关键词： Mobile edge computing  

摘要： Mobile edge computing (MEC) is a prominent computing paradigm which expands the application fields of wireless communication. Due to the limitation of the capacities of user equipments and MEC servers, edge caching (EC) optimization is crucial to the effective utilization of the caching resources in MEC-enabled wireless networks. However, the dynamics and complexities of content popularities over space and time as well as the privacy preservation of users pose significant challenges to EC optimization. In this paper, a privacy-preserving distributed deep deterministic policy gradient (P2D3PG) algorithm is proposed to maximize the cache hit rates of devices in the MEC networks. Specifically, we consider the fact that content popularities are dynamic, complicated and unobservable, and formulate the maximization of cache hit rates on devices as distributed problems under the constraints of privacy preservation. In particular, we convert the distributed optimizations into distributed model-free Markov decision process problems and then introduce a privacy-preserving federated learning method for popularity prediction. Subsequently, a P2D3PG algorithm is developed based on distributed reinforcement learning to solve the distributed problems. Simulation results demonstrate the superiority of the proposed approach in improving EC hit rate over the baseline methods while preserving user privacy. © 2021, CC BY.

关键词： Mobile edge computing  

摘要： —In recent years, deep learning models have become ubiquitous in industry and academia alike. Modern deep neural networks can solve one of the most complex problems today, but coming with the price of massive compute and storage requirements. This makes deploying such massive neural networks challenging in the mobile edge computing paradigm, where edge nodes are resource-constrained, hence limiting the input analysis power of such frameworks. Semantic and layer-wise splitting of neural networks for distributed processing show some hope in this direction. However, there are no intelligent algorithms that place such modular splits to edge nodes for optimal performance. This work proposes a novel placement policy, SplitPlace, for the placement of such neural network split fragments on mobile edge hosts for efficient and scalable computing. © 2021, CC BY.

关键词： context‐   awareness   data abstraction   edge computing environment   embedding convergence   knowledge representation  

摘要： Context-awareness techniques generally deal with steps for processing information that can be gathered from perspectives of objects in real-world scenes in accordance with resolving heterogeneous devices and providing meaningful context information in recent computing domains such as Internet of Things, cloud computing, edge computing, and big data analysis. However, analysis applications are necessary to be improved in a way of cooperative approaches because they are independently executed far away from context-aware systems. This means that the stages are applied to process the data or the derived results, are isolated. That leads difficulties of management and integration for other software modules. As a result, a computing paradigm called edge computing has been suggested to compromise such isolations. For processing context information, it is necessary to establish knowledge parts with validation processes against the isolations. In this paper, we suggest a method for processing datasets in a view of building knowledge parts. The suggested method uses an extensible data enrichment scheme, which represents the relations for the object. In the experiment section, applications of the proposed method are shown in one of the healthcare community centers, and the data abstraction with the analysis jobs can be processed for each real-world object.

关键词： Mobile edge computing  

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. Copyright © 2021, The Authors. All rights reserved.

关键词： Task analysis   Wireless communication   Resource management   Trajectory   Optimization   Wireless sensor networks   Unmanned aerial vehicles   Ubiquitous network accessibility   Beyond 5G   Unmanned aerial vehicle   Mobile edge computing   Wireless power transmission   Task offloading  

摘要： Providing the ubiquitous network accessibility is the key goal for 5G and Beyond 5G (B5G) networks. As the number of devices and data increase, ensuring the Quality of Service(QoS) by the existing network with a singular focus could be challenging. Meanwhile, it always is of the utmost importance to perform the computation-intensive or delay-sensitive tasks welland provide long-term services for the B5G networks. To tackle these challenges, we present an air-ground integrated network in B5G wireless communications, where an UAV is deployed as an aerial radio access platform to formulate system strategy intelligently, as well as provide task offloading and energy harvesting opportunities for terrestrial devices. To get more insight of it, we propose an intelligent charging-offloading scheme and formulate the joint multi-taskcharging-offloading scheduling as an optimization problem aiming to minimizing the system servicelatency of all devices by jointly optimizing the task offloading decisions, connection scheduling, charging and computation resources allocation of UAV. However, the formulated optimization problem is a Mixed-Integer Nonlinear Programming (MINLP) problem which is challenging to solve in general. Therefore, we decompose it into multiple convex sub-problems based on Block-Coordinate Descent (BCD) method and develop an improved greedy algorithm to obtain a feasible optimal solution. To validate the proposed algorithm, it is comprehensively compared with several existing schemes. Performance evaluation demonstrates that our scheme outperforms the benchmarks in terms of the system service latency of all UDs. Moreover, we represent the system working process and the paradigm of industrial applications.

关键词： Relays   Internet of Things   Delays   Servers   Unmanned aerial vehicles   Cloud computing   Mathematical model   Internet of things   mobile edge computing   unmanned aerial vehicle   relay communication  

摘要： At present, the Internet of things (IoT) is widely employed in various systems. The operation of these systems requires intelligent data processing, which needs to be performed rapidly at the network edge. Hence, studies on mobile edge computing (MEC) have been increasingly conducted in recent years. In MEC, edge servers perform data processing to reduce service delay. To meet the demand for computational and communication resources in infrastructureless areas or during large-scale disasters, the use of unmanned aerial vehicles (UAVs) in providing computational resources has attracted considerable attention. However, in MEC using UAVs, it is necessary to consider the workload balance and communication range of vehicles used. In this study, we highlight problems associated with such systems from a new perspective. Furthermore, we propose a relay control method equalizing the workload balance between UAVs and reducing network delay. We modeled the proposed method as a complex system, mathematically formulating each delay element. Additionally, experimental results highlight the effectiveness of the proposed method in various environments. The reduction in delay considerably improved the feasibility of the system. This study is expected to help achieve a significant advancement in MEC using UAVs by establishing a foundation for further research.

关键词： Mobile edge computing   energy efficiency   task allocation   dynamic frequency scaling  

摘要： As an emerging computing paradigm, mobile edge computing (MEC) can improve users' service experience by provisioning the cloud resources close to the mobile devices. With MEC, computation-intensive tasks can be processed on the MEC servers, which can greatly decrease the mobile devices' energy consumption and prolong their battery lifetime. However, the highly dynamic task arrival and wireless channel states pose great challenges on the computation task allocation in MEC. This paper jointly investigates the task allocation and CPU-cycle frequency, to achieve the minimum energy consumption while guaranteeing that the queue length is upper bounded. We formulate it as a stochastic optimization problem, and with the aid of stochastic optimization methods, we decouple the original problem into two deterministic optimization subproblems. An online Task Offloading and Frequency Scaling for Energy Efficiency (TOFFEE) algorithm is proposed to obtain the optimal solutions of these subproblems concurrently. TOFFEE can obtain the close-to-optimal energy consumption while bounding the applications' queue length. Performance evaluation is conducted which verifies TOFFEE's effectiveness. Experiment results indicate that TOFFEE can decrease the energy consumption by about 15 percent compared with the RLE algorithm, and by about 38 percent compared with the RME algorithm.

关键词： Task analysis   Energy consumption   Delays   Mobile handsets   Cloud computing   Servers   Edge computing   Mobile edge computing   fog computing   computation sharing   NSGA2   multi-objective optimization   evolutionary algorithms   energy consumption   delay  

摘要： In a mobile edge computing (MEC) network, mobile devices, also called edge clients, offload their computations to multiple edge servers that provide additional computing resources. Since the edge servers are placed at the network edge, e.g., cell-phone towers, transmission delays between edge servers and edge clients are shorter compared to those of cloud computing. In addition, edge clients can offload their tasks to other nearby edge clients with available computing resources by exploiting the Fog Computing (FC) paradigm. A major challenge in MEC and FC networks is to assign the tasks from edge clients to edge servers, as well as to other edge clients, in such a way that their tasks are completed with minimum energy consumption and minimum processing delay. In this paper, we model task offloading in MEC as a constrained multi-objective optimization problem (CMOP) that minimizes both the energy consumption and task processing delay of the mobile devices. To solve the CMOP, we design an evolutionary algorithm that can efficiently find a representative sample of the best trade-offs between energy consumption and task processing delay, i.e., the Pareto-optimal front. Compared to existing approaches for task offloading in MEC, we see that our approach finds offloading decisions with lower energy consumption and task processing delay.

关键词： mobile edge computing   distributed matrix multiplication   coded computing   cooperative transmission  

摘要： This paper exploits coding to speed up computation offloading in a multi-server mobile edge computing(MEC)network with straggling servers and channel *** specific task we consider is to compute the product between a user-generated input data matrix and a large-scale model matrix that is stored distributively across the multiple edge *** key idea of coding is to introduce computation redundancy to improve robustness against straggling servers and to create communication redundancy to improve reliability against channel *** utilize the hybrid design of maximum distance separable(MDS)coding and repetition *** on the hybrid coding scheme,we conduct theoretical analysis on the average task uploading time,average edge computing time,and average output downloading time,respectively and then obtain the end-to-end task execution *** results demonstrate that when the task uploading phase or the edge computing phase is the performance bottleneck,the hybrid coding reduces to MDS coding;when the downlink transmission is the bottleneck,the hybrid coding reduces to repetition *** hybrid coding also outperforms the entangled polynomial coding that causes higher uplink and downlink communication loads.

关键词： Servers   Cloud computing   Robustness   Delays   Edge computing   Partitioning algorithms   Cellular networks   Mobile edge computing   partitioning   geographical awareness  

摘要： The success of a mobile edge computing network depends critically on how to assign the edge servers to the user cells. The criterion for this assignment is application-specific. In many practical applications, the workload demanded by each cell is unknown and time-varying. So are the effective capacities of the servers. We need an assignment incurring minimum backhaul cost that is robust to these uncertainties. We also want to make the cells assigned to the same server form a geographically compact cluster. This challenge motivates us to introduce a novel stochastic geo-aware partitioning problem. As it is computationally hard, we propose a heuristic algorithm that can produce a range of solutions representing different tradeoffs between cost minimization versus geographical awareness. We evaluate the proposed algorithm using a real-world dataset.