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检索条件"主题词=computation offloading"

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Joint Radio and computation Resource Management for Low Latency Mobile Edge Computing

Joint Radio and Computation Resource Management for Low Late...

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IEEE Global Conference on Communications (GLOBECOM) / Workshop on Wireless Networking and Control for UAV

作者： Liu, Qiang Han, Tao Ansari, Nirwan Univ N Carolina Charlotte NC 28223 USA New Jersey Inst Technol Newark NJ 07102 USA

ISBN: (纸本)9781538647271

Mobile edge computing (MEC) is a new networking paradigm that enables low-latency computation offloading for compute-intensive mobile applications. The dynamic wireless channel, non-uniform spatiotemporal traffic, and limited computation resources impair the service latency of mobile edge computing. Therefore, jointly managing radio and computation resources is needed to achieve low latency MEC. In this paper, we propose a joint radio and computation resource management (iRAR) algorithm which minimizes users' service latency by optimizing the uplink transmission power, receive beamforming, computation task assignment, and computation resource allocation. We compare the performance of the proposed algorithm with three different algorithms and demonstrate that the iRAR algorithm reduces up to 52% average service latency as compared to the other algorithms.

关键词： Mobile edge computing computation offloading beamfoming task assignment

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IONN: Incremental offloading of Neural Network computations from Mobile Devices to Edge Servers

IONN: Incremental Offloading of Neural Network Computations ...

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ACM Symposium on Cloud Computing (SoCC)

作者： Jeong, Hyuk-Jin Lee, Hyeon-Jae Shin, Chang Hyun Moon, Soo-Mook Seoul Natl Univ Seoul South Korea

ISBN: (纸本)9781450360111

Current wisdom to run computation-intensive deep neural network (DNN) on resource-constrained mobile devices is allowing the mobile clients to make DNN queries to central cloud servers, where the corresponding DNN models are pre-installed. Unfortunately, this centralized, cloud-based DNN offloading is not appropriate for emerging decentralized cloud infrastructures (e.g., cloudlet, edge/fog servers), where the client may send computation requests to any nearby server located at the edge of the network. To use such a generic edge server for DNN execution, the client should first up-load its DNN model to the server, yet it can seriously delay query processing due to long uploading time. This paper proposes IONN (Incremental offloading of Neural Network), a partitioning-based DNN offloading technique for edge computing. IONN divides a client's DNN model into a few partitions and uploads them to the edge server one by one. The server incrementally builds the DNN model as each DNN partition arrives, allowing the client to start offloading partial DNN execution even before the entire DNN model is uploaded. To decide the best DNN partitions and the uploading order, IONN uses a novel graph-based algorithm. Our experiments show that IONN significantly improves query performance in realistic hardware configurations and network conditions.

关键词： Mobile computing edge computing computation offloading neural network cyber foraging

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Joint Load Management and Resource Allocation in the Energy Harvesting Powered Small Cell Networks with Mobile Edge Computing

Joint Load Management and Resource Allocation in the Energy ...

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IEEE Conference on Computer Communications (IEEE INFOCOM)

作者： Guo, Fengxian Ma, Liangde Zhang, Heli Ji, Hong Li, Xi Beijing Univ Posts & Telecommun Key Lab Universal Wireless Commun Minist Educ Beijing Peoples R China China Acad Railway Sci Beijing Peoples R China

ISBN: (纸本)9781538659793

Considering the advantages of small cell networks (SCNs) and mobile edge computing (MEC), it is natural to combine small cells with MEC, which can not only enhance the computation capacity of the mobile user equipment (UE) but also provide the users with a high quality of services (QoS). However, the high energy consumption becomes a rigorous problem to the operators and the environment with the small cell base stations (SBSs) densely deployed. Hence, it is crucial to exploit off-grid and green energy resources to power the SBSs, for which energy harvesting (EH) is one of the viable solutions. But there impose formidable challenges combining EH-based SCNs with MEC, due to the fact that the energy arrival and traffic are not uniformly distributed across space and time domain. In this work, we jointly study the green load management and resource allocation scheme in such a network for achieving green computation offloading under the constraints of network cost and users' QoS. First, we formulate the main problem. To reduce the complexity of the proposed problem, we adopt a distributed three-stage iteration approach to solve it. To avoid overload and make full use of the resources of SBSs, cell range expansion (CRE) is used to adjust the coverage of each SBS to match the resource state and traffic pattern. After that, channel allocation is performed considering interference migration. Based on the results of the former two steps, the MEC servers dynamically distribute the computation resources to the UEs. Simulation results verified the performance of the proposed algorithm with different system parameters.

关键词： MEC small cell networks energy harvesting computation offloading load management

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Open Carrier Interface: An Open Source Edge Computing Framework

Open Carrier Interface: An Open Source Edge Computing Framew...

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ACM SIGCOMM Workshop on Networking for Emerging Applications and Technologies (NEAT)

作者： Koerner, Marc Runge, Torsten M. Panda, Aurojit Ratnasamy, Sylvia Shenker, Scott ICSI Berkeley CA 94704 USA NYU New York NY USA Univ Calif Berkeley Berkeley CA USA

ISBN: (纸本)9781450359078

Edge computing is an emerging technology, which offers manifold performance improvements for applications with low-latency and high-bandwidth requirements. In order to lower the burden for the Network Service Provider to support edge computing, we introduce a generic and platform-agnostic open source edge computing framework implementation called Open Carrier Interface. The implemented prototype provides Application Service Providers with the opportunity to deploy software components directly at the edge of the network and without any operator intervention. We will elaborate and demonstrate that the developed framework and its interfaces (i) are easy to use for all involved parties, (ii) present a unifying abstraction layer for edge-based resource management systems and edge service architectures, (iii) and can deliver a significant performance impact on applications implementing the edge service paradigm.

关键词： edge computing computation offloading

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HTML5 Web Worker Transparent offloading Method for Web Applications 18

HTML5 Web Worker Transparent Offloading Method for Web Appli...

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18th IEEE International Conference on Communication Technology (IEEE ICCT)

作者： Wang, Zhao Deng, Haojiang Hu, Linlin Zhu, Xiaoyong Chinese Acad Sci Univ Chinese Acad Sci Inst Acoust Natl Network New Media Engn Res Ctr Beijing 100190 Peoples R China Chinese Acad Sci Inst Acoust Natl Network New Media Engn Res Ctr Beijing 100190 Peoples R China

ISBN: (纸本)9781538676356

With the release of HTML5 standards, web applications have become more powerful, complicated and resource-hungry, whereas smart devices are in general resource-constrained. computation offloading is one of the approaches used to increase application efficiency and decrease energy consumption of smart devices. In this paper, the offloading methods of HTML5 web worker in web applications are discussed, and a transparent offloading method of web worker is proposed to reduce execution time of web application and energy consumption of smart device. By rewrite of web worker implementation and modification of HTML5 websocket mechanism of web platform, web worker is offloaded to server side transparently. Common non-transparent web worker offloading method with JS framework has also been implemented. The experiments results show that the proposed web worker transparent offloading method achieves better performance improvements comparing to no offloading and non-transparent offloading respectively.

关键词： HTML5 web worker websocket computation offloading transparent

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Energy-Efficient Resource Allocation in Fog Computing Supported IoT with Min-Max Fairness Guarantees

Energy-Efficient Resource Allocation in Fog Computing Suppor...

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IEEE International Conference on Communications (ICC) / Workshop on Integrating UAVs into 5G

作者： Feng, Jie Zhao, Liqiang Du, Jianbo Chu, Xiaoli Yu, F. Richard Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China Univ Sheffield Elect & Elect Engn Sheffield S1 3JD S Yorkshire England Carleton Univ Syst & Comp Engn Ottawa ON Canada

ISBN: (纸本)9781538631805

Internet of things (IoT) are envisioned to be an essential in our daily lives, but most IoT devices (IDs) are battery powered and have limited resource. Recently, fog computing (FC) has been proposed to support IoT systems, where part or all of the data are offloaded from IDs to fog nodes for processing or computation. In this paper, we propose to optimise the partial computation offloading in an OFDMA based FC IoT system, while ensuring fairness among IoT links with respect to their energy consumption. In particular, we minimize the energy consumption of the worst-case link by jointly optimizing the size of offloaded data and the assignment of subcarriers, while guaranteeing the rate requirement. The formulated minmax energy efficiency optimization problem (MEP) is solved using Lagrangian dual decomposition and subgradient projection, bases on which we propose an iterative algorithm. Our simulation results show that the proposed resource allocation algorithm is more energy efficient than the existing algorithms for FC supported IoT, while achieving fairness among IoT links.

关键词： Energy efficiency computation offloading resource allocation fog computing IoT

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Performance Optimization in Mobile-Edge Computing via Deep Reinforcement Learning 88

Performance Optimization in Mobile-Edge Computing via Deep R...

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88th IEEE Vehicular Technology Conference (VTC-Fall)

作者： Chen, Xianfu Zhang, Honggang Wu, Celimuge Mao, Shiwen Ji, Yusheng Bennis, Mehdi VTT Tech Res Ctr Finland Espoo Finland Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou Zhejiang Peoples R China Univ Electrocommun Grad Sch Informat & Engn Tokyo Japan Auburn Univ Dept Elect & Comp Engn Auburn AL 36849 USA Natl Inst Informat Informat Syst Architecture Res Div Tokyo Japan Univ Oulu Ctr Wireless Commun Oulu Finland

ISBN: (纸本)9781538663585

To improve the quality of computation experience for mobile devices, mobile-edge computing (MEC) is emerging as a promising paradigm by providing computing capabilities within radio access networks in close proximity. Nevertheless, the design of computation offloading policies for a MEC system remains challenging. Specifically, whether to execute an arriving computation task at local mobile device or to offload a task for cloud execution should adapt to the environmental dynamics in a smarter manner. In this paper, we consider MEC for a representative mobile user in an ultra dense network, where one of multiple base stations (BSs) can be selected for computation offloading. The problem of solving an optimal computation offloading policy is modelled as a Markov decision process, where our objective is to minimize the long-term cost and an offloading decision is made based on the channel qualities between the mobile user and the BSs, the energy queue state as well as the task queue state. To break the curse of high dimensionality in state space, we propose a deep Q-network-based strategic computation offloading algorithm to learn the optimal policy without having a priori knowledge of the dynamic statistics. Numerical experiments provided in this paper show that our proposed algorithm achieves a significant improvement in average cost compared with baseline policies.

关键词： computation offloading

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Energy-Efficient Dynamic Task offloading for Energy Harvesting Mobile Cloud Computing 13

Energy-Efficient Dynamic Task Offloading for Energy Harvesti...

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13th IEEE International Conference on Networking, Architecture and Storage (NAS)

作者： Zhang, Yongqiang He, Jianbo Guo, Songtao Southwest Univ Coll Elect & Informat Engn Chongqing 400715 Peoples R China Hubei Univ Technol Dept Elect & Elect Engn Wuhan 430068 Hubei Peoples R China

ISBN: (纸本)9781538683675

Mobile-edge cloud computing (MEC) as an emerging and prospective computing paradigm, can significantly enhance computation capability and prolong the lifetime of mobile devices (MDs) by offloading computation-intensive tasks to the cloud. This paper considers applying simultaneous wireless information and power transfer (SWIPT) technique to a multi-user computation offloading problem for mobile-edge cloud computing, where energy-limited mobile devices (MDs) harvest energy form the ambient radio-frequency (RF) signal. We investigate partial computation offloading by jointly optimizing MDs' clock frequency, transmit power and offloading ratio with the system design objective of minimizing energy cost of mobile devices. To this end, we first formulate an energy cost minimization problem constrained by task completion time and finite mobile-edge cloud computation capacity. Then, by exploiting alternative optimization (AO) based on difference of convex function (DC) programming and linear programming, we design an iterative algorithm for clock frequency control, transmission power allocation, offloading ratio and power splitting ratio to solve the non-convex optimization problem. Our simulation results reveal that the proposed algorithm can converge within a few iterations and yield minimum system energy cost.

关键词： Mobile-egde cloud computing energy cost computation offloading resource allocation SWIPT

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HARNESSING BANDIT ONLINE LEARNING TO LOW-LATENCY FOG COMPUTING

HARNESSING BANDIT ONLINE LEARNING TO LOW-LATENCY FOG COMPUTI...

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IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Chen, Tianyi Giannakis, Georgios B. Univ Minnesota Dept ECE Minneapolis MN 55455 USA Univ Minnesota DTC Minneapolis MN 55455 USA

ISBN: (纸本)9781538646588

This paper focuses on the online fog computing tasks in the Internet-of-Things (IoT), where online decisions must flexibly adapt to the changing user preferences (loss functions), and the temporally unpredictable availability of resources (constraints). Tailored for such human-in-the-loop systems where the loss functions are hard to model, a family of bandit online saddle-point (BanSP) schemes are developed, which adaptively adjust the online operations based on (possibly multiple) bandit feedback of the loss functions, and the changing environment. Performance here is assessed by: i) dynamic regret that generalizes the widely used static regret;and, ii) fit that captures the accumulated amount of constraint violations. Specifically, BanSP is proved to simultaneously yield sub-linear dynamic regret and fit, provided that the best dynamic solutions vary slowly over time. Numerical tests on fog computing tasks corroborate that BanSP offers desired performance under such limited information.

关键词： computation offloading fog computing Internet of Things online learning bandit convex optimization

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Optimizing Task Execution for Mobile Edge Computing [Poster]

Optimizing Task Execution for Mobile Edge Computing [Poster]

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作者： Muhammad Fayyaz Cao Bin Waleed Almughalles Yun Li Liaqat Ali Key Lab of Mobile Communication Technology Chongqing University of Posts and Telecommunications

computation-intensive applications can be enabled by mobile edge computing(MEC) in 5 G networks because MEC carries cloud computing almost near to smart devices. In this paper, we study a multi-user MEC system, where several smart devices(SDs) can fulfill computation offloading over wireless channels to a MEC server. we study the minimization of a total sum cost which is energy consumption and time delay for all the smart devices(where smart devices can choose one out of three scenarios to execute the task, i.e., full local computing scenario, full offloading execution scenario, and partial offloading execution scenario) as our objective function optimization. We mutually optimize task partition, offloading decision and computation resource sharing to reduce the total cost of the MEC system. We used an extensive search method and Lagrange method to solve these problems. Statistical results prove the effectiveness of our proposed scheme.

关键词： Mobile edge computing computing mode computation offloading task partition execution scenario

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