关键词： Mobile edge computing   double-queue   load balancing   lyapunov optimization  

摘要： Mobile edge computing (MEC) is a popular service paradigm by which mobile devices can offload their latencysensitive and computation-intensive workloads to edge servers. The MEC service scheduling problem has been investigated in recent years. However, most MEC service scheduling mechanisms only consider workloads on homogeneous edge servers, causing servers' queue backlogs to be too large when innumerable user requests arrive concurrently. In this paper, we are the first to propose a double-queue workloads scheduling model innovatively, and formulate a system (including user ends and edge server ends) utility into a scheduling optimization problem. To tackle such an NP scheduling problem, we present a Lyapunov-based decomposition strategy to convert the original problem into three equivalent subproblems. By aggregating three subproblem solving strategies, we propose the Lyapunov-based online matching algorithm for edge service scheduling, named LOMES, to obtain an optimal system utility while guaranteeing the load balancing of mobile devices and heterogeneous edge servers. Simulations further validate that LOMES realizes the load balancing of two queue lengths and a [O(1/V);O(V)] tradeoff between the system's utility and workloads with a utilityworkload tradeoff parameter V.

关键词： Mobile Edge Computing   Wireless power transfer   Local computing   Offload computing   Resource allocation   Problem decomposition   Alternate optimization algorithm  

摘要： With the rapid integration of Intelligent Computing and Internet of Things technology (IoT), Iot sensor network devices are expected not only gather signal but also handle data computing. However, the intrinsic shortcomings of IoT devices on limited computing capacity and battery energy are the bottlenecks. Recently, Wireless Power Transmission (WPT) integrated with Mobile Edge Computing (MEC) provides an effective method for energy and computing resource allocation of IoT devices. However,the offloading ratio strategy and computing and energy resources allocation problems are challenging because the coupling of WPT and MEC, especially when the cost of feedback cannot be ignored. In this paper, we consider simultaneous power transfer and wireless information (SWIPT) strategy for an MEC powered IoT network, which allows the hybrid access point (HAP) to transfer energy by the feedback signal so that IoT devices can capture energy and receive feedback data simultaneously Firstly,the WP-MEC network model, local computation model and computation offloading model of the edge wireless devices (WDs) were built. Secondly, the causal relationship among computational rate ,network channel gain, energy transmission time and transmission power of HAP, CPU computing cycle frequency of WDs and offloading ratio allocation of local computing tasks were analyzed. Then,the above resource allocation problem was modeled as a non-convex problem and was decomposed into two convex sub-problems of offloading ratio strategy and computing and energy allocation. Finally, an alternative optimization methods were proposed to solve these two sub-problems in a sequential and the optimal solution was derived. The proposed algorithm and strategy were effective with the simulation results.

关键词： Collaborative computation offloading   Mobile edge computing   DQN   6G   Software-defined networking   Network congestion  

摘要： The implementation of mobile edge computing (MEC) and software-defined networking (SDN) over sixth-generation networks is a driving force in the future of cloud computing. It holds significant promise in addressing smart device (SD) resources and battery life limitations. To deal with the variety and resource constraints of SDs while making better use of network infrastructure, collaborative offloading has emerged as a viable technique for improving the ability to schedule independent tasks while alleviating the burden of restricted computation resources and network congestion. Network congestion is a common issue when a network node or link carries more data than it can manage. Existing works frequently overlook the impact of insufficient edge server capacity and network congestion. This paper primarily focuses on an SDN-powered MEC network that utilizes a full offloading policy, which completely offloads all tasks from the SD to the edge server and adopts a collaborative offloading of the MEC network when it is overloaded. The problem also takes into consideration when and to whom to offload the task. To bridge this gap, we first implement a collaborative offloading scheme among MEC servers based on the edge server's resources and neighbors' status to alleviate network congestion. It takes advantage of the computing capacities of edge servers deployed at the network's edge and the SDN controller's global view of the entire network. Then we devise a Deep Q-Network methodology to achieve near-optimal performance, and minimize the total execution time concerning deadline constraints. The experiments reveal that our proposed task scheduling of collaborative computation offloading algorithm can significantly minimize the total execution time more than the existing schemes.

关键词： Air-ground cooperation   heterogeneous   mobile edge computing   resource allocation   trajectory optimization  

摘要： The unmanned aerial vehicle (UAV) enabled mobile edge computing (MEC) is a promising paradigm for providing extensive coverage and additional computation services to the Internet of Things (IoT) devices with smart applications, which are normally computation-intensive and delay-critical, especially in some remote areas or emerging scenarios without available infrastructure. However, considering the restricted computation capabilities of UAVs, even multiple UAVs may not satisfy the quality of service (QoS) requirement of IoT users with heavy-computation applications. In this paper, a heterogenous-multiple-UAVs enabled collaborative aerial-ground MEC framework is proposed. The total computation bits maximization problem under the time-division multiple access (TDMA) scheme and partial offloading mode is formulated by jointly optimizing the user association, the CPU-cycle frequencies of users and computing UAVs, the transmit power of users and UAVs, the offloading time of users and UAVs, as well as the UAVs' trajectories. Due to the non-convexity of the original problem with the nonlinear coupling of different variables, a two-layered alternative optimization (TLAO) algorithm is proposed, where the outer layer optimizes the user association and resource allocation, while the inner layer optimizes the UAV trajectory scheduling. Simulation results demonstrate that our proposed TLAO scheme can improve the total computation bits compared with other benchmark schemes, and achieve fast convergence. Moreover, comparative results also verify that our proposed strategy is flexible to apply to homogeneous multi-UAVs and single-UAV scenarios.

关键词： Collaborative computing   computing resource allocation   Lagrangian dual   mobile edge computing   task partitioning  

摘要： With more computational intensive applications deployed involving mobile edge computing (MEC), the collaboration among mobile devices, edge and cloud servers becomes an effective mechanism to fully utilize all available distributed computing resources. However, two main challenges have yet to be addressed to enable this three-way collaboration for securing necessary computational resources and further guaranteeing the quality of service (QoS) of task handling. The first challenge is related to the partitioning of an application task into several dependent subtasks and schedule them among the collaborating device-edge-cloud (DEC). The second is focused on the allocation of necessary computing resources of device, edge and cloud servers for effective subtask handling. To this end, we study the joint task offloading and resource allocation for DEC collaboration in this paper by formulating a new optimization problem with the objective of minimizing the task handling latency. To solve this problem, we decompose the original problem into two subproblems, which include the first one of calculating the optimal task partitioning ratio by mathematical analytical method, as well as the second on using the Lagrangian dual (LD) method for obtaining the optimal task offloading and resource allocation policy. Finally, we conduct simulation experiments on a real-life dataset obtained from the central business district (CBD) of Melbourne, Australia, and the experimental results validate the efficacy of our approach in minimizing latency.

关键词： Video transcoding analysis   Edge transcoding   Mobile edge computing   Machine learning   Transcoding time prediction  

摘要： The variable network conditions of end-users demand different resolutions, formats, and bitrate versions of videos to be delivered over the network. Fetching each video from the Content Delivery Network (CDN) burdens all network layers. A promising solution is to leverage Mobile Edge Computing (MEC). This paper presents a Machine Learning based caching and transcoding model, which helps release the burden on the backhaul links of the network. The purposed scheme contains a task scheduler and time estimator. The time estimator predicts the job's transcoding time based on the Virtual Machines (VMs) load. The task scheduler maps the transcoding task to different VMs regarding the cost feasibility, Quality of Service (QoS) of the users, and the cost-to-performance ratio of VMs. For this purpose, we prepare a dataset of 500 videos and transcode each video in every lower representation using Amazon Elastic Compute Cloud (EC2). The time estimator is trained on 77% of the video dataset containing more than 80,000 transcoding time data of different videos. The simulation results show that the proposed scheme outperforms its counterparts in terms of cost, average delay perceived by the user, and backhaul burden.

关键词： Mobile edge computing   task priority   resource scheduling   deep Q-learning  

摘要： In this paper, we investigate the joint impact of task priority and mobile computing service on the mobile edge computing (MEC) networks, in which one unmanned aerial vehicle (UAV) provides mobile computing service to help compute the tasks from users in multiple hotspots where the task priority is time-varying. For such a system, we firstly measure the system performance by the computing utility of multiples users, where the effect of a wide-range task priority is incorporated. We then analyze the impact of the network wireless bandwidth and UAV computational capability on the system performance, from which we optimize the system through UAV hotspot selection and user task offloading. To solve the optimization problem, we further employ deep Q-learning algorithm to learn an effective solution by continuous interaction between the UAV agent and system environment. Simulations are finally conducted to verify the superiority of the proposed studies in this paper.

关键词： Mobile edge computing   radio access network   micro-architecture   mobile edge co-located workloads  

摘要： Nowadays, virtual Radio Access Network (vRAN) plays a vital role in today's mobile edge system for its better support for latency-sensitive applications. However, our characterization of vRAN on modern processors depicts a frustrating picture of Single-Instruction Multi-Data (SIMD) acceleration. Specifically, the existing data arrangement processes cannot efficiently utilize the ports in modern processors, which leads to high backend bound and fails to saturate the memory bandwidth between registers and the L1 cache. To tackle the issue, we thoroughly examine the state-of-the-art CPU architecture and observe the idle ports which could be utilized by the process. Motivated by this observation, we propose an "Arithmetic Ports Consciousness Mechanism" (APCM) utilizing these idle ports to eliminate the backend bound and saturate the memory bandwidth. The APCM decreases the data arrangement's backend bound from 45% to 3% and promotes its memory bandwidth utilization by 4X-16X. Moreover, we illustrate that the APCM can be utilized to promote the performance of typical mobile edge applications such as network routing, image processing, and AI applications. The CPU time of the data arrangement process time of the selected typical mobile edge applications can be reduced by 55$\%$% - 95$\%$% when utilizing the proposed mechanism.

关键词： mobile edge computing   DAG task scheduling   delay   service deployment  

摘要： Efficient task scheduling plays a key role in unmanned aerial vehicle (UAV)-empowered edge computing due to the limitation in energy supply and computation resource on the UAV platforms. This problem becomes much more complicated when the processing-dependent tasks that can be described as directed acyclic graphs (DAGs) and each of their components can only be processed on a virtual machine or container that deploys the desired service function (SF). In this paper, we first build an optimization problem that aims to minimize the completion time of all DAG tasks subject to constraints including task dependency, computation resource occupied by the UAVs, etc. To tackle this problem, a genetic algorithm-based joint deployment and scheduling algorithm, named GA-JoDeS, is put forward, since solving the established 0-1 integer programming problem in polynomial time is infeasible. Subtask offloading decision and UAV position are encoded into the chromosome in the GA-JoDeS algorithm, and the fitness value of an individual is decided by the maximum completion time of all DAG tasks. Through selection, crossover, and mutation, the GA-JoDeS algorithm evolves until it determines the individual with the optimal fitness value as the suboptimal solution to the problem. To evaluate the performance of the proposal, a series of simulations is conducted, and three traditional methods are chosen as comparison benchmarks. The results show that the GA-JoDeS algorithm can convergence quickly, and it can effectively reduce the completion time of DAG tasks with different parameter settings.

关键词： Mission-critical communications   teleoperation   real-time systems   telerobotics   human-machine interaction   mobile edge computing   low-latency high-reliability  

摘要： The stringent timing and reliability requirements in mission-critical applications require a detailed statistical characterization of end-to-end latency. Teleoperation is a representative use case, in which a human operator (HO) remotely controls a robot by exchanging command and feedback signals. We present a framework to analyze the latency of a closed-loop teleoperation system consisting of three entities: an HO, a robot located in remote environment, and a Base Station (BS) with Mobile edge Computing (MEC) capabilities. A model of each component is used to analyze the closed-loop latency and optimize the compression strategy. The closed-form expression of the distribution of the closed-loop latency is difficult to estimate, such that suitable upper and lower bounds are obtained. We formulate a non-convex optimization problem to minimize the closed-loop latency. Using the obtained upper and lower bound on the closed-loop latency, a computationally efficient procedure to optimize the closed-loop latency is presented. The simulation results reveal that compression of sensing data is not always beneficial, while system design based on average performance leads to under-provisioning and may cause performance degradation. The applicability of the proposed analysis is much wider than teleoperation, including a large class of systems whose latency budget consists of many components.