关键词： Availability-aware   service function chain   mobile edge computing  

摘要： With the advent of Network Function Virtualization (NFV) and Mobile Edge Computing (MEC), outsourcing network functions (NFs) to the MEC is becoming popular among network service providers (NSPs), since it brings the scalability and flexibility for NF deployment and maintenance. Each user's request will go through a service function chain (SFC), which consists of several virtual network functions (VNFs, software substitutions for traditional hardware-based middleboxes) in a specific order, and then get a response. Unlike conventional hardware-based middleboxes, VNFs are not very reliable due to potential software faults and host malfunctions. Thus, a sensible way is to add redundancy for the primary VNFs of an SFC to enhance its availability. Nevertheless, which MEC node to place each VNF, and how many backup instances are enough to ensure the availability requirement of each SFC? These issues have not yet been resolved. In this article, we present the availability-aware provision of SFC (APoS) in the MEC environment with the primary goal of maximizing the number of served requests while meeting the requirements and reliability expectations of SFCs. For the APoS, we have primarily addressed the following two fundamental challenges: (i) First, how to efficiently map these primary and backup VNFs to meet the availability requirements of SFCs? At this point, we formulate it as an integer nonlinear programming (INLP) under the limitation of each MEC node's resources. This issue is NP-hard, and a novel binary N-back search method is proposed to derive the optimal solution for the primary and backup VNFs mapping;(ii) Second, how can we reduce the latency for users to access their desired SFCs? Then, we investigate how to minimize the average delay for all requests in each time slot. To solve this problem, we design an online service switching (OSS) method, which jointly considers the queuing delay, communication delay, and switching delay. It achieves the optimal solution

关键词： drone edge cluster   mobile edge computing   task scheduling   deep reinforcement learning  

摘要： The heterogeneity of unmanned aerial vehicle (UAV) nodes and the dynamic service demands make task scheduling particularly complex in the drone edge cluster (DEC) scenario. In this paper, we provide a universal intelligent collaborative task scheduling framework, named DECCo, which schedules dynamically changing task requests for the heterogeneous DEC. Benefiting from the latest advances in deep reinforcement learning (DRL), DECCo autonomously learns task scheduling strategies with high response rates and low communication latency through a collaborative Advantage Actor-Critic algorithm, which avoids the interference of resource overload and local downtime while ensuring load balancing. To better adapt to the real drone collaborative scheduling scenario, DECCo switches between heuristic and DRL-based scheduling solutions based on real-time scheduling performance, thus avoiding suboptimal decisions that severely affect Quality of Service (QoS) and Quality of Experience (QoE). With flexible parameter control, DECCo can adapt to various task requests on drone edge clusters. Google Cluster Usage Traces are used to verify the effectiveness of DECCo. Therefore, our work represents a state-of-the-art method for task scheduling in the heterogeneous DEC.

关键词： Mobile edge computing   Cooperative computation offloading   Genetic algorithm   Data compression  

摘要： Cooperative computation offloading (CCO) is a technique to improve computation offloading performance in edge networks through collaboration between edge nodes. CCO can achieve better resource utilization, balance the computational load and further reduce delay to improve the service experience of user equipment (UE). In this paper, we investigate the problem of collaborative computing task offloading scheme and computing resource allocation in mobile edge computing and propose a data compression cooperation computing offloading (DCCO) scheme. To reduce the amount of data transmitted on the UE offloading link, we introduce a Data Compression into CCO and present the computational offloading strategy, collaborative offloading and computational resource allocation problems with the goal of minimizing the weighted sum of delay and energy consumption of the UE under the constraints of UE delay and energy consumption. And an improved genetic algorithm is proposed to solve the problem which is a non-convex mixed-integer problem with binary and continuous variables. The offloading strategy and computational resource allocation correspond to the genes in the genetic algorithm chromosome. The simulation results show that the DCCO scheme can reduce the offloading cost up to 11% compared with the existing schemes. It effectively improves the computation offloading performance of edge network computing.

关键词： cloud computing   edge server   mobile edge computing   security   privacy  

摘要： Human-centered systems play an important role in the modern world, for example, driverless car, autonomous and smart vehicles, drones, and robotics. The internet of things environment demands a faster real-time response depending on the applications processed in a particular duration. Mobile edge computing (MEC) allows a user to get a real-time response as compared with cloud computing (CC), although ensuring a number of security attributes in MEC environment remains challenging. In this article, a protocol is designed to achieve mutual authentication, anonymous communication, and security against traceability, as these are very crucial factors to ensure the security of data and user's privacy. Moreover, the proposed scheme ensures mutual authentication between a mobile user and an edge server along with the user's anonymity and untraceability. The proof of security and evaluation of performance of the scheme validates that it ensures security attributes and improves efficiency in terms of communication and computation overheads.

关键词： Mobile edge computing   Task offloading   Cooperation   Energy efficiency   Forwarding method  

摘要： Mobile edge computing (MEC) has emerged as a promising paradigm to improve the energy efficiency for latency-constraint computation. This paper proposes a novel user cooperation approach in both computation and communication for MEC, based on the three-node cooperative offloading architecture, which consists of two mobile users and a computing access point (CAP). The mobile application tasks can be executed locally or be offloaded to either a cooperative mobile user or CAP for remote execution. The cooperative task offloading problem is investigated to minimize the energy consumption of mobile users while satisfying the execution delay. The problem is formulated as a mixed integer programming, and the NP-hardness is provided by reducing it to a 0-1 knapsack problem. This paper also provides an optimal algorithm based on dynamic programming and an efficient heuristic approach. Numerical results show that the cooperative offloading scheme outperforms the local computing method by 66.4% on the energy consumption of mobile nodes. Furthermore, the proposed heuristic algorithm can achieve near-optimal performance under different network settings.

关键词： 5G   computation offloading   game slicing   millimeter wave   mobile edge computing   potential game   vehicular network  

摘要： Smart vehicles require constantly running heavy vehicular computations with their limited computation/energy resources. 5G vehicular networks have potential to resolve the issue, by letting the vehicular tasks offloaded to 5G mobile edge computing (MEC) servers. To better support vehicular computation offloading, this paper proposes a road-side 5G infrastructure consisting of multiple millimeter-wave (mmWave) small-cell base stations (BSs) and a cellular mid-band based macro-cell BS where each BS is equipped with an MEC server. Then, the vehicles with mmWave/mid-band dual interfaces can decide which BS to choose for offloading. We propose a decentralized offloading decision mechanism where each vehicle tries to minimize the time-energy joint cost with three choices: local computing, offloading to a small-cell MEC, offloading to a macro-cell MEC. In particular, we model the problem as an ordinal potential game, derive its potential function to ensure the existence of and finite-time convergence to a Nash equilibrium (NE), analyze its Price-of-Anarchy, and develop an iterative offloading decision update algorithm. In doing so, we also consider slicing the global game into multiple non-overlapping smaller games and running them in parallel, to investigate the best slicing strategy. Our extensive simulations show the game's real-time convergence to an NE, reveal the NE's near-optimal performance, and present the efficacy of the proposed game slicing.

关键词： Mobile edge computing   MEC server deployment   resource placement   profit maximization  

摘要： With the explosive growth of mobile applications, the development of mobile edge computing (MEC) has been greatly promoted since it can ably improve the quality of service for mobile applications by providing low latency and high-quality computation services. Most existing works focus on improving the efficiency of MEC with an assumption that the MEC servers have already been deployed. However, without appropriate deployment of MEC servers, the profitability of the MEC system can be significantly restrained, which hinders the rapid promotion of the MEC. To address this issue, we formulate an MEC server deployment problem for the MEC operator as a revenue maximization problem. Firstly, we model and analyze the various factors that affect the revenue. Secondly, we formulate a revenue maximization problem, which is NP-hard, but it is proved to be convex with respect to the total available computation units. Based on this feature, we propose a three-layer optimization algorithm, named EDM, in which the location, the deployed computation units, and the wholesaled computation resources are determined gradually, to maximize the total revenue. Experimental results demonstrate that the proposed EDM algorithm has significant advantages on revenue improvement compared to competitive benchmarks.

关键词： Mobile edge computing   computation offloading   quality of service   resource allocation   federated learning  

摘要： Mobile edge computing (MEC) driven by 5G cellular systems has recently emerged as a promising paradigm, enabling mobile devices (MDs) with limited computing resources to offload various computation-intensive tasks (such as autopilot, online game) to edge servers to enhance the data processing capabilities of MDs. However, the uncertainty of wireless channel state and data volume of offloading tasks, as well as the data security privacy of offloading tasks, bring serious challenges to computation offloading in MEC. In this article, we consider a time-varying MEC scenario and formalize the delay and energy consumption during the computation offloading process as a joint optimization problem. Then the optimization problem is decomposed into two sub-problems: intelligent task prediction and resource allocation. Different from traditional methods, we improve the federated learning (FL) algorithm and propose a thoughtful cloud-edge-client FL task prediction mechanism based on Bidirectional Long Short-Term Memory. Each participating MD trains the model locally without uploading data to the server, and periodically aggregates the model in the edge and in the cloud. The algorithm both eliminates the need to solve complex optimization problems and ensures user privacy security. Finally, experimental results show that our proposed algorithm significantly outperforms other benchmark algorithms in energy efficiency.

关键词： delay aware   energy consumption aware   load balancing   mobile edge computing   power wireless sensor networks  

摘要： Aiming at the problems of high access latency, high energy consumption, and deteriorating user service quality in traditional wireless power sensor networks, the mobile edge computing (MEC) is introduced to establish a joint optimization model of access latency and network energy consumption, with objective to minimize the joint function of latency and energy consumption. Then, the total resource processing capacity of the central unit/distribution unit is determined by controlling the waiting latency in the load transfer process under the common constraints of the remaining resources of the RF remote unit, the load queuing latency, and the remaining resources of the central unit/distribution unit. And then, based on the resource utilization of the MEC server, the energy consumption of the sensor devices is reduced by reducing the number of active MEC servers. The influence of the weight coefficient on the objective function and the comparison of the algorithm under different traffic flows and times are analyzed. The results show that the proposed allocation mechanism is better than other comparison algorithms in reducing access latency and energy consumption. Therefore, this mechanism can effectively improve the communication quality of the sensors and the efficiency of the system, extend the life of the sensor equipment, and reduce the network cost. © The Author(s) 2023.

关键词： Mobile edge computing   Optimal stopping theory   Deep Q network   Task offloading   Sequential decision making  

摘要： Offloading tasks and data is an important use case for Mobile Edge Computing (MEC) that benefits a wide variety of mobile applications on different platforms, including autonomous vehicles and smartphones. A moving vehicle may pass through multiple Roadside Units (RSUs) and the MEC servers located within them as it travels. On-board applications generate structured tasks and can offload them to observable servers. However, since the mobile node cannot determine the information of the global server, choosing the best server becomes a key issue. In this paper, we address the problem of the offloading threshold in traditional Optimal Stopping Theory (OST) based decision models that cannot adapt to the environment. We use Deep Q-Network (DQN) to provide the model with the optimal offloading threshold for adaptive environments and propose a DQN-based Adaptive Threshold Multi-task Offloading approach (DQN-ATMOO). This method uses the principle of OST to solve the offloading order decision problem, divides structured tasks into sub-tasks, and executes offloading in sequence, with the goal of maximizing the probability of offloading to the best server and minimizing the total offloading delay. We experimentally compare and evaluate the proposed model using simulated and real datasets. The results show that the proposed model can be effectively implemented on mobile nodes and significantly reduces the total expected processing time.