schemes in terms of weighted sum computation bits.

关键词： Servers   Task analysis   Downlink   Uplink   Wireless communication   Heterogeneous networks   Edge computing   Mobile edge computing   heterogeneous network   latency   offloading   queueing theory   stochastic geometry  

摘要： The mobile edge computing (MEC) has been introduced for providing computing capabilities at the edge of networks to improve the latency performance of wireless networks. In this paper, we provide the novel framework for MEC-enabled heterogeneous networks (HetNets), composed of the multi-tier networks with access points (APs) (i.e., MEC servers), which have different transmission power and different computing capabilities. In this framework, we also consider multiple-type mobile users with different sizes of computation tasks, and they offload the tasks to a MEC server, and receive the computation resulting data from the server. We derive the successful edge computing probability (SECP), defined as the probability that a user offloads and finishes its computation task at the MEC server within the target latency. We provide a closed-form expression of the approximated SECP for general case, and closed-form expressions of the exact SECP for special cases. This paper then provides the design insights for the optimal configuration of MEC-enabled HetNets by analyzing the effects of network parameters and bias factors, used in MEC server association, on the SECP. Specifically, it shows how the optimal bias factors in terms of SECP can be changed according to the numbers of user types and tiers of MEC servers, and how they are different to the conventional ones that did not consider the computing capabilities and task sizes.

关键词： Predictive models   Servers   Decoding   Computer architecture   Feature extraction   Logic gates   Edge computing   Multi-head attention   popularity prediction   caching strategy   mobile edge computing   SocialCCN  

摘要： With the rapid growth of social network traffic, the design of an efficient caching strategy is crucial in the social content-centric network (SocialCCN). In order to design a more comprehensive popularity prediction caching strategy, in this letter, we proposed a novel architecture that integrates mobile edge computing (MEC) in SocialCCN (MeSoCCN) and proposed multi-head attention based popularity prediction caching strategy in MeSoCCN. Firstly, we proposed a multi-head attention based popularity prediction model (MAPP) that considers multi-dimensional features including history and future popularity, social relationships, and geographic location to predict content popularity. Then, we design a caching strategy based on the prediction results of MAPP. The simulation results show that the proposed MAPP model achieves lower predictive error and the proposed predictive caching strategy improves cache hit rate and reduces hop redundancy in the network.

关键词： mobile edge computing   computation offloading   offloading strategy   genetic algorithm  

摘要： With the rapidly development of mobile cloud computing (MCC), the Internet of Things (IoT), and artificial intelligence (AI), user equipment (UEs) are facing explosive growth. In order to effectively solve the problem that UEs may face with insufficient capacity when dealing with computationally intensive and delay sensitive applications, we take Mobile Edge Computing (MEC) of the IoT as the starting point and study the computation offloading strategy of UEs. First, we model the application generated by UEs as a directed acyclic graph (DAG) to achieve fine-grained task offloading scheduling, which makes the parallel processing of tasks possible and speeds up the execution efficiency. Then, we propose a multi-population cooperative elite algorithm (MCE-GA) based on the standard genetic algorithm, which can solve the offloading problem for tasks with dependency in MEC to minimize the execution delay and energy consumption of applications. Experimental results show that MCE-GA has better performance compared to the baseline algorithms. To be specific, the overhead reduction by MCE-GA can be up to 72.4%, 38.6%, and 19.3%, respectively, which proves the effectiveness and reliability of MCE-GA.

关键词： Task analysis   NOMA   Energy consumption   Wireless power transfer   Computational modeling   Time division multiple access   Edge computing   Mobile edge computing   NOMA   user cooperation   wireless power transfer  

摘要： Integrating mobile edge computing (MEC) and wireless power transfer (WPT) is a promising technology that provide sustainable energy supply and cloud-like computing services at network edge. In this article, we propose a non-orthogonal multiple access (NOMA)-aided user cooperative scheme in a three-node WPT-MEC system, which is composed of a far user (FU), a near user (NU) and an access point (AP). What is innovative is that the FU employs NOMA so that it can simultaneously offload data to NU and AP which can overcome the doubly near-far effect of FU. We design an energy consumption minimization problem by jointly optimizing offloading time, task allocation and computation frequency. The optimal solution for the non-convex problem is obtained by some ingenious mathematical methods. Simulations are conducted to demonstrate that the proposed joint design has obvious improvement over other benchmark schemes.

关键词： Computation offloading   Mobile edge computing   Density allocation   Mode selection  

摘要： Cooperation between the mobile edge computing (MEC) and the mobile cloud computing (MCC) in offloading computing could improve quality of service (QoS) of user equipments (UEs) with computation-intensive tasks. In this paper, in order to minimize the expect charge, we focus on the problem of how to offload the computation-intensive task from the resource-scarce UE to access point's (AP) and the cloud, and the density allocation of APs' at mobile edge. We consider three offloading computing modes and focus on the coverage probability of each mode and corresponding ergodic rates. The resulting optimization problem is a mixed-integer and non-convex problem in the objective function and constraints. We propose a low-complexity suboptimal algorithm called Iteration of Convex Optimization and Nonlinear Programming (ICONP) to solve it. Numerical results verify the better performance of our proposed algorithm. Optimal computing ratios and APs' density allocation contribute to the charge saving.

关键词： Ultra dense network   Heterogeneous network   Radio access network   Mobile edge computing   Dynamic spectrum coordination   5G   Semantic-based framework  

摘要： Ultra Dense Networks with flexible heterogeneous technologies (HetUDN) for the Radio Access Network (RAN) are prospective candidate architectures that promise to bring the required network flexibility and to support the transition to 5G networks. In the same time, Multi-access Edge Computing (MEC) is a recent paradigm shift that pushes the main focal point of mobile systems from centralized Mobile Cloud Computing (MCC) towards the network edge. Network softwarization enables the operation of new computational elements such as base stations and access points at the MEC, whereas edge computing, network control and storage promise to enable computation-intensive and latency-critical applications for resource constrained mobile devices. In this paper, we employ a novel disaggregated 5G architecture and apply a semantic-based coordination approach from the network edge. We consider semantics as the key enabling principle for the implementation of spectrum coordination schemes addressing the coexistence of heterogeneous technologies at the edge of 5G HetUDN wireless networks. A Semantic-based Framework for dynamic Spectrum Coordination (SFSC) deployed according to the MEC approach is proposed dealing with agile complex system resource management. Practical experimental evidence evaluated on a networking testbed is provided, proving that a semantic MEC middleware is capable of delivering novel advanced computation communication paradigms as needed for 5G networks.

关键词： Mobile edge computing  

摘要： By pushing computation, cache, and network control to the edge, mobile edge computing (MEC) is expected to play a leading role in fifth generation (5G) and future sixth generation (6G). Nevertheless, facing ubiquitous fast-growing computational demands, it is impossible for a single MEC paradigm to effectively support high-quality intelligent services at end user equipments (UEs). To address this issue, we propose an air-ground collaborative MEC (AGC-MEC) architecture in this article. The proposed AGC-MEC integrates all potentially available MEC servers within air and ground in the envisioned 6G, by a variety of collaborative ways to provide computation services at their best for UEs. Firstly, we introduce the AGC-MEC architecture and elaborate three typical use cases. Then, we discuss four main challenges in the AGC-MEC as well as their potential solutions. Next, we conduct a case study of collaborative service placement for AGC-MEC to validate the effectiveness of the proposed collaborative service placement strategy. Finally, we highlight several potential research directions of the AGC-MEC. © 2021, CC BY.

关键词： Mobile edge computing  

摘要： In mobile edge computing (MEC) systems, users offload computationally intensive tasks to edge servers at base stations. However, with unequal demand across the network, there might be excess demand at some locations and underutilized resources at other locations. To address such load-unbalanced problem in MEC systems, in this paper we propose virtual machines (VMs) sharing across base stations. Specifically, we consider the joint VM placement and pricing problem across base stations to match demand and supply and maximize revenue at the network level. To make this problem tractable, we decompose it into master and slave problems. For the placement master problem, we propose a Markov approximation algorithm MAP on the design of a continuous time Markov chain. As for the pricing slave problem, we propose OPA - an optimal VM pricing auction, where all users are truthful. Furthermore, given users’ potential untruthful behaviors, we propose an incentive compatible auction iCAT along with a partitioning mechanism PUFF, for which we prove incentive compatibility and revenue guarantees. Finally, we combine MAP and OPA or PUFF to solve the original problem, and analyze the optimality gap. Simulation results show that collaborative base stations increases revenue by up to 50%. © 2021, CC BY.