关键词： Mobile edge computing   Nash equilibrium   non-cooperative game theory   task offloading   power controlling  

摘要： Mobile edge computing (MEC) is becoming a promising paradigm of providing computing servers, like cloud computing, to Edge node. Compared to cloud servers, MECs are deployed closer to mobile devices (MDs) and can provide high quality-of-service (QoS;including high bandwidth, low latency, etc) for MDs with computation-intensive and delay-sensitive tasks. Faced with many MDs with high QoS requirements, MEC with limited computation capacity should consider how to allocate the computing resources to MDs to maximize the number of served MDs. Besides, for each MD, he/she wants to minimize the energy consumption within an acceptance delay range. To solve these issues, we propose a Game-based Computation Offloading (GCO) algorithm including a task offloading profile of MEC and the transmission power controlling of each MD. Specifically, we propose a Greedy-Pruning algorithm to determine the MDs that can offload the tasks to MEC. Meanwhile, each MD competes the computing resources by using his/her transmission powercontrolling strategy. We illustrate the problem of task offloading for multi-MD as a non-cooperative game model, in which the information of each player (MDs) is incomplete for others and each player wishes to maximize his/her own benefit. We prove the existence of the Nash equilibrium solution of our proposed game model. Then, it is proved that the transmission power solution sequence obtained from GCO algorithm converges to the Nash equilibrium solution. Extensive simulated experiments are shown and the comparison experiments with the state-of-the-art and benchmark solutions validate and show the feasibility of the proposed method.

关键词： Resource management   Servers   Delays   Edge computing   Computational modeling   Computer architecture   Mobile edge computing   software-defined ultra-dense networking   edge resource sharing   design structure matrix  

摘要： The emerging mobile edge computing (MEC) evolutionarily extends the cloud services to the network edge. In order to efficiently coordinate distributed edge resources, software defined networking (SDN) at the network edge has been explored to realize the integrated management of communication, computation, and cache (3C) resources. However, many research efforts, in software-defined edge networks, are mainly devoted to 1C or 2C resource sharing. Motivated by high service performance and user demands, we propose a user-centric edge resource sharing model for software-defined ultra-dense network (SD-UDN) where multiple MEC servers around small base stations (SBSs) can share their 3C resources through OpenFlow-enabled switches. In particular, the service models of MEC servers and users are formulated to optimize the service process by minimizing the service delay, which is NP-hard. To address this NP-hard issue, a service association model is constructed based on design structure matrix (DSM), and a simulated annealing algorithm is employed to further optimize the service association model for reducing time complexity and offering a nearoptimal solution. Compared with traditional 1C or 2C resource sharing, the proposed edge resource sharing model can guarantee lower service delay for users.

关键词： Task analysis   Scheduling   Virtual machining   Cloud computing   Scheduling algorithms   Lattices   Mobile edge computing   virtual machines   formal concept analysis   infusion   matching  

摘要： Mobile Edge Computing (MEC) is providing cloud computing capabilities within the radio access networks and offering a new paradigm to liberate the mobile devices from heavy computational workloads. Importantly, MEC can effectively reduce latency, avoid congestion, and prolong the battery lifetime of mobile devices by offloading the computation tasks from the mobile devices to a physically proximal MEC servers. Particularly, Virtual Machines (VMs) scheduling is a critical issue for tasks offloading and computation in MEC. Regarding to the VMs scheduling problem in MEC environmnet, this paper pioneers the use of Formal Concept Analysis (FCA) methodology for identifying the mapping from tasks to VMs. Specifically, the VMs profile and tasks descriptions are initially characterized as the formal contexts, respectively. With the constructed formal contexts, the corresponding formal concepts which refer to the rules set, are then generated. To better infuse the rules set of VMs and tasks, this paper defines a similarity measurement between formal concepts of VMs and tasks. Consequently, the matching problem from a given task to a virtual machine is to return the expected virtual machine according to the principle of maximum similarity degree between formal concepts of virtual machine and task. Extensive simulations are conducted with a real dataset for the validation of feasibility and effectiveness of the proposed approach. Specifically, the proposed approach can significantly reduce the energy consumption around 28 percent comparing to the approach without consideration of energy consumption. Overall, It is demonstrated that FCA-based VMs scheduling is a novel solution for a sustainable VMs scheduling in MEC environment.

关键词： multi-access edge computing   personal multi-access edge computing   multi-access edge computing mobility management   mobile edge computing   mobile edge computing mobility management  

摘要： The Multi-Access Edge Computing (MEC) paradigm provides a promising solution to solve the resource-insufficiency problem in user mobile devices by offloading computation-intensive and delay-sensitive computing services to nearby edge nodes. However, there is a lack of research on the efficient task offloading and mobility support when mobile users frequently move in the MEC environment. In this paper, we propose the mobile personal MEC architecture that utilizes a user's mobile device as an MEC server (MECS) so that mobile users can receive fast response and continuous service delivery. The results show that the proposed scheme reduces the average service delay and provides efficient task offloading compared to the existing MEC scheme. In addition, the proposed scheme outperforms the existing MEC scheme because the existing mobile user devices are used as MECS, enabling low-latency service and continuous service delivery, even as the mobile user requests and task sizes increase.

关键词： Servers   Task analysis   Cloud computing   Computer architecture   Real-time systems   Processor scheduling   Edge computing   Computation-intensive services   distributed scheduling   multi-armed bandit learning learning   mobile edge computing   vehicular networks  

摘要： Mobile edge computing (MEC) is an emerging paradigm to offload computations from the cloud to the MEC servers in vehicular networks, aiming at better supporting computation-intensive services with requirements of low latency and real-time processing. In this work, we investigate a new service scenario of computation offloading and workload balancing in MEC-empowered vehicular networks, where the computational resources of MEC/cloud servers are cooperatively utilized. Then, we formulate a distributed task assignment (DTA) problem by considering heterogeneous computation resources, high mobility of vehicles and uneven distribution of workloads, targeting at optimizing task assignment among MEC/cloud servers and minimizing task completion time. We prove that the DTA is NP-hard. Further, we propose a multi-armed bandit learning algorithm called Utility-table based Learning. For workload balancing among MEC servers, a utility table is established to determine the optimal solution by online learning of real-time workload distribution, which is updated based on the feedback signal of task assignment. For optimal computation offloading, a theoretical bound is derived to determine the ratio of workload assigned to the cloud. Lastly, we build the simulation model and conduct an extensive experiment, which demonstrates the superiority of the proposed algorithm.

关键词： NOMA   Task analysis   Delays   Energy consumption   Resource management   Servers   Wireless communication   Non-orthogonal multiple access   mobile edge computing   time delay   energy consumption   user grouping   resource allocation  

摘要： Non-orthogonal multiple access (NOMA) and mobile edge computing (MEC) have been recognized as promising technologies for the beyond fifth generation networks to achieve significant capacity improvement and delay reduction. In this paper, the technologies of hybrid NOMA and MEC are integrated. In the hybrid NOMA MEC system, multiple users are classified into different groups and each group is allocated a dedicated time slot. In each group, a user first offloads its task by sharing a time slot with another user, and then solely offloads during a time interval. To reduce the delay and save the energy consumption, we consider jointly optimizing the power and time allocation in each group as well as the user grouping. As the main contribution, the optimal power and time allocation is characterized in closed form. In addition, by incorporating the matching algorithm with the optimal power and time allocation, we propose a low complexity method to efficiently optimize user grouping. Simulation results demonstrate that the proposed resource allocation method in the hybrid NOMA MEC systems not only yields better performance than the conventional OMA scheme but also achieves quite close performance as global optimal solution.

关键词： Task analysis   Mobile handsets   Servers   Processor scheduling   Reinforcement learning   Uplink   Optimization   Mobile edge computing   random user arrivals   approximate MDP   reinforcement learning  

摘要： In this paper, we investigate the scheduling design of a mobile edge computing (MEC) system, where active mobile devices with computation tasks randomly appear in a cell. Every task can be computed at either the mobile device or the MEC server. We jointly optimize the task offloading decision, uplink transmission device selection and power allocation by formulating the problem as an infinite-horizon Markov decision process (MDP). Compared with most of the existing literature, this is the first attempt to address the transmission and computation optimization with random device arrivals in an infinite time horizon to our best knowledge. Due to the uncertainty in the device number and location, the conventional approximate MDP approaches addressing the curse of dimensionality cannot be applied. An alternative and suitable low-complexity solution framework is proposed in this work. We first introduce a baseline scheduling policy, whose value function can be derived analytically with the statistics of random mobile device arrivals. Then, one-step policy iteration is adopted to obtain a sub-optimal scheduling policy whose performance can be bounded analytically. The complexity of deriving the sub-optimal policy is reduced dramatically compared with conventional solutions of MDP by eliminating the complicated value iteration. To address a more general scenario where the statistics of random mobile device arrivals are unknown, a novel and efficient algorithm integrating reinforcement learning and stochastic gradient descent (SGD) is proposed to improve the system performance in an online manner. Simulation results show that the gain of the sub-optimal policy over various benchmarks is significant.

关键词： Game theory   Mobile edge computing   Multiple resources allocation   Resource pricing  

摘要： Mobile edge computing is a new paradigm that can enhance the computation capability of end devices and alleviate communication traffic loads during transmission. Mobile edge computing is highly useful for emerging resource-hungry mobile applications. However, a key challenge for mobile edge computing systems is multiple resources allocation between Mobile Edge Clouds (MECs) and End Users (EUs), especially for multiple heterogeneous MECs and EUs. To address this problem, we propose a Stackelberg game-based framework in which EUs and MECs act as followers and leaders, respectively. The proposed framework aims to compute a Stackelberg equilibrium solution in which each MEC achieves the maximum revenue while each EU obtains utility-maximized resources under budget constraints. We decompose the multiple resources allocation and pricing problem into a set of subproblems in which each subproblem only considers a single resource type. The Stackelberg game framework is constructed for each subproblem wherein each player (i.e., an EU) can selfishly maximize its utility by selecting an appropriate strategy in the strategy space. We prove the existence of the subgame Stackelberg equilibrium and develop algorithms to determine the Stackelberg equilibrium for each resource type, including an optimal demand computation algorithm, to determine the best resource demand strategy for an EU and an iterative algorithm to find an equilibrium price. The equilibrium solutions of all subgames constitute the equilibrium solution of the original problem. We also conduct simulation experiments of our game, such as numerical data for the Stackelberg equilibrium, numerical data for the convergence of the Stackelberg equilibrium, and numerical data as the system size increases. Finally, we demonstrate that an EU with idle resources can play the role of an MEC. (C) 2020 Elsevier B.V. All rights reserved.

关键词： healthcare   IoT   SMS   SDN   Twofish   WSN   mobile edge computing   blockchain   OAuth  

摘要： Technological innovations have enabled the realization of a utopian world where all objects of everyday life, as well as humans, are interconnected to form an "Internet of Things (IoT)." These connected technologies and IoT solutions have led to the emergence of smart cities where all components are converted into a connected smart ecosystem. IoT has envisioned several areas of smart cities including the modern healthcare environment like real-time monitoring, patient information management, ambient-assisted living, ambient-intelligence, anomaly detection, and accelerated sensing. IoT has also brought a breakthrough in the medical domain by integrating stake holders, medical components, and hospitals to bring about holistic healthcare management. The healthcare domain is already witnessing promising IoT-based solutions ranging from embedded mobile applications to wearable devices and implantable gadgets. However, with all these exemplary benefits, there is a need to ensure the safety and privacy of the patient's personal and medical data communicated to and from the connected devices and systems. For a smart city, it is pertinent to have an accessible, effective, and secure healthcare system for its inhabitants. This paper discusses the various elements of technology-enabled healthcare and presents a privacy-preserved and secure "Smart Medical System (SMS)" framework for the smart city ecosystem. For providing real-time analysis and responses, this paper proposes to use the concept of secured Mobile Edge Computing (MEC) for performing critical time-bound computations on the edge itself. In order to protect the medical and personal data of the patients and to make the data tamper-proof, the concept of blockchain has been used. Finally, this paper highlights the ways to capture and store the medical big data generated from IoT devices and sensors.

关键词： Task analysis   Downlink   Uplink   Interference   Fading channels   Edge computing   Servers   Mobile edge computing   computation replication   computation-communication tradeoff   transmission cooperation  

摘要： Existing works on task offloading in mobile edge computing (MEC) networks often assume a task is executed once at a single edge node (EN). Downloading the computed result from the EN back to the mobile user may suffer long delay if the downlink channel experiences strong interference or deep fading. This paper exploits the idea of computation replication in MEC networks to speed up the downloading phase. Computation replication allows each user to offload its task to multiple ENs for repetitive execution so as to create multiple copies of the computed result at different ENs which can then enable transmission cooperation and hence reduce the communication latency for result downloading. Yet, computation replication may also increase the communication latency for task uploading, despite the obvious increase in computation load. The main contribution of this work is to characterize asymptotically an order-optimal upload-download communication latency pair for a given computation load in a multi-user multi-server MEC network. Analysis shows when the computation load increases within a certain range, the downloading time decreases in an inversely proportional way if it is binary offloading or decreases linearly if it is partial offloading, both at the expense of linear increase in the uploading time.