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IEEE Conference on Computer Communications (IEEE INFOCOM)

作者： Meng, Deng Guo, Jianmeng Zhao, Liang Zhou, Huan Xu, Shouzhi China Three Gorges Univ Coll Econ & Management Yichang Peoples R China China Three Gorges Univ Coll Comp & Informat Technol Hubei Key Lab Intelligent Vis Based Monitoring Hy Yichang Peoples R China Northwestern Polytech Univ Sch Comp Sci Xian Peoples R China

ISBN: (纸本)9798350384482;9798350384475

Mobile Edge Computing (MEC) can effectively speed up data processing and improve Quality of Service (QoS) by offloading Mobile Users' (MUs') tasks to nearby Edge Servers (ESs). However, due to the individual rationality of entities (i.e., ESs and MUs) in MEC networks, they may be reluctant to participate in the computation offloading process without reasonable resource pricing or compensation. To address the challenge, we propose a Two-stage Stackelberg game-based computation offloading and Resource Pricing mechanism (TORP). Specifically, we first introduce a broker in MEC, which rents computation resources from ESs and provides services to MUs. Next, we formulate the interactions among the broker, MUs, and ESs as a two-stage Stackelberg game, aiming to maximize their respective utilities. Then, we propose a Gradient-Ascent-Based Dynamic Iterative Search Algorithm (GADISA) and an Alternating Iteration-Based Resource Pricing and Task offloading Algorithm (AIPOA) to solve the optimization problem. Finally, simulations show that TORP greatly outperforms other benchmarks in improving the utilities of three entities.

关键词： Edge Computing computation offloading Resource pricing Stackelberg game

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M³OFF: Module-Compositional Model-Free computation offloading in Multi-Environment MEC

M<SUP>3</SUP>OFF: Module-Compositional Model-Free Computatio...

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IEEE Conference on Computer Communications (IEEE INFOCOM)

作者： Ren, Tao Hu, Zheyuan Niu, Jianwei Feng, Weikun He, Hang Chinese Acad Sci State Key Lab Intelligent Game Inst Software Beijing Peoples R China Beihang Univ Sch Comp Sci & Engn State Key Lab Virtual Real Technol & Syst Beijing Peoples R China Zhongguancun Lab Beijing Peoples R China Zhengzhou Univ Res Inst Ind Technol Sch Informat Engn Zhengzhou Peoples R China

ISBN: (纸本)9798350383515;9798350383508

computation offloading is one of the key issues in mobile edge computing (MEC) that alleviates the tension between user equipment's limited capabilities and mobile application's high requirements. To achieve model-free computation offloading when reliable MEC dynamics are unavailable, deep reinforcement learning (DRL) has become a popular methodology. However, most existing DRL-based offloading approaches are developed for a single MEC environment, with invariant system bandwidth, edge capability, task types, etc., while realistic MEC scenarios tend to be of high diversity. Unfortunately, in multi-MEC environments, DRL-based offloading faces at least two challenges, learning inefficiency and interference of offloading experiences. To address the challenges, we propose a DRL-based Multi-environmental Module-compositional Model-free computation offloading ((MOFF)-O-3) framework. (MOFF)-O-3 generates offloading policies using module composition instead of a single DRL network so that learning efficiency could be improved by reusing the same modules and learning interference could be reduced by composing different modules. Furthermore, we design multiple module composition-specific training methods for (MOFF)-O-3, including alternate modules-and-composer updates to improve training stability, loss-regularization to avoid module degeneration, and module-dropout to mitigate overfitting. Extensive experimental results on both simulation and testbed demonstrate that (MOFF)-O-3 outperforms the performances of most state-of-the-arts in multi-MEC and reaches close to single-MEC.

关键词： Mobile Edge Computing computation offloading Module Composition Multiple Environments

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NOMA Enabled Multi-Access Edge Computing: A Joint MU-MIMO Precoding and computation offloading Design 33

NOMA Enabled Multi-Access Edge Computing: A Joint MU-MIMO Pr...

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33rd International Symposium on Industrial Electronics (ISIE)

作者： Zhang, Deyou Lyu, Honghao Beihang Univ Sch Elect & Informat Engn Beijing 100191 Peoples R China Zhejiang Univ Sch Mech Engn State Key Lab Fluid Power & Mechatron Syst Hangzhou Peoples R China

ISBN: (纸本)9798350394085;9798350394092

This paper investigates computation offloading and transmit precoding co-design for multi-access edge computing (MEC), where multiple MEC users (MUs) equipped with multiple antennas access the MEC server in a non-orthogonal multiple access manner. We aim to minimize the total energy consumption of all MUs while satisfying the latency constraints by jointly optimizing the computational frequency, offloading ratio, and precoding matrix of each MU. For tractability, we first decompose the original problem into three subproblems and then solve these subproblems iteratively until convergence. Simulation results validate the convergence of the proposed method and demonstrate its superiority over baseline algorithms.

关键词： Multi-access edge computing non-orthogonal multiple access computation offloading precoding design

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Artifact: Wasimoff: Distributed computation offloading Using WebAssembly in the Browser

Artifact: Wasimoff: Distributed Computation Offloading Using...

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IEEE International Conference on Pervasive Computing and Communications (PerCom)

作者： Semjonov, Anton Bornholdt, Heiko Edinger, Janick Russo, Gabriele Russo Univ Hamburg Dept Informat Hamburg Germany Univ Roma Tor Vergata Rome Italy

ISBN: (纸本)9798350304367;9798350304374

This artifact guide describes the Wasimoff research prototype - an innovative browser-based framework designed for dynamic volunteer computing and computation offloading. Leveraging WebAssembly, a wide range of applications can run securely in an isolated browser environment. Device owners can effortlessly share their available CPU cycles and participate in the collaborative network by simply accessing a web page in their browser.

关键词： serverless computing computation offloading peer-to-peer computing webassembly

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Content-aware Input Scaling and Deep Learning computation offloading for Low-Latency Embedded Vision

Content-aware Input Scaling and Deep Learning Computation Of...

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IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Prabhune, Omkar Chen, Tianen Kim, Younghyun Purdue Univ W Lafayette IN 47907 USA Univ Wisconsin Madison WI 53706 USA Google Mountain View CA 94043 USA

ISBN: (纸本)9798350365474

Deploying deep learning (DL) models for visual recognition on embedded systems is often constrained by their limited compute power and storage capacity, and has stringent latency and power requirements. As emerging DL applications continue to evolve, they place increasing demands on computational resources that embedded vision systems are unable to provision. One promising solution to overcome these limitations is computation offloading. However, for performance improvements to be realized, it is essential to carefully partition tasks, taking into account both the quality of the data and the communication overhead. In this paper, we introduce a novel framework for content-aware offloading of DL computations, aimed at maximizing quality-of-service while adhering to latency constraints. Our proposed framework involves the embedded vision system/edge device intelligently compressing data in a contentaware manner using a lightweight model and transmitting it to a more powerful server. The framework consists of two key components: offline training for efficient content-aware data scaling and online control that adapts to the network variations in real-time. To illustrate the effectiveness of our approach, we apply it to multiple downstream tasks such as face identification, person keypoint detection, and instance segmentation, showcasing a significant enhancement in the overall quality of results for various applications.

关键词： AI on edge computation offloading content-aware compression content-aware offloading deep learning edge computing edge ML efficient deep learning efficient ML embedded vision face identification low-latency deep learning low-power ML ml system tinyML

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Multi-objective Optimization for Joint Handover Decision and computation offloading in Integrated Communications and Computing 6G Networks 1

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23rd International Conference on Algorithms and Architectures for Parallel Processing (ICA3PP)

作者： Wu, Dong-Fang Huang, Chuanhe Yin, Yabo Huang, Shidong Gong, Hui Wuhan Univ Sch Comp Sci Wuhan Peoples R China Unit 92192 Ningbo Peoples R China

ISBN: (数字)9789819708598

ISBN: (纸本)9789819708581;9789819708598

Mobile edge computing (MEC) deploys the edge computing servers (ECSs) to the network edge and alleviates the problems of limited computational resources and power for mobile users' equipment (UE). Thus, MEC supports the massive computation-intensive applications in the integrated communications and computing 6G network (CCN). However, in mobility management of CCN, users' mobility triggers the new handover between not only two BSs but also two ECSs. When we select the optimal BS, we also need to consider whether the co-located ECS has the sufficient computational resources and low queuing delay. To obtain the lower offloading delay, the existing offloading methods produce extra handover in the decision of the optimal ECS. What's more, the existing handover decision methods ignore the problem of limited computational resources of ECS. In this paper, to meet the demands of communication and computation services, we propose a joint decision method based on multi-objective optimization method (JD-MOO) to solve the joint handover decision and computation offloading problem. We define the services satisfaction degree functions to evaluate the quality of two services. Simulation results show that the proposed JD-MOO method has good performance of handover and offloading.

关键词： Integrated Communications and Computing 6G Networks Handover Decision computation offloading

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Accelerator-Aware computation offloading under Timing Constraints

Accelerator-Aware Computation Offloading under Timing Constr...

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International Conference on Computing, Networking and Communications (ICNC)

作者： Latzko, Vincent Vielhaus, Christian Mehrabi, Mahshid Fitzek, Frank H. P. Tech Univ Dresden Deutsch Telekom Chair Commun Networks D-01062 Dresden Germany Barkhausen Inst Dresden Germany Tech Univ Dresden Ctr Tactile Internet Human Loop CeTI D-01062 Dresden Germany

ISBN: (纸本)9798350371000;9798350370997

The rise of chiplets in personal and high performance computing is mirrored in System on Chip (SOC) in mobile devices. Both paradigms allow vendors and designers to integrate dedicated circuitry for accelerating computation. Implementations like cryptographic or vector engines are well known, and nowadays Machine Learning (ML) blocks are often included to accelerate Deep Neural Network (DNN) inference. The shift toward diverse device architectures, as exemplified by RISC-V, is poised to gain momentum. The widespread integration of accelerators in smartphones, tablets, SoCs, and dedicated server systems, is opening up exciting new innovations. In this short paper we present computation offloading for specific workloads in the framework of Multi-Access Edge Computing (MEC) and energy optimisation. We honour intertask dependency through use of a Directed Acyclic Graph (DAG). Our system model with multiple mobile users, Device-to-Device (D2D) links between User Equipments (UEs), and edge servers enables computational and communication cooperation. The system's energy efficiency is significantly improved by introducing accelerators to the UEs and the MEC. We study the capabilities of the devices (accelerators) and propose an effective solution.

关键词： computation offloading Multi-Access Edge Computing Heterogeneous Computing

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Accelerating Fingerprint-based Person Identification through computation offloading in Edge and Cloud Environments 12

Accelerating Fingerprint-based Person Identification through...

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12th International Conference on Cloud Networking (CloudNet)

作者： Barbosa, Renan A. Andrade, Joao P. B. Da Silva, Mauro R. C. Trinta, Fernando A. M. Rego, Paulo A. L. Software Engn & Syst GREat Grp Comp Networks Fortaleza Ceara Brazil Fed Univ Ceara UFC Fortaleza Ceara Brazil Univ Campinas Unicamp Inst Comp Campinas SP Brazil

ISBN: (纸本)9798350313062;9798350313079

The increasing popularity of smartphones has driven the development of advanced features, including access to users' biometric data and a wide range of services via wireless connections. This provides developers with opportunities to create increasingly complex applications. However, the limited resources on mobile devices pose challenges when executing operations such as fingerprint-based person identification, especially with large datasets. Given these limitations, we propose and evaluate a software architecture to enable fingerprint identification on mobile devices. Our approach leverages computation offloading techniques to migrate application components to cloud and edge/cloudlet servers. Through our evaluation, we have observed that offloading the task to cloudlet or cloud servers significantly accelerates the identification process, even when multiple devices simultaneously offload to the remote environment. These findings highlight the potential benefits of leveraging offloading to enhance the performance of fingerprint identification on mobile devices.

关键词： fingerprint-based person identification computation offloading mobile cloud computing

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computation offloading in Multi-Access Edge Computing: A Multi-Task Learning Approach

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2021年第9期20卷 2745-2762页

作者： Yang, Bo Cao, Xuelin Bassey, Joshua Li, Xiangfang Qian, Lijun Texas A&M Univ Syst Dept Elect & Comp Engn CREDIT Ctr Prairie View A&M Univ Prairie View TX 77446 USA Univ Houston Dept Elect & Comp Engn Houston TX 77004 USA

Multi-access edge computing (MEC) has already shown great potential in enabling mobile devices to bear the computation-intensive applications by offloading some computing jobs to a nearby access point (AP) integrated with a MEC server (MES). However, due to the varying network conditions and limited computational resources of the MES, the offloading decisions taken by a mobile device and the computational resources allocated by the MES can be formulated as a mixed-integer nonlinear programming (MINLP) problem, which may not be optimized with the lowest cost. In this paper, we propose a novel offloading framework for the multi-server MEC network where each AP is equipped with an MES assisting mobile users (MUs) in executing computation-intensive jobs via offloading. Specifically, we formulate the offloading decision problem as a multiclass classification problem and formulate the MES computational resource allocation problem as a regression problem. Then a multi-task learning based feedforward neural network (MTFNN) model is designed and trained to jointly optimize the offloading decision and computational resource allocation. Numerical results show that the proposed MTFNN outperforms the conventional optimization method in terms of inference accuracy and computational complexity.

关键词： Optimization Resource management computational modeling Delays Training Mobile computing Mobile handsets Multi-access edge computing neural networks multi-task learning mixed-integer nonlinear programming (MINLP) computation offloading

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Joint Optimization of computation offloading, Dynamic Pricing and Admission Control in Mobile Edge Computing-Enable Blockchain 24

Joint Optimization of Computation Offloading, Dynamic Pricin...

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3rd International Conference on Signal Processing, Computer Networks and Communications, SPCNC 2024

作者： Liu, Yi Zhang, Wenjie Zheng, Yifeng Chen, Liming Key Lab of Data Science and Intelligence Application School of Computer Sciences Minnan Normal University Fujian Zhangzhou China Zhangzhou Affiliated Hospital of Fujian Medical University Fujian Zhangzhou China

ISBN: (纸本)9798400710834

In order to cope with the problem of insufficient resources on mobile devices, the combination of blockchain and Mobile Edge Computing (MEC) has attracted much attention. In this paper, we consider an edge-enabled blockchain system containing two types of miners and a single Edge Service Provider (ESP). Different types of miners have different resource requirements and corresponding prices. Each miner makes a computation offloading decision based on the resource price set by ESP. Different miners possess varying resource requirements and correspond to distinct prices. Miners formulate task offloading decisions based on the prices set by ESP for different resource types. The ESP dynamically adjusts resource price based on miners’ offloading requests and makes admission control according to resource usage in each time slot. The problem is a joint optimization problem of computation offloading, price strategy, and admission control, we model the problem as a sequential decision-making problem and use a dynamic programming algorithm to obtain the optimal solution. Through simulation experiments, our results demonstrate that the proposed algorithm can effectively improve the overall social utility. © 2024 Copyright held by the owner/author(s).

关键词： computation offloading

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