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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Feng, Jing Wen, Mei Ju, Xin Shen, Junzhong Guo, Yang Natl Univ Def Technol Key Lab Adv Microprocessor Chips & Syst Changsha Peoples R China

ISBN: (纸本)9798350330991;9798350331004

Systolic array (SA) architectures are widely recognized as the optimal choice for Convolutional Neural Networks (CNNs). However, existing SAs suffer from reduced computational efficiency when confronted with an inadequate workload scale. Furthermore, this issue becomes even more pronounced in the accelerators that support multiple precisions. In this paper, by analyzing the under-utilization of processing element (PE), we propose a SA accelerator that optimizes computation latency for multi-precision scenarios. Considering dynamic changes in data precision, we incorporate a switching strategy to further enhance computational efficiency. Experimental results demonstrate that our proposed design only incurs a 1.203% increase in area compared to the classic approach, while achieving an average performance improvement of 20% on small-scale CNN models.

关键词： processing element utilization computation latency systolic array

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latency Minimization for UAV-Enabled URLLC-Based Mobile Edge Computing Systems

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2024年第4期23卷 3298-3311页

作者： Wu, Qingjie Cui, Miao Zhang, Guangchi Wang, Feng Wu, Qingqing Chu, Xiaoli Guangdong Univ Technol Sch Informat Engn Guangzhou 510006 Peoples R China ShanghaiJiao Tong Univ Dept Elect Engn Shanghai 200240 Peoples R China Univ Sheffield Dept Elect & Elect Engn Sheffield S1 4ET England

In this paper, we consider an unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) system, where multiple ground devices offload portions of their latency-sensitive and mission-critical computational tasks to a UAV-carried MEC server for remote computing and compute the remaining portions locally. To meet the low-latency requirements of the MEC, ultra-reliable and low-latency communication (URLLC) is used to offload tasks from the devices to the UAV. We minimize the maximum computation latency among all devices by jointly optimizing the computing times and CPU frequencies of the devices and the UAV, the offloading bandwidths of the devices, and the three-dimensional location of the UAV. We propose an algorithm that decomposes the joint optimization problem into three subproblems, which optimize the UAV's horizontal location, the UAV's altitude, and the offloading bandwidths and computing CPU frequencies, respectively. In solving the subproblems, the data rate expression of the devices' finite-blocklength offloading is accurately approximated by a tractable logarithmic function, and the successive convex approximation technique is applied to tackle the non-convex structure. Furthermore, a semi-closed-form solution to the subproblem that optimizes the bandwidths and CPU frequencies is derived to reduce the complexity. Simulation results show that the proposed algorithm can significantly reduce the system's computation latency compared to the benchmark schemes.

关键词： Unmanned aerial vehicle mobile edge computing ultra-reliable and low-latency communications computation latency

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computation Offloading and Service Caching for Mobile Edge Computing Under Personalized Service Preference

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2022年第8期21卷 6568-6583页

作者： Ko, Seung-Woo Kim, Seong Jin Jung, Haejoon Choi, Sang Won Inha Univ Dept Smart Mobil Engn Incheon 22212 South Korea Korea Railrd Res Inst KRRI Uiwang 16105 South Korea Kyung Hee Univ Dept Elect Engn Yongin 17104 South Korea Kyonggi Univ Dept Elect Engn Suwon 16227 South Korea

Mobile edge computing (MEC) has emerged as an attractive solution by executing computation-intensive services at a powerful edge server instead of mobiles. Two types of data are necessary to this end. One is user-specific data acquired from mobiles, called computation offloading (CO). The other is service-specific data downloaded from a central cloud, called service caching (SC). It is noteworthy that CO and SC decisions are coupled when each user's service preference (SP) is personalized. Specifically, noting that the optimal SC is to cache services likely to be requested more frequently, the resultant SC tends to be biased to the SP of the user whose offloading rate is high. On the other hand, such an SC decision causes longer computing latency of users with a relatively low offloading rate, which ultimately limits a CO decision for agile MEC services. This work tackles this issue from a sum-utility maximization perspective under radio-resource and computation-latency constraints. The average computation latency is first derived in closed-form by modeling a computation as a stochastic process following a hyper-exponential distribution. Based on it, we first consider the case for homogeneous SP where CO and SC decisions are decoupled. Thus, SC can be deterministically controlled using the homogeneous SP, while CO decision is independently determined, lying between water-filling and channel-inversion allocations. Next, we design a joint CO-and-SC policy for heterogeneous SP. CO and SC decisions are iteratively optimized with the other fixed by leveraging the homogeneous SP's result. The optimal stopping rules are derived, guaranteeing the sum-utility enhancement. The proposed algorithm's effectiveness is verified by simulations that the proposed CO-and-SC design for heterogenous SP always outperforms that for homogeneous SP.

关键词： Servers Wireless communication Task analysis Image edge detection computational modeling Energy consumption Resource management Mobile edge computing computation offloading service caching personalized service preference computation latency sum-utility maximization

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Improving latency in Internet-of-Things and cloud computing for real-time data transmission: a systematic literature review (SLR)

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2023年第5期26卷 2657-2680页

作者： Shukla, Saurabh Hassan, Mohd Fadzil Tran, Duc Chung Akbar, Rehan Paputungan, Irving Vitra Khan, Muhammad Khalid Natl Univ Ireland Galway NUIG Data Sci Inst DSI Elect & Elect Dept Galway Ireland Univ Teknol PETRONAS CERDAS Dept Comp & Informat Sci Seri Iskandar 32610 Perak Darul Rid Malaysia FPT Univ Comp Fundamental Dept Hanoi Vietnam Univ Tunku Abdul Rahman Fac Informat & Commun Technol Kampar Malaysia Univ Islam Indonesia Informat Dept Yogyakarta Indonesia PAF Karachi Inst Econ & Technol Coll Comp & Informat Sci Karachi Pakistan

To store, analyse and process the large volume of data generated by IoT traditional cloud computing, is used everywhere. However, the traditional cloud data centres have their limitations to handle high latency issues in time-critical applications of IoT and cloud. Their applications are computer gaming, e-healthcare, telemedicine and robot surgery. The high latency in IoTs and cloud includes high computational, communication latency (service) and network latencies. The vital requirement of IoT is to have minimum network, service and computation latencies for real-time applications. Network latency causes a delay in transmitting a message or communication from one location to another. Services that require data in real-time are almost impossible to access the data via the cloud. Traditional cloud computing approaches are unable to fulfil the quality-of-service (QoS) requirements in IoT devices. Researches related to latency reduction techniques are still in infancy. Some new approaches to minimize the latency for transmitting time-sensitive data in real-time are discussed in this paper for cloud and IoT devices. This research will help the researchers and industries to identify the techniques and technologies to minimize the latencies in IoT and cloud. The paper also discusses the research trends and the technical differences between the various technologies and techniques. With the increasing interest in the literature on latency minimization and its requirements for time-sensitive applications;it is important to systematically review and synthesize the approaches, tools, challenges and techniques to minimize latencies in IoT and cloud. This paper aims at systematically reviewing the state of the art of latency minimization to classify approaches, and techniques. The paper uses a PRISMA technique for a systematic review. The paper further identifies challenges and gaps in this regard for future research. We have identified 23 approaches and 32 technologies associat

关键词： Internet-of-Things Fog computing Cloud computing latency Service latency Communication latency Network latency Queuing delay Transmission delay computation latency

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SGedge: Stochastic Geometry-Based Model for Multi-Access Edge Computing in Wireless Sensor Networks

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IEEE ACCESS 2021年 9卷 111238-111248页

作者： Chen, Yuanfang Liu, Jia Siano, Pierluigi Hangzhou Dianzi Univ Sch Cyberspace Hangzhou 310018 Zhejiang Peoples R China Univ Salerno Dept Ind Engn I-84084 Fisciano Italy

Multi-access edge computing (MEC) is used to provide low-latency computing services for users and offload computation-intensive tasks from users. The MEC network has two parts, a server and wireless sensor access networks, with the latter all independently connected to the server. In this study, the limited latency performance of a MEC network-namely, the communication and computation latency under coverage of the sensor access networks, as well as the stability of the server-is investigated. Moreover, referring to network constraints, the scaling laws are determined, which are for both communication and computation latency regarding network loads and resource parameters. The deduced scaling laws quantify the trade-offs between computing latency, network coverage, and stability.

关键词： Task analysis Servers computational modeling Edge computing Stochastic processes Interference Wireless sensor networks latency performance communication latency computation latency multi-access edge computing (MEC) wireless sensor access networks (WSANs)

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To Compute or Not to Compute? Adaptive Smart Sensing in Resource-Constrained Edge Computing

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2024年第1期11卷 736-749页

作者： Ballotta, Luca Peserico, Giovanni Zanini, Francesco Dini, Paolo Univ Padua Dept Informat Engn I-35131 Padua Italy Univ Alberta Dept Comp Sci Edmonton AB T6G 2E8 Canada Ctr Tecnol Telecomunicac Catalunya Barcelona 08860 Spain

We consider a network of smart sensors for an edge computing application that sample a time-varying signal and send updates to a base station for remote global monitoring. Sensors are equipped with sensing and compute, and can either send raw data or process them on-board before transmission. Limited hardware resources at the edge generate a fundamental latency-accuracy trade-off: raw measurements are inaccurate but timely, whereas accurate processed updates are available after processing delay. Hence, one needs to decide when sensors should transmit raw measurements or rely on local processing to maximize network monitoring performance. To tackle this sensing design problem, we model an estimation-theoretic optimization framework that embeds both computation and communication latency, and propose a Reinforcement Learning-based approach that dynamically allocates computational resources at each sensor. Effectiveness of our proposed approach is validated through numerical experiments motivated by smart sensing for the Internet of Drones and self-driving vehicles. In particular, we show that, under constrained computation at the base station, monitoring performance can be further improved by an online sensor selection.

关键词： Sensors Monitoring Delays Sensor systems Intelligent sensors computational modeling Robot sensing systems Communication latency computation latency edge computing Q-learning resource allocation sensing design

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Joint Resource Allocation and Intrusion Prevention System Deployment for Edge Computing

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IEEE TRANSACTIONS ON SERVICES COMPUTING 2024年第5期17卷 2502-2515页

作者： Lee, Chun-Yen Lin, Chia-Hung Chang, Zhan-Lun Wang, Chih-Yu Wei, Hung-Yu Natl Taiwan Univ Dept Elect Engn Taipei 106 Taiwan Purdue Univ Elmore Family Sch Elect & Comp Engn W Lafayette IN 47906 USA Acad Sinica Res Ctr Informat Technol Innovat Taipei 115 Taiwan Natl Taiwan Univ Dept Elect Engn Taipei 106 Taiwan Natl Taiwan Univ Grad Inst Commun Engn Taipei 106 Taiwan

Distributed Denial-of-Service (DDoS) attack is critical to latency-critical systems such as Multi-Access Edge Computing (MEC) as it significantly increases the response delay of the victim service. An intrusion prevention system (IPS) is a promising solution to defend against such attacks. Still, there will be a trade-off between IPS deployment and application resource reservation as IPS deployment will reduce the computational resources for MEC applications. In this work, we propose a game-theoretic framework to study the joint computational resource allocation and IPS deployment in the MEC architecture. Given the expected attack strength and end-user demands, we study the pricing strategy of the MEC platform operator (MPO) and purchase strategy of the application service providers (ASPs). The best responses of both MPO and ASPs are derived theoretically. Based on the best responses, we propose an efficient algorithm to derive the Stackelberg equilibrium. The properties and optimality in the efficiency of the equilibrium are analyzed through simulations. The results confirm that the proposed solutions significantly increase the social welfare of the system.

关键词： IP networks Denial-of-service attack Resource management Games Computer crime Image edge detection Servers Allocation in edge computing computation latency computation resource allocation computational resources convex function convex optimization distributed denial-of-service attack dynamic pricing edge computing edge server game theory optimization problem pricing strategy poisson process queuing theory quality of experience resource allocation resource management stackelberg equilibrium stackelberg game service quality virtual machines

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Deep Reinforcement Learning Based Joint Edge Resource Management in Maritime Network

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China Communications 2020年第5期17卷 211-222页

作者： Fangmin Xu Fan Yang Chenglin Zhao Sheng Wu School of Information and Communication Engineering Beijing University of Posts and TelecommunicationsBeijing 100876China

Due to the rapid development of the maritime networks, there has been a growing demand for computation-intensive applications which have various energy consumption, transmission bandwidth and computing latency requirements. Mobile edge computing(MEC) can efficiently minimize computational latency by offloading computation tasks by the terrestrial access network. In this work, we introduce a space-air-ground-sea integrated network architecture with edge and cloud computing components to provide flexible hybrid computing service for maritime service. In the integrated network, satellites and unmanned aerial vehicles(UAVs) provide the users with edge computing services and network access. Based on the architecture, the joint communication and computation resource allocation problem is modelled as a complex decision process, and a deep reinforcement learning based solution is designed to solve the complex optimization problem. Finally, numerical results verify that the proposed approach can improve the communication and computing efficiency greatly.

关键词： maritime network edge computing computation offload computation latency reinforcement learning deep learning

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The Design of Secure Coded Edge Computing for User-Edge Collaborative Computing 20

The Design of Secure Coded Edge Computing for User-Edge Coll...

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20th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (IEEE TrustCom)

作者： Cui, Mingyue Wang, Jin Zhou, Jingya Lu, Kejie Wang, Jianping Soochow Univ Sch Comp Sci & Technol Suzhou Peoples R China Collaborat Innovat Ctr Novel Software Technol & I Nanjing Peoples R China Univ Puerto Rico Dept Comp Sci & Engn Mayaguez PR USA City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China

ISBN: (纸本)9781665416580

In recent years, edge computing (EC), as an emerging technology, has been widely used in various industries. It can meet the needs of industries in real-time business, application intelligence, security and privacy protection. However, edge devices may not always be trustworthy in the edge computing environment. Moreover, traditional edge computing systems have ignored the fact that the computation capability of user device can also be used. In this paper, we propose the Minimum computation latency Secure Edge Computing (MCLSEC) scheme to minimize computation latency and provide the security of computing data by utilizing linear coding and the resources of both edge devices and user device. Specifically, we consider the matrix multiplication as a computation task, which is an important module in many application operations, such as machine learning, big data analysis, etc.. We firstly theoretically analyze the total computation latency of edge devices and user device in the coded edge computing. We then give the design of the MCLSEC scheme, which includes of the coding scheme and the task allocation scheme. Moreover, we also give theoretical analysis to show the proposed MCLSEC scheme is secure and optimal. Finally, we conduct extensive simulation experiments to show the effectiveness of the proposed scheme. Compared with the existing schemes, MCLSEC scheme significantly reduces the computation latency of edge computing while ensuring data confidentiality.

关键词： edge computing linear coding security computation latency task allocation

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Energy Efficient Resource Allocation Over Cloud-RAN Based Heterogeneous Network 7

Energy Efficient Resource Allocation Over Cloud-RAN Based He...

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IEEE 7th International Conference on Cloud

作者： Zhang, Heli Ji, Hong Li, Xi Wang, Ke Wang, Weidong Beijing Univ Posts & Telecommun Sch Elect Engn Beijing Peoples R China Beijing Univ Posts & Telecommun Key Lab Universal Wireless Commun Minist Educ Beijing Peoples R China

ISBN: (纸本)9781467395601

To enjoy rich Internet services, contemporary mobile devices generate heavy loads of computation tasks, which cannot be suffered locally due to the limited computing and energy capacities of each device. Cloud radio access network (Cloud RNA) based heterogeneous structure is widely accepted as a promising way to make up devices' shortage and computation need. To determine whether the computation task should be migrated from the device to the Cloud RAN, traditional work usually aims to improve resource utilization efficiency and constrain the latency of computation task. However, less concerned on the energy efficiency problem for the device. Through maintaining higher energy efficiency, device can utilize the limited energy in a reasonable way. In this paper, we formulate an energy efficient resource allocation problem for the computation tasks in Cloud-RAN based heterogeneous Network (Cloud-RAN HetNet). Within this problem, two subproblems are proposed. The first is determining whether computation task on the device should be offloaded to the the Cloud RAN. The second is energy efficiency based resource allocation while considering latency limitation for the computation tasks. To solve the proposed problem, a computation location selection and resource allocation (CCSRA) method is put forward. Finally, we utilize Various simulation results show the property of the strategy.

关键词： Cloud-RAN based heterogeneous Network computation latency Resource allocation Energy Efficiency

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