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Federated learning (FL) in mobile edge computing has emerged as a promising machine-learning paradigm in the Internet of Things, enabling distributed training without exposing private data. It allows multiple mobile devices (MDs) to collaboratively create a global model. FL not only addresses the issue of private data exposure but also alleviates the burden on a centralized server, which is common in conventional centralized learning. However, a critical issue in FL is the imposed computing for local training on multiple MDs, which often have limited computing capabilities. This limitation poses a challenge for MDs to actively contribute to the training process. To tackle this problem, this paper proposes an adaptive dataset management (ADM) scheme, aiming to reduce the burden of local training on MDs. Through an empirical study on the influence of dataset size on accuracy improvement over communication rounds, we confirm that the amount of dataset has a reduced impact on accuracy gain. Based on this finding, we introduce a discount factor that represents the reduced impact of the size of the dataset on the accuracy gain over communication rounds. To address the ADM problem, which involves determining how much the dataset should be reduced over classes while considering both the proposed discounting factor and Kullback–Leibler divergence (KLD), a theoretical framework is presented. The ADM problem is a non-convex optimization problem. To solve it, we propose a greedy-based heuristic algorithm that determines a suboptimal solution with low complexity. Simulation results demonstrate that our proposed scheme effectively alleviates the training burden on MDs while maintaining acceptable training accuracy. [ABSTRACT FROM AUTHOR]

As the fundamental mechanism in mobile edge computing (MEC), task offloading strategy is of great significance to the quality of computing services provided by MEC systems. When coping with workflow applications, the precedence relations among tasks increase the difficulty in developing task offloading strategies. This paper studies the problem of safety-critical task offloading for workflow applications in a MEC environment. Considering the precedence constraints on workflow tasks and the overhead of security services, we formulate the safety-critical workflow offloading model with the objective of jointly optimizing the total completion time and energy consumption. By using a task sequence to represent a feasible solution to the optimization model, we introduce a family of heuristics to solve the safety-critical workflow offloading problem under precedence constraints upon workflow tasks. Depending on whether the offloading solution satisfies the precedence relations among workflow tasks, task sequences can be classified into two categories, i.e., precedence-aware and precedence-unaware offloading solutions. With the satisfaction of precedence constraints, a family of heuristics by using a precedence-aware strategy and a precedence-unaware strategy is designed to offload safety-critical workflow tasks. Given an offloading sequence and the operating conditions of MEC servers, the heuristic algorithms select the currently best MEC server to offload workflow tasks. Experimental results justify the performance of the proposed algorithms in solving the safety-critical workflow offloading problem under precedence constraints. [ABSTRACT FROM AUTHOR]

As more and more intelligent devices are put into the field of power system, the number of connected nodes in the power network is increasing exponentially. Under the background of smart grid cooperation across power areas and voltage levels, how to effectively process the massive data generated by smart grid has become a difficult problem to ensure the stable operation of power system. In the complex calculation process of power system, the operation time of complex calculation can not be shortened to the greatest extent, and the execution efficiency can not be improved. Therefore, this paper proposes a two-phase heuristic algorithm based on edge computing. In solving the virtual machine sequence problem, for the main partition and the coordination partition, the critical path algorithm is used to sort the virtual machines to minimize the computing time. For other sub-partitions, the minimum cut algorithm is used to reduce the traffic interaction of each sub-partition. In the second stage of the virtual machine placement process, an improved best fit algorithm is used to avoid poor placement of virtual machines across physical machine configurations, resulting in increased computing time. Through the experiment on the test system, it is proved that the calculation efficiency is improved when the coordinated partition calculation belongs to the target partition. Because the edge computing is closer to the data source, it can save more data transmission time than cloud computing. This paper provides an effective algorithm for power system distributed computing in virtual machine configuration in edge computing, which can effectively reduce the computing time of power system and improve the efficiency of system resource utilization. [ABSTRACT FROM AUTHOR]

In many IoT scenarios, the resources of terminal devices are limited, and it is difficult to provide services with low latency and low energy consumption. Mobile edge computing is an effective solution by offloading computing tasks to edge server processing. There are some problems in the existing offloading decision algorithms: the offloading decision method based on heuristic algorithms cannot dynamically adjust the policy in the changing environment; the offloading algorithm based on deep reinforcement learning will lead to slow convergence and poor exploration effect due to the problem of dimension explosion. To solve the above problems, this paper designs an offloading decision algorithm to make dynamic decisions in a mobile edge computing network with multi-device access. The algorithm comprehensively considers the energy consumption of terminal equipment, offloading overhead, average delay and success rate of task completion, aiming to achieve the highest total revenue of the whole system in a period of time. In this work, the online offloading problem is abstracted as a Markov decision process. Based on the Double Dueling Deep Q-Network (D3QN) algorithm, the offloading decision is designed to adapt to the highly dynamic environment of the edge computing network and solve the problem of high state space complexity. In addition, this paper innovatively introduces a clustering algorithm into deep reinforcement learning (DRL) to preprocess the action space and solve the explosion problem of the action space dimension caused by the increase of terminal devices. The experimental results show that the proposed algorithm is superior to the baseline strategies such as Deep Q-Network (DQN) algorithm in convergence speed and total reward. [ABSTRACT FROM AUTHOR]

Edge computing is an emerging promising computing paradigm that brings computation and storage resources to the network edge, hence significantly reducing the service latency and network traffic. In edge computing, many applications are composed of dependent tasks where the outputs of some are the inputs of others. How to offload these tasks to the network edge is a vital and challenging problem which aims to determine the placement of each running task in order to maximize the Quality-of-Service (QoS). Most of the existing studies either design heuristic algorithms that lack strong adaptivity or learning-based methods but without considering the intrinsic task dependency. Different from the existing work, we propose an intelligent task offloading scheme leveraging off-policy reinforcement learning empowered by a Sequence-to-Sequence (S2S) neural network, where the dependent tasks are represented by a Directed Acyclic Graph (DAG). To improve the training efficiency, we combine a specific off-policy policy gradient algorithm with a clipped surrogate objective. We then conduct extensive simulation experiments using heterogeneous applications modelled by synthetic DAGs. The results demonstrate that: 1) our method converges fast and steadily in training; 2) it outperforms the existing methods and approximates the optimal solution in latency and energy consumption under various scenarios. [ABSTRACT FROM AUTHOR]

In the Internet of Vehicles (IoV) scenarios, vehicles are equipped with computing resources to support vehicle-oriented IoV applications. Meanwhile, these computing resources can be also leveraged to enhance the task processing performance for the devices in Multi-access Edge Computing (MEC) scenarios, and alleviate the load of edge servers. Existing research works rarely consider the resource utilization of moving vehicles, which can be an important complement to the MEC schemes with the assistance of parked vehicles. In this paper, a joint task offloading and resource allocation scheme is proposed for a parked-and-moving-vehicles-assisted MEC scenario consisting of multiple devices, parked vehicles, and moving vehicles covered by a Base Station (BS) equipped with an edge server. The tasks of the devices can be either offloaded to the BS or further offloaded from the BS to the vehicles. The service time that a moving vehicle can provide its task offloading service before it moves out of the coverage of the BS, are taken into account of our system model. The aim of our scheme it to minimize the total priority-weighted task processing delay for all the devices through offloading the tasks to the edge server or to the vehicles, allocating the wireless channels of the BS, and allocating the computing resource of the edge server and the vehicles. A generalized benders decomposition and reformulation linearization-based iterative algorithm is designed to obtain the optimal solution to the optimization problem, and a two-stage heuristic algorithm is also given to provide near-optimal solutions with low computational complexity. The simulation results demonstrate the superiority of our scheme in seven different scenarios by comparing it with three other schemes. [ABSTRACT FROM AUTHOR]

Mobile vehicles have been considered as potential edge servers to provide computation resources for the emerging Intelligent Transportation System (ITS) applications. However, how to fully utilize the mobile computation resources to satisfy the real-time arrived computation requests has not been explored yet. This work will address the critical challenges of limited computation resources, stringent computation delay and unknown requirement statistics of real-time tasks in realistic vehicular edge computing scenarios. Specifically, we design a distributed clustering strategy to classify vehicles into multiple cooperative edge servers according to the available computation resources, effective connection time and the distribution of tasks’ expected deadlines. Then, a ‘Less than or Equal to’ Generalized Assignment Problem (i.e., LEGAP) is formulated to maximize the system service revenue, and on this basis, we propose an offline Bound-and-Bound based Optimal (BBO) algorithm to make periodical scheduling with a global view of tasks’ requirement statistics. The quick branching is conducted by following a greedy solution and the upper bound at each branch is derived by solving a multiple-choice knapsack problem. In addition, we present an online heuristic algorithm which makes real-time offloading decision with the guarantees that the resource capacities of all the computing servers are never exceeded with new tasks arriving. Through comparing with the other four online algorithms, the BBO algorithm achieves the highest service revenues by offloading tasks with shorter delays, and the online heuristic algorithm has the best performance in improving the service ratios. [ABSTRACT FROM AUTHOR]

Mobile edge computing (MEC) is a promising paradigm that helps mobile terminals save energy and increase battery life by offloading tasks to MEC servers. To obtain full benefit from MEC, the joint optimization of offloading decision-making and resource allocation, while considering user mobility, is crucial and challenging. The objective of this paper was to propose mobility-aware and energy-efficient offloading schemes for MEC in cellular networks. The proposed schemes helped to make offloading decisions, allocate transit power, and schedule transmissions among multiple moving users. An optimal scheme was proposed, in terms of minimizing total energy consumed by all users, which was based on Mixed-Integer Programming (MIP). Considering the high computational complexity of solving MIP problems, a practical heuristic scheme was also proposed. Simulation results showed the effectiveness in reducing energy consumption of both the proposed optimal and heuristic schemes and demonstrated the importance of mobility awareness as well. • The variations in transmission power caused by end-user mobility is considered. • An optimal solution for joint resource allocation is proposed. • A low-complexity heuristic algorithm is proposed for practical applications. • It is demonstrated that mobility awareness enhances energy efficiency effectively. [ABSTRACT FROM AUTHOR]

Parked vehicle-assisted multi-access edge computing (PVMEC) is a paradigm that exploits the under-utilized resources of parked vehicles (PVs) to assist MEC servers for offloaded task execution. This article investigates a partial offloading strategy for multi-user PVMEC, where each mobile device (MD)’s task can be partially offloaded to the MEC server or a nearby PV. We formulate the system utility maximization problem with joint consideration of offloading decisions, offloading ratio, and resource allocation. Considering the complexity and privacy issues of centralized scheme as well as the inherent resource contention among MDs, we adopt game-theoretic approach to devise a low-complexity distributed offloading scheme, in which the offloading decision-making problem is modeled as an exact potential game, and the optimal offloading ratio and resource allocation are determined using a subgradient method. Finally, simulation results show that our proposed approach can significantly improve the system utility over traditional baselines. [ABSTRACT FROM AUTHOR]

Nowadays, computation offloading has become a research focus since it has the potential to solve the challenges faced when dealing with computation-intensive applications in the Internet of Vehicles (IoVs), especially in the 5G or future network environment. However, major issues still exist and the performance of main metrics can be improved to better adapt to the practical scenarios. This paper focuses on achieving resource- and time-efficient computation offloading in IoVs by boosting the cooperation efficiency of vehicles. Firstly, a fuzzy logic-based pricing strategy is designed to evaluate the cooperation tendency and capability of each vehicle from multiple aspects. Vehicles are encouraged to participate in the offloading process even if they are in a disadvantageous position compared to other vehicles. Secondly, a Max-Min fairness-oriented approach is proposed to find the most suitable offloading decision, and vehicles with poor cooperation capabilities are guaranteed to be treated equally in the offloading. Finally, two heuristic algorithms are presented to solve the problem with applicable complexity and to suit the practical IoV environment. Extensive simulation results prove that the proposed approach achieves remarkable performance improvements in terms of delay, service cost and the resource utilization ratios of vehicles. [ABSTRACT FROM AUTHOR]

The computing power of mobile devices is too limited to execute computation tasks fast. Mobile edge computing (MEC) allows mobile devices to offload tasks to near servers to reduce the completion time of the tasks (a.k.a makespan). The input data of some critical tasks should be encrypted before the offloading. Aiming at the security critical tasks in the MEC composed of multiple servers, this paper addresses to minimize the makespan by scheduling security-critical tasks. We provide the formulation of the problem which is generally an integer programming problem, and three effective composite heuristics CH1–CH3 are proposed to solve the problem. Task permutations are considered as solutions. We construct a greedy heuristic algorithm to calculate the value of the objective. These three composite heuristics consist of two phases: solution initialization and solution improvement. In the first phase, the solutions of all the proposed algorithms are generated by a task arrangement rule called Biggest data Task First (BTF), and then in the second phase, improved by three searching methods based on different neighborhoods including a insertion neighborhood, a swap neighborhood and a hybrid neighborhood, respectively. Experimental results show that CH1–CH3 outperform the well-known RoundRobin algorithm. Particularly, BTF is demonstrated to initialize highly qualified solutions, making contributions to the high effectiveness. Meanwhile, all the improvement methods are justified to be effective and the method based on the hybrid neighborhood achieves the best effectiveness. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) technology provisions unprecedented opportunities to evolve the interconnection among human beings. However, the latency brought by unstable wireless networks and computation failures caused by limited resources on IoT devices prevents users from experiencing high efficiency and seamless user experience. To address these shortcomings, the integrated Mobile Edge Computing (MEC) with remote clouds is a promising platform to enable delay-sensitive service provisioning for IoT applications, where edge-clouds (cloudlets) are co-located with wireless access points in the proximity of IoT devices. Thus, computation-intensive and sensing data from IoT devices can be offloaded to the MEC network immediately for processing, and the service response latency can be significantly reduced. In this paper, we first formulate two novel optimization problems for delay-sensitive IoT applications, i.e., the total utility maximization problems under both static and dynamic offloading task request settings, with the aim to maximize the accumulative user satisfaction on the use of the services provided by the MEC, and show the NP-hardness of the defined problems. We then devise efficient approximation and online algorithms with provable performance guarantees for the problems in a special case where the bandwidth capacity constraint is negligible. We also develop efficient heuristic algorithms for the problems with the bandwidth capacity constraint. We finally evaluate the performance of the proposed algorithms through experimental simulations. Experimental results demonstrate that the proposed algorithms are promising in reducing service delays and enhancing user satisfaction, and the proposed algorithms outperform their counterparts by at least 10.8 percent. [ABSTRACT FROM AUTHOR]

Edge computing has become one of the key enablers for ultra-reliable and low-latency communications in the industrial Internet of Things in the fifth generation communication systems and is also a promising technology in the future sixth generation communication systems. In this work, we consider the application of edge computing to smart factories for mission critical task offloading through wireless links. In such scenarios, although high end-to-end delays from the generation to completion of tasks happen with low probability, they may incur severe casualties and property loss and should be seriously treated. Inspired by the risk management theory widely used in finance, we adopt the Conditional Value at Risk to capture the tail of the delay distribution. An upper bound of the Conditional Value at Risk is derived through analysis of the queues both at the devices and the edge computing servers. We aim to find out the optimal offloading policy taking into consideration both the average and the worst-case delay performance of the system. Given that the formulated optimization problem is a non-convex mixed integer nonlinear programming problem, a decomposition into subproblems is performed and a two-stage heuristic algorithm is proposed. The simulation results validate our analysis and indicate that the proposed algorithm can reduce the risk in both the queueing and end-to-end delay. [ABSTRACT FROM AUTHOR]

The explosive growth of mobile devices promotes the prosperity of novel mobile applications, which can be realized by service offloading with the assistance of edge computing servers. However, due to limited computation and storage capabilities of a single server, long service latency hinders the continuous development of service offloading in mobile networks. By supporting multi-server cooperation, Pervasive Edge Computing (PEC) is promising to enable service migration in highly dynamic mobile networks. With the objective of maximizing the system utility, we formulate the optimization problem by jointly considering the constraints of server storage capability and service execution latency. To enable dynamic service placement, we first utilize Lyapunov optimization method to decompose the long-term optimization problem into a series of instant optimization problems. Then, a sample average approximation-based stochastic algorithm is proposed to approximate the future expected system utility. Afterwards, a distributed Markov approximation algorithm is utilized to determine the service placement configurations. Through theoretical analysis, the time complexity of our proposed algorithm is linear to the number of users, and the backlog queue of PEC servers is stable. Performance evaluations are conducted based on both synthetic and real trace-driven scenarios, with numerical results demonstrating the effectiveness of our proposed algorithm from various aspects. [ABSTRACT FROM AUTHOR]

With the development of high-speed railways (HSRs), people's lives have become more convenient. At the same time, people are demanding more data services on trains in terms of wireless communications and mobile computation. Recently, Multi-access Edge Computing (MEC), a new computing paradigm, has the great potential to improve the user experience of computing services by placing the cloud infrastructures closer to end-users so that users can offload their computation-intensive and delay-sensitive tasks to the edge servers. In this paper, we concentrate on finding proper data routing paths for each offloaded task and each process result, with the objective of maximizing the network throughput by serving as many tasks as possible while meeting the MEC network resource constraints and the user demand constraints within a finite time horizon of equal time slots. It is challenging because of the unspecific data volume of an offloaded task and handovers of task offloading procedures that may frequently occur. Thus, we first investigate the impact of handovers in uplinks and downlinks. Then we propose a graph-based model to portray the resource availability in the network. Furthermore, a heuristic algorithm is devised, which finds a set of single-source-single-destination delay-constrained shortest paths in a series of auxiliary graphs. Finally, we perform an empirical evaluation of the proposed algorithm by simulations. The simulation results show that the proposed algorithm could achieve a higher network throughput than the other two benchmark heuristics. [ABSTRACT FROM AUTHOR]

Mobile edge computing (MEC) can use wireless access network (RAN) to provide the services required by user's information technology (IT) and cloud computing functions nearby, which can create a high-performance and low latency service environment. Performing task offloading and data caching at access points (APs) in a cooperative manner can reduce the heavy backhaul load and the retransmission of content downloading. However, in edge networks (ENs), how to maximize storage utilization while reducing service latency and energy consumption is still a key issue, because the heterogeneity of ENs and the uneven distribution of users make it difficult to determine which MEC server and what data should be cached. In this paper, we study a two-tier MEC system, which enables data caching and computing offloading policy to minimize the network cost at the user equipment (UE) side, while satisfying the constraints of task offloading deadline, the cache capacity at APs and the computing capability of MEC servers. The optimization problem is formulated as a mixed integer nonlinear program (MINLP) problem. In order to solve the problem, we transform it into an equivalent task offloading convex optimization problem by fixing an optimization variable. Furthermore, we solve a cache placement problem by dynamic programming (DP) algorithm. Then we propose a distributed collaborative data caching and computing offloading (CDCCO) iterative algorithm. Simulation results demonstrate that our proposed CDCCO algorithm can significantly reduce the network cost and achieve better performance than other existing schemes. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In this paper, we consider a multi-access edge computing (MEC) network with multiple servers. Due to the low latency constraints, the wireless data transmission/offloading is carried by finite blocklength codes. We characterize the reliability of the transmission phase in the finite blocklength regime and investigate the extreme event of queue length violation in the computation phase by applying extreme value theory. Under the assumption of perfect channel state information (CSI), we follow the obtained characterizations and provide an optimal framework design including server selection and time allocation aiming to minimize the overall error probability. Moreover, when only the outdated CSI is available, a deep reinforcement learning based design is proposed applying the deep deterministic policy gradient method. Via simulations, we validate the convexity proven in our analytical model and show the performance advantage of proposed analytical solution and learning-based solution comparing to the benchmark for perfect CSI and outdated CSI, respectively. [ABSTRACT FROM AUTHOR]

Provisioning reliable network services for mobile users in edge computing environments is the top priority of network service providers, as unreliable services will result in tremendous losses of revenues and customers. In this paper, we study a novel service reliability augmentation problem in a mobile edge computing (MEC) network, where mobile users request network services with service function chain (SFC) and reliability expectation requirements. To enhance the service reliability of user requests, it is a common practice to make use of redundant virtualized network function (VNF) instance placement in case the primary VNF instance fails. We aim to augment the service reliability of each admitted request to its specified reliability expectation, subject to computing capacity on each cloudlet. To this end, we first formulate a novel service reliability augmentation problem for each request with an SFC and a reliability expectation requirement, by augmenting its reliability through redundant VNF instance deployment. We then show that the problem is NP-hard, and provide an admission framework of user requests by placing primary VNF instances of network functions in the SFC to different cloudlets. We then deal with the service reliability augmentation problem of an admitted request under the assumption that all secondary VNF instances of each primary VNF instance must be placed into the cloudlets no more than $l$ l hops from the cloudlet of its primary VNF instance for a fixed $l$ l with $1\leq l \leq n-1$ 1 ≤ l ≤ n - 1 , where $n$ n is the number of cloudlets in the network, for which we formulate an integer linear program solution, and develop a randomized algorithm with a good approximation ratio and high probability, at the expense of moderate resource constraint violations. We also devise a deterministic heuristic for the problem without any resource violation. We third study the service reliability augmentation problem for a set of admitted requests by extending the proposed algorithm for the service reliability augmentation problem for a single request admission. We finally evaluate the performance of the proposed algorithms through experimental simulations. Experimental results demonstrate that the proposed algorithms are promising, and their empirical results are superior to their analytical counterparts. [ABSTRACT FROM AUTHOR]

Virtual reality (VR) and augmented reality (AR) have been proposed as revolutionary applications for the next generation, especially in education. Many VR applications have been designed to promote learning via virtual environments and 360° video. However, due to the strict requirements of end-to-end latency and network bandwidth, numerous VR applications using 360° video streaming may not achieve a high-quality experience. To address this issue, we propose relying on tile-based 360° video streaming and the caching capacity in Mobile Edge Computing (MEC) to predict the field of view (FoV) in the head-mounted device, then deliver the required tiles. Prefetching tiles in MEC can save the bandwidth of the backend link and support multiple users. Smart caching decisions may reduce the memory at the edge and compensate for the FoV prediction error. For instance, caching whole tiles at each small cell has a higher storage cost compared to caching one small cell that covers multiple users. In this paper, we define a tile selection, caching, and FoV coverage model as the Tile Selection and Caching Problem and propose a heuristic algorithm to solve it. Using a dataset of real users' head movements, we compare our algorithm to the Least Recently Used (LRU) and Least Frequently Used (LFU) caching policies. The results show that our proposed approach improves FoV coverage by 30% and reduces caching costs by 25% compared to LFU and LRU. [ABSTRACT FROM AUTHOR]

To support the explosive growth of Information and Communications Technology (ICT), Mobile Edge Computing (MEC) provides users with low latency and high bandwidth service by offloading computational tasks to the network's edge. However, resource-constrained mobile devices still suffer from a capacity mismatch when faced with latency-sensitive and compute-intensive emerging applications. To address the difficulty of running computationally intensive applications on resource-constrained clients, a model of the computation offloading problem in a network consisting of multiple mobile users and edge cloud servers is studied in this paper. Then a user benefit function EoU (Experience of Users) is proposed jointly considering energy consumption and time delay. The EoU maximization problem is decomposed into two steps, i.e., resource allocation and offloading decision. The offloading decision is usually given by heuristic algorithms which are often faced with the challenge of slow convergence and poor stability. Thus, a combined offloading algorithm, i.e., a Gini coefficient-based adaptive genetic algorithm (GCAGA), is proposed to alleviate the dilemma. The proposed algorithm optimizes the offloading decision by maximizing EoU and accelerates the convergence with the Gini coefficient. The simulation compares the proposed algorithm with the genetic algorithm (GA) and adaptive genetic algorithm (AGA). Experiment results show that the Gini coefficient and the adaptive heuristic operators can accelerate the convergence speed, and the proposed algorithm performs better in terms of convergence while obtaining higher EoU. The simulation code of the proposed algorithm is available: https://github.com/Grox888/Mobile_Edge_ Computing/tree/GCAGA. [ABSTRACT FROM AUTHOR]

Brain visual dynamics encode rich functional and biological patterns of the neural system, and if decoded, are of great promise for many applications such as intention understanding, cognitive load quantization and neural disorder measurement. We here focus on the understanding of the brain visual dynamics for the Amyotrophic lateral sclerosis (ALS) population, and propose a novel system that allows these so- called ‘lock-in’ patients to ‘speak’ with their brain visual movements. More specifically, we propose an intelligent system to decode the eye bio-potential signal, Electrooculogram (EOG), thereby understanding the patients’ intention. We first propose to leverage a deep learning framework for automatic feature learning and classification of the brain visual dynamics, aiming to translate the EOG to meaningful words. We afterwards design and develop an edge computing platform on the smart phone, which can execute the deep learning algorithm, visualize the brain visual dynamics, and demonstrate the edge inference results, all in real-time. Evaluated on 4,500 trials of brain visual movements performed by multiple users, our novel system has demonstrated a high eye-word recognition rate up to 90.47%. The system is demonstrated to be intelligent, effective and convenient for decoding brain visual dynamics for ALS patients. This research thus is expected to greatly advance the decoding and understanding of brain visual dynamics, by leveraging machine learning and edge computing innovations. [ABSTRACT FROM AUTHOR]

Many applications in Multi-access Edge Computing (MEC) consist of interdependent tasks where the output of some tasks is the input of others. Most of the existing research on computational offloading does not consider the dependency of the task and uses convex relaxation or heuristic algorithms to solve the offloading problem, which lacks adaptability and is not suitable for computational offloading in the dynamic environment of fast fading channels. Therefore, in this paper, the optimization problem is modeled as a Markov Decision Process (MDP) in multi-user and multi-server MEC environments, and the dependent tasks are represented by Directed Acyclic Graph (DAG). Combined with the Soft Actor–Critic (SAC) algorithm in Deep Reinforcement Learning (DRL) theory, an intelligent task offloading scheme is proposed. Under the condition of resource constraint, each task can be offloaded to the corresponding MEC server through centralized control, which greatly reduces the service delay and terminal energy consumption. The experimental results show that the algorithm converges quickly and stably, and its optimization effect is better than existing methods, which verifies the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

In multi-access edge computing systems with energy harvesting (MEC-EH), the mobile devices are empowered with unstable energy harvested from renewable energy sources. To prolong the life of mobile devices, as many computation-intensive tasks as possible should be offloaded to the MEC server. However, when the system states of mobile device and MEC server are unstable, e.g. poor communication channel conditions, a great number of tasks will be executed locally, leading to a long execution time. Even worse, some tasks may be dropped due to low energy levels. To address this problem, in this paper, we propose a multi-relay assisted computation offloading framework for MEC-EH systems. In this framework, a computation task can be executed by offloading to the MEC server with the help of multiple relay nodes, such as the neighboring nodes. We introduce execution cost as a performance metric to incorporate both the task execution time and task failure. We then develop a low-complexity online algorithm, namely MRACO algorithm, to minimize the average execution cost. MRACO algorithm can select the optimal execution strategy for each task from (1) executing the task locally, (2) offloading it to the MEC server directly, (3) offloading it to the MEC server with the help of the most suitable neighboring nodes, and (4) simply dropping it. Moreover, we also develop an algorithm for selecting the suitable neighboring devices to act as relays and determining the optimal task splitting ratio between them. Finally, performance evaluation shows that the proposed MRACO algorithm greatly outperforms the benchmarks in terms of both average execution time and task drop rate. [ABSTRACT FROM AUTHOR]

Vehicular edge computing (VEC) is a promising paradigm based on the Internet of vehicles to provide computing resources for end users and relieve heavy traffic burden for cellular networks. In this paper, we consider a VEC network with dynamic topologies, unstable connections and unpredictable movements. Vehicles inside can offload computation tasks to available neighboring VEC clusters formed by onboard resources, with the purpose of both minimizing system energy consumption and satisfying task latency constraints. For online task scheduling, existing researches either design heuristic algorithms or leverage machine learning, e.g., deep reinforcement learning (DRL). However, these algorithms are not efficient enough because of their low searching efficiency and slow convergence speeds for large-scale networks. Instead, we propose an imitation learning enabled online task scheduling algorithm with near-optimal performance from the initial stage. Specially, an expert can obtain the optimal scheduling policy by solving the formulated optimization problem with a few samples offline. For online learning, we train agent policies by following the expert’s demonstration with an acceptable performance gap in theory. Performance results show that our solution has a significant advantage with more than 50 percent improvement compared with the benchmark. [ABSTRACT FROM AUTHOR]

Network slicing is a promising approach for enabling low latency computation offloading in edge computing systems. In this paper, we consider an edge computing system under network slicing in which the wireless devices generate latency sensitive computational tasks. We address the problem of joint dynamic assignment of computational tasks to slices, management of radio resources across slices and management of radio and computing resources within slices. We formulate the Joint Slice Selection and Edge Resource Management (JSS-ERM) problem as a mixed-integer problem with the objective to minimize the completion time of computational tasks. We show that the JSS-ERM problem is NP-hard and develop an approximation algorithm with bounded approximation ratio based on a game theoretic treatment of the problem. We use extensive simulations to provide insight into the performance of the proposed solution from the perspective of the whole system and from the perspective of individual slices. Our results show that the proposed slicing policy can achieve significant gains compared to the equal slicing policy, and that the computational complexity of the proposed task placement algorithm is approximately linear in the number of devices. [ABSTRACT FROM AUTHOR]

In this paper, in order to reduce the energy consumption and time of data transmission, the non-orthogonal multiple access (NOMA) and mobile edge caching technologies are jointly considered in mobile edge computing (MEC) networks. As for the cache-assisted vehicular NOMA–MEC networks, a problem of minimizing the energy consumed by vehicles (mobile devices, MDs) is formulated under time and resource constraints, which jointly optimize the computing resource allocation, subchannel selection, device association, offloading and caching decisions. To solve the formulated problem, we develop an effective joint computation offloading and task-caching algorithm based on the twin-delayed deep deterministic policy gradient (TD3) algorithm. Such a TD3-based offloading (TD3O) algorithm includes a designed action transformation (AT) algorithm used for transforming continuous action space into a discrete one. In addition, to solve the formulated problem in a non-iterative manner, an effective heuristic algorithm (HA) is also designed. As for the designed algorithms, we provide some detailed analyses of computation complexity and convergence, and give some meaningful insights through simulation. Simulation results show that the TD3O algorithm could achieve lower local energy consumption than several benchmark algorithms, and HA could achieve lower consumption than the completely offloading algorithm and local execution algorithm. [ABSTRACT FROM AUTHOR]

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Wireless powered mobile edge computing (WP-MEC) has been widely studied as a promising technology to liberate wireless terminals from the computation-intensive and energy-consuming tasks. This article considers a WP-MEC system consisting of multiple base stations (BSs) and mobile devices (MDs), where the MDs offload tasks to the BSs for computational resources and the BSs charge the MDs using wireless power transfer (WPT). In practice, each BS and MD are equipped with a task buffer with limited size and a battery with limited capacity. First, we develop a time slotted WP-MEC system with task and energy queuing dynamics to study long-term system performance under time-varying fading channels and stochastic task and energy arrivals. Second, we propose a dynamic throughput maximum (DTM) algorithm based on perturbed Lyapunov optimization to maximize the system throughput under task and energy queue stability constraints, by optimizing the allocation of communication, computation, and energy resources. For the DTM algorithm, we characterize a throughput-backlog trade-off of [ $\mathcal {O}(1/V)$ O (1 / V) , $\mathcal {O}(V)$ O (V) ] to indicate that the system throughput goes up as the queue backlog increases, where $V$ V is a control parameter between the system throughput and the queue backlog. However, we find that, as $V$ V goes large, the system throughput can be pushed arbitrarily close to the optimum at the cost of linearly increasing queue backlog (i.e., $\mathcal {O}(V)$ O (V) ). To reduce the cost, we further develop an improved dynamic throughput maximum (IDTM) algorithm, and verify that the IDTM algorithm can achieve a trade-off of [ $\mathcal {O}(1/V)$ O (1 / V) , $\mathcal {O}((\log (V))^2)$ O ((log (V)) 2) ] between the system throughput and the queue backlog. The simulation results demonstrate that IDTM retains close system throughput to DTM with only $\mathcal {O}((\log (V))^2)$ O ((log (V)) 2) queue backlog. [ABSTRACT FROM AUTHOR]

In this paper, we study the joint radio and computational resource allocation in the ultra-dense mobile-edge computing networks. In which, the scenario which including both computation offloading and communication service is discussed. That is, some mobile users ask for computation offloading, while the others ask for communication with the minimum communication rate requirements. We formulate the problem as a joint channel assignment, power control and computational resource allocation to minimize the offloading cost of computing offloading, with the precondition that the transmission rate of communication nodes are satisfied. Since the formulated problem is a mixed-integer nonlinear programming (MINLP), which is NP-hard. By leveraging the particular mathematical structure of the problem, i.e., the computational resource allocation variable is independent with other variables in the objective function and constraints, and then the original problem is decomposed into a computational resource allocation subproblem and a joint channel assignment and power allocation subproblem. Since the former is a convex programming, the KKT (Karush–Kuhn–Tucker) conditions can be used to find the closed optimal solution. For the latter, which is still NP-hard, is further decomposed into two subproblems, i.e., the power allocation and the channel assignment, to optimize alternatively. Finally, two heuristic algorithms are proposed, i.e., the Co-channel Equal Power allocation algorithm (CEP) and the Enhanced CEP (ECEP) algorithm to obtain the suboptimal solutions. Numerical results are presented at last to verify the performance of the proposed algorithms. [ABSTRACT FROM AUTHOR]

With the rapid development of smart mobile devices, mobile applications are becoming more and more popular. Since mobile devices usually have constrained computing capacity, computation offloading to mobile edge computing (MEC) to achieve a lower latency is a promising paradigm. In this paper, we focus on the optimal offloading problem for streaming applications in MEC. We present solutions to find offloading policies of streaming applications to achieve an optimal latency. Streaming applications are modeled with synchronous data flow graphs. Two architecture assumptions are considered — with sufficient processors on both the local device and the MEC server, and with a limited number of processors on both sides. The problem is generally NP-complete. We present an exact algorithm and a heuristic algorithm for the former architecture assumption and a heuristic method for the latter. We carry out our experiments on a practical application and thousands of synthetic graphs to comprehensively evaluate our methods. The experimental results show that our methods are effective and computationally efficient. [ABSTRACT FROM AUTHOR]

In mobile edge computing systems, an edge node may have a high load when a large number of mobile devices offload their tasks to it. Those offloaded tasks may experience large processing delay or even be dropped when their deadlines expire. Due to the uncertain load dynamics at the edge nodes, it is challenging for each device to determine its offloading decision (i.e., whether to offload or not, and which edge node it should offload its task to) in a decentralized manner. In this work, we consider non-divisible and delay-sensitive tasks as well as edge load dynamics, and formulate a task offloading problem to minimize the expected long-term cost. We propose a model-free deep reinforcement learning-based distributed algorithm, where each device can determine its offloading decision without knowing the task models and offloading decision of other devices. To improve the estimation of the long-term cost in the algorithm, we incorporate the long short-term memory (LSTM), dueling deep Q-network (DQN), and double-DQN techniques. Simulation results show that our proposed algorithm can better exploit the processing capacities of the edge nodes and significantly reduce the ratio of dropped tasks and average delay when compared with several existing algorithms. [ABSTRACT FROM AUTHOR]

With the rapid development of mobile Internet technologies and various new service services such as virtual reality (VR) and augmented reality (AR), users' demand for network quality of service (QoS) is getting higher and higher. To solve the problems of high load and low latency in-network services, this paper proposes a data caching strategy based on a multi-access mobile edge computing environment. Based on the MEC collaborative caching framework, an SDN controller is introduced into the MEC collaborative caching framework, a joint cache optimization mechanism based on data caching and computational migration is constructed, and the user-perceived time-lengthening problem in the data caching strategy is solved by a joint optimization algorithm based on an improved heuristic genetic algorithm and simulated annealing. Meanwhile, this paper proposes a multi-base station collaboration-based service optimization strategy to solve the problem of collaboration of computation and storage resources due to multiple mobile terminals and multiple smart base stations. For the problem that the application service demand in MEC server changes due to time, space, requests and other privacy, an application service optimization algorithm based on the Markov chain of service popularity is constructed, and a deep deterministic strategy (DDP) based on deep reinforcement learning is also used to minimize the average delay of computation tasks in the cluster while ensuring the energy consumption of MEC server, which improves the accuracy of application service cache updates in the system as well as reducing the complexity of service updates. The experimental results show that the proposed data caching algorithm weighs the cache space of user devices, the average transfer latency of acquiring data resources is effectively reduced, and the proposed service optimization algorithm can improve the quality of user experience. [ABSTRACT FROM AUTHOR]

Mobile image recognition services, which provide people with image recognition services through the cameras of mobile devices, are revolutionizing our lives. However, most existing cloud/edge-based approaches suffer from two major limitations, (i) Low recognition accuracy and high network bandwidth pressure, and (ii) Not easy to extract features based on currently available resources of mobile devices. In this paper, we propose a resource-aware feature extraction framework for mobile image recognition services. The proposed framework consists of discriminative feature extraction (DFE) and NestDFE algorithms. The DFE algorithm can generate an extractor ${{\mathbf E}}$ E to extract discriminative features from the image data set on the edge server and images on mobile devices. Thus, the proposed framework can achieve higher recognition accuracy and require mobile devices to upload less feature data to the edge server. The NestDFE algorithm generates a single multi-capacity extractor that acts as a series of sub-extractors and enables mobile devices to dynamically select sub-extractors. Experimental results show that the proposed framework improves recognition accuracy by about 23 percent and reduces network traffic by about 76 percent compared with existing approaches. [ABSTRACT FROM AUTHOR]

The fast popularization of the Internet of Things (IoT) has caused the data scale to increase geometrically. The data of IoT devices is processed on the cloud, but the way of processing data in the cloud center gradually causes problems, such as communication delay, latency, and privacy leakage. Edge computing sinks some cloud center services to the edge of the device so that data processing is completed in the terminal network, thereby realizing rapid data processing. At the same time, since long-distance communication is avoided, user data is processed locally, so that user privacy data can be safely protected. A genetic algorithm is a type of heuristic algorithm that is based on the genetic development of organisms in nature and has a high global optimization capability. The basic aim and objective of this paper is to study the existing edge computing framework along with computing offloading technology. The genetic algorithm is investigated using multiedge computing-oriented collaborative computing offloading, which is helpful to the IoT's growth as well as the generation and the use of data. The use of a genetic algorithm based on a color graph for load balancing on several edge servers is also investigated. In terms of the study's performance evaluation, it is obvious that our proposed approach produces superior results than previous studies. [ABSTRACT FROM AUTHOR]

With the rapid advancement of intelligent transportation systems (ITS) and vehicular communications, vehicular edge computing (VEC) is emerging as a promising technology to support low-latency ITS applications and services. In this paper, we consider the computation offloading problem from mobile vehicles/users in a heterogeneous VEC scenario, and focus on the network- and base station selection problems, where different networks have different traffic loads. In a fast-varying vehicular environment, computation offloading experience of users is strongly affected by the latency due to the congestion at the edge computing servers co-located with the base stations. However, as a result of the non-stationary property of such an environment and also information shortage, predicting this congestion is an involved task. To address this challenge, we propose an on-line learning algorithm and an off-policy learning algorithm based on multi-armed bandit theory. To dynamically select the least congested network in a piece-wise stationary environment, these algorithms predict the latency that the offloaded tasks experience using the offloading history. In addition, to minimize the task loss due to the mobility of the vehicles, we develop a method for base station selection. Moreover, we propose a relaying mechanism for the selected network, which operates based on the sojourn time of the vehicles. Through intensive numerical analysis, we demonstrate that the proposed learning-based solutions adapt to the traffic changes of the network by selecting the least congested network, thereby reducing the latency of offloaded tasks. Moreover, we demonstrate that the proposed joint base station selection and the relaying mechanism minimize the task loss in a vehicular environment. [ABSTRACT FROM AUTHOR]

The application of network slicing to mobile edge computing (MEC) systems has aroused great interests from both academia and industry. However, the optimization of network slicing and MEC in most existing research works only focuses on resource slicing, energy scheduling, and power allocation from the perspective of mobile devices, without considering the operator's revenue. In this paper, we propose a novel framework for network slicing in MEC systems, including slice request admission and a revenue model, to investigate the operator's revenue escalation problem while considering traffic variations. The revenue model is mainly composed of the longer-term revenue and short-term revenue. Particularly, we jointly optimize slice request admission in the long-term and resource allocation in the short-term to maximize the operator's average revenue. By employing the Lyapunov optimization technique, we develop an algorithm without requiring any prior-knowledge of traffic distributions, referred to as the DNSRA, to solve the problem. To reduce the computational complexity of directly solving the DNSRA, we decouple the optimization variables for efficient algorithm design. By this, the strategies for user association and CPU-cycle frequency are obtained in closed forms, respectively. Power allocation and subcarriers assignment are obtained by employing the successive convex approximation approach. Meanwhile, we develop a heuristic algorithm to obtain the dynamic slice request admission decision. Simulation results show that the proposed DNSRA can strike a flexible balance between the average revenue and the average delay, and can significantly increase the operator's revenue against existing schemes. [ABSTRACT FROM AUTHOR]

Mobile Edge Computing (MEC) is a promising network architecture that pushes network control and mobile computing to the network edge. Recent studies propose to deploy MEC applications in the Network Function Virtualization (NFV) environment. The mobile network service in NFV is deployed as a Service Function Chaining (SFC). In the dynamic and resource-limited mobile network, SFC placement aiming at optimizing resource utilization is a challenging problem. In this article, we solve the SFC placement problem in the MEC-NFV environment. We formulate the SFC placement problem as a weighted graph matching problem, including two sub-problems: a graph matching problem and an SFC mapping problem. To efficiently solve the graph matching problem, we propose a Linear Programming–(LP) based approach to calculate the similarity between VNFs and physical nodes. Based on the similarity, we design a Hungarian-based algorithm to solve the SFC mapping problem. Evaluation results show that our proposed LP-based solutions outperform the heuristic algorithms in terms of execution time and resource utilization. [ABSTRACT FROM AUTHOR]

With the development of cloud technology, hierarchical and distributed cross-cloud architecture is gradually replacing traditional centralized architecture, for example, used in edge (or fog) computing. Due to the fluctuation of resource requirements, if a node does not have sufficient resources to process requests, the same or higher-level nodes can share their resources by offloading or redirecting requests to themselves, at the possible cost of reduced service quality. However, it is difficult to effectively optimize the sharing effect based on mean requirements. We formulate the multilevel problem with horizontal and vertical resource sharing using stochastic models, identify the optimal structures with embedded subproblems, and obtain the approximation solution in an efficient dynamic programming manner. In the problem setting with a wide range of different parameters, the proposed algorithm can outperform existing mean and heuristic algorithms in all scenarios to improve the total satisfied requirements by up to 26%, and can be hundreds of times faster than these heuristic algorithms. • The demand fluctuation in edge clouds is a new challenge for resource scheduling. • We formulate the general problem in hierarchical and distributed edge clouds. • The problem has non-linear object with non-linear constraints. • We identify the nested subproblems with their optimal condition. • The algorithm outperforms the existing mean and heuristic based algorithms. [ABSTRACT FROM AUTHOR]

Edge Computing is a novel paradigm that extends Cloud Computing by moving the computation closer to the end users and/or data sources. When considering Edge Computing, it is possible to design a three-tier architecture (comprising tiers for the cloud devices, edge devices, and end devices) which is useful to meet emerging IoT applications that demand low latency, geo-localization, and energy efficiency. Like the Cloud, the Edge Computing paradigm relies on virtualization. An Edge Computing virtualization model provides a set of virtual nodes (VNs) built on top of the physical devices that make up the three-tier architecture. It also provides the processes of provisioning and allocating VNs to IoT applications at the edge of the network. Performing these processes efficiently and cost-effectively raises a resource management challenge. Applying the traditional cloud virtualization models (typically centralized) to virtualize the edge tier, are unsuitable to handle emerging IoT application scenarios due to the specific features of the edge nodes, such as geographical distribution, heterogeneity and, resource constraints. Therefore, we propose a novel distributed and lightweight virtualization model targeting the edge tier, encompassing the specific requirements of IoT applications. We designed heuristic algorithms along with a P2P collaboration process to operate in our virtualization model. The algorithms perform (i) a distributed resource management process, and (ii) data sharing among neighboring VNs. The distributed resource management process provides each edge node with decision-making capability, engaging neighboring edge nodes to allocate or provision on-demand VNs. Thus, the distributed resource management improves system performance, serving more requests and handling edge node geographical distribution. Meanwhile, data sharing reduces the data transmissions between end devices and edge nodes, saving energy and reducing data traffic for IoT-edge infrastructures. [ABSTRACT FROM AUTHOR]

Mobile edge computing (MEC) is envisioned as a prospective technology that supports latency-critical and computation-intensive applications by using storage and computation resources in network edges. The advantages of this technology are trapped in limited edge cloud resources, and one of the prime challenges is how to allocate available edge cloud resources to satisfy user requests. However, previous works usually optimize service (data&code) placement and request routing simultaneously within the same timescale, ignoring the fact that frequent service replacement will incur expensive operating expenses. In this paper, we jointly optimize service placement and request routing in the MEC network for data analysis applications, under the constraints of computation and storage resource. In particular, the Cloud Radio Access Network (C-RAN) architecture is applied to pool available resources and realize load balancing among edge clouds. In addition, we adopt a two timescale framework to reduce high operating expenses caused by frequent cross-cloud service replication and replica deletion. Then, we develop a greedy-based approximation algorithm for service placement subproblem and a linear programming (LP) relaxation-based heuristic algorithm for request routing subproblem, respectively. Finally, the numerical results demonstrate that our proposed solution reaches 90% of the optimal performance in services homogeneous case and 76% in services heterogeneous case. [ABSTRACT FROM AUTHOR]

In this paper, we address the problem of overall delay minimization in a cellular multi-access edge computing (MEC) network, where servers with limited computing and storage resources are co-located with a base station (BS) to execute the computation tasks offloaded by the users. We formulate this problem as a distributed mirror prox (DMP) optimization at individual users and the MEC servers’ local controller (LC) over a long-time horizon and develop an online algorithm to ensure queue stability of the overall network in the long run. Both, the cost and the constraint functions are time-varying with unknown statistics. We evaluate the performance of the proposed algorithm using two performance metrics: the dynamic regret to assess the closeness of the achievable cost against the dynamic optimal value; and the aggregate violation to measure the asymptotic satisfaction of the constraints. Given the optimum hindsight variation is sub-linear, we prove that both of the dynamic regret and the aggregate violation are sub-linear in the long run. The simulation results confirm the superiority of the proposed DMP algorithm over the stochastic dual gradient in terms of delay minimization, dynamic regret, aggregate violation and energy efficiency in battery-powered user devices. [ABSTRACT FROM AUTHOR]

Edge computing is becoming pervasive in our daily lives with emerging smart devices and the development of communication technology. Smart devices with various resources make data transactions prevalent over edge environments. To ensure such transactions are unmodifiable and undeniable, blockchain technology is introduced into edge environments. In this paper, we propose a hybrid blockchain system to enhance the security for transactions and determine the incentive for miners in edge computing environments. We propose a Proof of Work (PoW) and Proof of Stake (PoS) hybrid consensus blockchain system utilizing the heterogeneity of devices to adapt to the characteristic of edge environments. We raise the incentive assignment problem for a fair incentive to PoW miners. We formulate the problem and propose an iterative and another heuristic algorithm to determine the incentive that the miner will receive for a new block. We further prove that the iterative algorithm can obtain global optimal results. Simulation and experiment results show that our proposed algorithm can give a reasonable incentive to miners under different system parameters in edge blockchain systems. [ABSTRACT FROM AUTHOR]

The vehicular edge computing system integrates the computing resources of vehicles, and provides computing services for other vehicles and pedestrians with task offloading. However, the vehicular task offloading environment is dynamic and uncertain, with fast varying network topologies, wireless channel states, and computing workloads. These uncertainties bring extra challenges to task offloading. In this paper, we consider the task offloading among vehicles, and propose a solution that enables vehicles to learn the offloading delay performance of their neighboring vehicles while offloading computation tasks. We design an adaptive learning based task offloading (ALTO) algorithm based on the multi-armed bandit theory, in order to minimize the average offloading delay. ALTO works in a distributed manner without requiring frequent state exchange, and is augmented with input-awareness and occurrence-awareness to adapt to the dynamic environment. The proposed algorithm is proved to have a sublinear learning regret. Extensive simulations are carried out under both synthetic scenario and realistic highway scenario, and results illustrate that the proposed algorithm achieves low delay performance, and decreases the average delay up to $30\%$ compared with the existing upper confidence bound based learning algorithm. [ABSTRACT FROM AUTHOR]

In order to help user terminal devices (UTDs) efficiently handle computation-intensive and time-delay sensitive computing task, multi-access edge computing (MEC) has been proposed. However, due to the differences among the performance of UTDs, and the resource limitation of MEC servers, the joint optimization between the offloading decisions of UTDs and the allocation of resources in network is still a focus of the research. This paper studies the joint computation offloading and resource allocation strategy in multi-user and multi-server scenarios. Firstly, we formulate the joint optimization problem of computation offloading and resource allocation as a mixed integer nonlinear programming (MINP) problem to minimize the energy consumption of UTDs, by constraining the offloading decision, channel selection, power allocation and resource allocation. Secondly, we propose a two-stage heuristic optimization algorithm based on genetic algorithms, which divides the joint optimization problem of computation offloading and resource allocation in two stages. Based on the coupling relationship between the offloading decision and the resource allocation scheme, we iteratively update the solution of the problem, and finally obtain the stable convergence solution of the optimization problem. Finally, the proposed algorithm is compared with other classical methods to prove the effectiveness. [ABSTRACT FROM AUTHOR]

In the multi-access edge computing environment, app vendors deploy their services and applications at the network edges, and edge users offload their computation tasks to edge servers. We study the user-perceived delay-aware service placement and user-allocation problem in edge environment. We model the MEC-enabled network, where the user-perceived delay consists of computing delay and transmission delay. The total cost in the offloading system is defined as the sum of service placement, edge server usage and energy consumption cost, and we need to minimize the total cost by determining the overall service-placing decision and user-allocation decision, while guaranteeing that the user-perceived delay requirement of each user is fulfilled. Our considered problem is formulated as a Mixed Integer Linear Programming problem, and we prove its NP-hardness. Due to the intractability of the considered problem, we propose a LOCal-search based algorithm for USer-perceived delay-aware service placement and user-allocation in edge environment, named LOCUS, which starts with a feasible solution and then repeatedly reduces the total cost by performing local-search steps. After that, we analyze the time complexity of LOCUS and prove that it achieves provable guaranteed performance. Finally, we compare LOCUS with other existing methods and show its good performance through experiments. [ABSTRACT FROM AUTHOR]

Abstract The effective scheduling of multiple agile earth observation satellites plays a key role in improving the efficiency of the agile satellite observation system. Due to the complex constraints and large solution space (increases exponentially with the problem size), it has been a big challenge to effectively solve the multiple agile satellites scheduling problem. In this study, an edge computing framework is proposed in order to solve this problem in a flexible manner. In this framework, a central node and several edge nodes are included. The central node corresponds to the operation center of the satellite observation system while each edge node corresponds to an agile satellite. The central node filters tasks for edge nodes according to the time of scheduling period and visible time windows. Tasks in each edge node are scheduled according to the order of scheduling periods by a constructive heuristic algorithm based on the density of residual tasks (HADRT). The efficiency of this algorithm is proved. The time and space complexity are analyzed in detail. The proposed method is compared with other three advanced methods, and the experimental results show that HADRT could achieve higher task completion rate and reward rate than other algorithms with acceptable computing time. [ABSTRACT FROM AUTHOR]

Mobile Edge Computing is a promising paradigm that can provide cloud computing capabilities at the edge of the network to support low latency mobile services. The fundamental concept relies on bringing cloud computation closer to users by deploying cloudlets or edge servers, which are small clusters of servers that are mainly located on existing wireless Access Points (APs), set-top boxes, or Base Stations (BSs). In this article, we focus on computation offloading over a heterogeneous cloudlet environment. We consider several users with different energy--and latency-constrained tasks that can be offloaded over cloudlets with differentiated system and network resources capacities. We investigate offloading policies that decide which tasks should be offloaded and select the assigned cloudlet, accordingly with network and system resources. The objective is to minimize an offloading cost function, which we defined as a combination of tasks' execution time and mobiles' energy consumption. We formulate this problem as a Mixed-Binary Programming. Since the centralized optimal solution is NP-hard, we propose a distributed linear relaxation-based heuristic approach that relies on the Lagrangian decomposition method. To solve the subproblems, we also propose a greedy heuristic algorithm that computes the best cloudlet selection and bandwidth allocation following tasks' offloading costs. Numerical results show that our offloading policy achieves a good solution quickly. We also discuss the performances of our approach for large-scale scenarios and compare it to state-of-the-art approaches from the literature. [ABSTRACT FROM AUTHOR]

Mobile edge computing (MEC) deploys computing and storage resources close to mobile devices, enabling resource demanding applications to run on mobile devices with short network latency. In the past few years, large numbers of research works focused on the research hotspots in MEC, such as computation offloading and energy efficiency. However, few researchers have investigated the deployment of edge servers. On the one hand, blindly deploying numerous edge servers will result in a large amount of capital expenditure. On the other hand, the deployment of edge servers is a multimodal problem that should provide decision makers with multiple deployment options to deal with the impact of unmeasured real-world factors. Considering these factors, we study the multimodal optimization problem of edge server placement (MESP) with the goal of minimizing the average system response time in this work. Regarding the difficulty of the MESP problem,we propose a heuristic algorithm that combines particle swarm optimization and niching technology to obtain a set of competitive placement solutions. Extensive experiments over a real-world dataset show that the proposed algorithm can significantly reduce the system response time. [ABSTRACT FROM AUTHOR]