关键词： Internet of Things   mobile edge computing   mixed integer nonlinear program   deep reinforcement learning  

摘要： Internet of Things (IoT) has emerged as an enabling platform for smart cities. In this paper, the IoT devices' offloading decisions, CPU frequencies and transmit powers joint optimization problem is investigated for a multi-mobile edge computing (MEC) server and multi-IoT device cellular network. An optimization problem is formulated to minimize the weighted sum of the computing pressure on the primary MEC server (PMS), the sum of energy consumption of the network, and the task dropping cost. The formulated problem is a mixed integer nonlinear program (MINLP) problem, which is difficult to solve since it contains strongly coupled constraints and discrete integer variables. Taking the dynamic of the environment into account, a deep reinforcement learning (DRL)-based optimization algorithm is developed to solve the nonconvex problem. The simulation results demonstrate the correctness and the effectiveness of the proposed algorithm.

关键词： Mobile edge computing   Edge caching   Machine learning   Mobility prediction   Interest prediction  

摘要： With the rapid development of 5G mobile networks, data traffic has increased dramatically and is putting tremendous pressure on the backhaul link. In 5G-based mobile edge computing (MEC) environment, efficient caching at the edge of the network provides a solution for satisfying the quality of experience (QoE) requirements for lower latency. An intelligent caching strategy for MEC based on machine learning has been proposed, namely EICache, which considers the user's mobility and interest preferences. It could predict user's mobility using historical trajectory based on Long Short-Term Memory (LSTM) algorithm, and predict interest using Gradient Boosting Decision Tree (GBDT) method, to obtain the content of interest in advance, and then cache the content in advance on the neighboring edge node where the user is likely to go. Performance evaluations have been conducted using public YouTube trending video datasets from Kaggle and real trajectory datasets, compared with different cache replacement methods. The metrics of the cache hit rate, and the overall request latency are used for evaluation. By training the datasets first and then predicting, the accuracy of LSTM-based location prediction is about 80%, and the accuracy of GBDT-based interest prediction reaches about 25.4%. The hit rate of the edge caching strategy is increased by 40.5% compared with the strategy of random caching without any predictions. The results have proved the efficiency of EICache, which could meet the user's QoE requirements of low request latency.

关键词： Mobile edge computing   service migration   intelligent service migration algorithm   partially observable Markov decision process  

摘要： Mobile edge computing, enabling cloud computing capabilities at edge servers, is shown to be an effective way to enhance network performance as well as user experience. However, due to user mobility and limited coverage of a single edge server, service migration is inevitable to guarantee the quality of service (QoS). The service migration problem is to decide when, where and how to migrate the ongoing service from an edge server to another. In this paper, we formulate the problem as a partially observable markov decision process (POMDP) based on the fact that an edge server can obtain only partial user's information. A learning based intelligent service migration algorithm, named iSMA, is proposed to minimize the long-term average service delay of all users. iSMA consists of two function modules, a latent space model and a cross-entropy planning algorithm. The latent space model is used to infer the full state of the environment based on the partial information observed, and the cross-entropy planning algorithm is used to search the best service migration strategy. Numerical results show that our proposed iSMA reduces the service delay by about 28% and 24% when compared with two well-known deep learning based solutions.

关键词： Device to Device   Computation Offloading   IoT   Mobile Edge Computing   Wireless Sensor Networks  

摘要： In this paper, allocation memory algorithms (i.e. First Fit, Next Fit and Best Fit) are redesigned to offload the tasks of end devices in multi-device multi-task D2D communication. The proposed algorithms offload each task to single device, which enhance the performance by 4x at their maximum performance. To enhance the performance, the next fit algorithm is redesigned to offload the task to multiple devices, which enhances the performance to 88x at its maximum performance. The proposed algorithms are evaluated for different cell scenarios (i.e. femto, pico, micro and macro cells). Simulation results demonstrates that utilizing computation offloading minimizes the latency.

关键词： Mobile edge computing  

摘要： Optimal user association and resource utilization entails a challenging problem of high latency in mobile edge computing (MEC)-driven Internet of Things (IoT) applications. Increased power consumption is another aspect that requires attention, specifically for the systems that involve huge number of users (IoT nodes) and computing devices (cloudlets/MEC nodes/fog nodes). Though MEC/fog networks are designed for low latency and low transmit power connections, yet the substantial increase in IoT devices signify the need to redesign it for future demands. In this article, we formulate an optimization problem related to quality of service-driven node selection and utility maximization subject to power and workload constraints for applications that require very low latency. Outer approximation algorithm is a proven technique in the field of optimization theory and is scalable with number of nodes. A distributed self-converging algorithm based on the outer approximation algorithm is presented in this work, which proved to be efficient for the problem formulated. Extensive simulations are done to validate the numerical results. This work concludes by comparing results of outer approximation method with matching theory to validate its effectiveness.

关键词： Servers   Codes   Privacy   Data privacy   Cryptography   Computational modeling   Spatial diversity   Coded computing   joint beamforming   mobile edge computing   privacy   spatial diversity  

摘要： We consider a mobile edge computing scenario where a number of devices want to perform a linear inference Wx on some local data x given a network-side matrix W. The computation is performed at the network edge over a number of edge servers. We propose a coding scheme that provides information-theoretic privacy against z colluding (honest-but-curious) edge servers, while minimizing the overall latency-comprising upload, computation, download, and decoding latency-in the presence of straggling servers. The proposed scheme exploits Shamir's secret sharing to yield data privacy and straggler mitigation, combined with replication to provide spatial diversity for the download. We also propose two variants of the scheme that further reduce latency. For a considered scenario with 9 edge servers, the proposed scheme reduces the latency by 8% compared to the nonprivate scheme recently introduced by Zhang and Simeone, while providing privacy against an honest-but-curious edge server.

关键词： mobile edge computing   computing offloading   scheduling   dependent tasks  

摘要： Recent years have witnessed a paradigm shift from centralized cloud computing to decentralized edge computing. As a key enabler technique in edge computing, computation offloading migrates computation-intensive tasks from resource-limited devices to nearby devices, optimizing service latency and energy consumption. In this paper, we investigate the problem of offloading task graphs in edge computing scenarios. Previous work based on list-scheduling heuristics is likely to suffer from severe processor time wastage due to intricate task dependencies and data transfer requirements. To this end, we propose a novel offloading algorithm, referred to as Iterative Dynamic Critical Path Scheduling (IDCP). IDCP minimizes the makespan by iteratively migrating tasks to keep shortening the dynamic critical path. Through IDCP, what is managed are essentially the sequences among tasks, including task dependencies and scheduled sequences on processors. Since we only schedule sequences here, the actual start time of each task is not fixed during the scheduling process, which effectively helps to avoid unfavorable schedules. Such flexibilities also offer us much space for continuous scheduling optimizations. Our experimental results show that our algorithm significantly outperforms existing list-scheduling heuristics in various scenarios, which demonstrates the effectiveness and competitiveness of our algorithm.

关键词： Resource management   Collaboration   Task analysis   Servers   Mobile handsets   Wireless communication   Delays   Mobile edge computing   computing offloading   resource allocation   power and subcarrier allocation   deep Q-network  

摘要： Mobile edge computing (MEC) is an emergent architecture, which brings computation and storage resources to the edge of mobile network and provides rich services and applications near the end users. The joint problem of task offloading and resource allocation in the multi-user collaborative mobile edge computing network (C-MEC) based on Orthogonal Frequency-Division Multiple Access (OFDMA) is a challenging issue. In this paper, we investigate the offloading decision, collaboration decision, computing resource allocation and communication resource allocation problem in C-MEC. The delay-sensitive tasks of users can be computed locally, offloaded to collaborative devices or MEC servers. The goal is to minimize the total energy consumption of all mobile users under the delay constraint. The problem is formulated as a mixed-integer nonlinear programming (MINLP), which involves the joint optimization of task offloading decision, collaboration decision, subcarrier and power allocation, and computing resource allocation. A two-level alternation method framework is proposed to solve the formulated MINLP problem. In the upper level, a heuristic algorithm is used to handle the collaboration decision and offloading decisions under the initial setting;and in the lower level, the allocation of power, subcarrier, and computing resources is updated through deep reinforcement learning based on the current offloading decision. Simulation results show that the proposed algorithm achieves excellent performance in energy efficient and task completion rate (CR) for different network parameter settings.

关键词： Task analysis   Servers   Resource management   Optimization   Radio links   Energy consumption   Computational modeling   Mobile edge computing   task offloading   resource allocation   end-to-end latency   task placement  

摘要： In this paper, we propose a novel resource management scheme that jointly allocates the transmit power and computational resources in a centralized radio access network architecture. The network comprises a set of computing nodes to which the requested tasks of different users are offloaded. The optimization problem minimizes the energy consumption of task offloading while takes the end-to-end-latency, i.e., the transmission, execution, and propagation latencies of each task, into account. We aim to allocate the transmit power and computational resources such that the maximum acceptable latency of each task is satisfied. Since the optimization problem is non-convex, we divide it into two sub-problems, one for transmit power allocation and another for task placement and computational resource allocation. Transmit power is allocated via the convex-concave procedure. In addition, a heuristic algorithm is proposed to jointly manage computational resources and task placement. We also propose a feasibility analysis that finds a feasible subset of tasks. Furthermore, a disjoint method that separately allocates the transmit power and the computational resources is proposed as the baseline of comparison. A lower bound on the optimal solution of the optimization problem is also derived based on exhaustive search over task placement decisions and utilizing Karush-Kuhn-Tucker conditions. Simulation results show that the joint method outperforms the disjoint method in terms of acceptance ratio. Simulations also show that the optimality gap of the joint method is less than 5%.

关键词： satellite-terrestrial network   mobile edge computing   satellite-terrestrial cooperation   offloading decision   resource allocation  

摘要： With the development of satellite-terrestrial network (STN), mobile edge computing (MEC) servers are deployed at low orbit earth (LEO) satellites to provide computing services for user devices (UEs) in areas without terrestrial network coverage. There is symmetry between satellite networks and terrestrial networks, but there is asymmetry between their resources. Computing resources of satellites' MEC servers may not be enough. The satellite-terrestrial cooperation is promising, where a satellite migrates tasks to a base station (BS) in an adjacent area, thus utilizing computing resources of the BS's MEC server. Although there are some studies on computation offloading in STN, few studies consider a satellite as both a relay and a computing unit to assist UEs in computing tasks. This paper proposes a joint offloading decision and resource allocation scheme in MEC-enabled STN, which minimizes the completion delay of all UEs' indivisible tasks. Firstly, the optimization problem is formulated and decomposed. Then, the proposed scheme based on potential game and the Lagrange multiplier method makes UEs' task offloading decisions and allocates the satellite's and the BS's computing resources, thus obtaining the optimal solution through continuous iterations. Finally, the simulation results validate that the proposed scheme can obtain better gain than other baseline schemes.