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ATO-EDGE: Adaptive Task Offloading for Deep Learning in Resource-Constrained Edge computing Systems 27

ATO-EDGE: Adaptive Task Offloading for Deep Learning in Reso...

引用

27th IEEE international conference on parallel and distributed Systems, ICPADS 2021

作者： Wang, Yihao Gao, Ling Ren, Jie Cao, Rui Wang, Hai Zheng, Jie Gao, Quanli Northwest University China Shannxi Normal University China Xian Polytechnic University China

ISBN: (纸本)9781665408783

On-device deep learning enables mobile devices to perform complex tasks, such as object detection and voice translation, regardless of the network condition. The advanced deep learning model gives an excellent performance, also leads to a heavy burden on resource-limited devices (i.e., mobile devices). To speed up the on-device deep learning. Prior studies focus on developing lightweight network architecture for real-time inference by sacrificing model accuracy. This paper presents ATO-EDGE: adaptive task offloading for deep learning based on edge computing. Considering three optimization goals, energy consumption, accuracy, and latency, ATO-EDGE leverages an offline pre-trained model to select a suitable deep learning model on a specific device to process the given task. We apply our approach to object detection and evaluate it on Jetson TX2, Xilinx ZYNQ 7020, and Raspberry 3B+. The deep learning model candidates contain ten typical object detection models trained on Microsoft COCO 2017 dataset. We obtain, on average, 28.25%, 35.44%, and 0.9 improvements respectively for latency, energy consumption, and mAP (mean average precision) when compared to the SOTA DETR model on the Raspberry Pi. © 2021 IEEE.

关键词： Energy efficiency

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Reactive power optimization system of distribution network based on edge calculation

Reactive power optimization system of distribution network b...

引用

2021 international conference on Electrical, Electronics and computing Technology, EECT 2021

作者： Huang, Jinbo Fang, Jiangxiao Hu, Liexiang Shi, Bolong Li, Suirong Tu, Yiyan Jiaxing Power Supply Company State Grid Zhejiang Electric Power Co. LTD. Jiaxing314016 China State Grid Zhejiang Electric Power Co. LTD. Hangzhou310000 China Hangzhou Wairui Electric Power Technology Co. LTD. Hangzhou310000 China

To deal with the adjustment difficulties caused by the access of photovoltaic (PV) in distribution network, this paper introduces a reactive power optimization system of distribution network based on edge calculation. The proposed system includes two parts. Firstly, the distribution PV edge nodes based on edge calculation are carefully designed. Secondly, based on the edge nodes, the PV station, the grid reactive power optimization, the Automatic Voltage Control (AVC) centre, and the distribution photovoltaic power reactive voltage are controlled co-ordinately. The simulation results prove that the proposed system can effectively improve the voltage quality and the voltage regulation ability of distribution network. © Published under licence by IOP Publishing Ltd.

关键词： Voltage regulators

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A New distributed Optimization Algorithm for MINLPs with Affinely Coupled Decision Variables 20

A New Distributed Optimization Algorithm for MINLPs with Aff...

引用

4th international conference on Algorithms, computing and Systems (ICACS) / 2nd African Electronics, Computer and Communication conference (AECCC)

作者： Murray, Alexander Hagenmeyer, Veit Karlsruhe Inst Technol Karlsruhe Germany

ISBN: (纸本)9781450377324

The present paper provides a new algorithm for distributed optimization of Mixed-Integer Nonlinear Programs (MINLPs) with separable, affinely coupled decision variables. The algorithm is based on a standard branch and bound method but extended to allow for distributed or parallel computing framework. For the subclass of Mixed-Integer Convex Programs, convergence and optimality guarantees are given. A suite of benchmark problems are used to evaluate the performance of the new distributed algorithm.

关键词： Branch and bound method

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Resource provisioning and workload scheduling of CMS offline computing 40

Resource provisioning and workload scheduling of CMS offline...

引用

40th international conference on High Energy Physics, ICHEP 2020

作者： Yzquierdo, A. Perez-Calero Madrid Spain Barcelona Spain

The CMS experiment requires vast amounts of computational capacity in order to generate, process and analyze the data coming from proton-proton collisions at the Large Hadron Collider, as well as Monte Carlo simulations. CMS computing needs have been mostly satisfied up to now by the supporting Worldwide LHC computing grid (WLCG), a joint collaboration of more than a hundred computing centers geographically distributed around the world. However, as CMS faces the Run 3 and High Luminosity LHC (HL-LHC) challenges, with increasing luminosity and event complexity, growing demands for CPU have been estimated. In these future scenarios, additional contributions from more diverse types of resources, such as Cloud and High-Performance computing (HPC) clusters, will be required to complement the limited growth of the capacities of WLCG resources. A number of strategies are being evaluated on how to access and use WLCG and non-WLCG processing capacities as part of a combined infrastructure, successfully exploit an increasingly more heterogeneous pool of resources, efficiently schedule computing workloads according to their requirements and priorities, and timely deliver analysis results to the collaboration, which are described in this paper. © Copyright owned by the author(s) under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 international License (CC BY-NC-ND 4.0).

关键词： Luminance

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Enhanced Particle Swarm Optimization for Workflow Scheduling in Clouds 8

Enhanced Particle Swarm Optimization for Workflow Scheduling...

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IEEE international conference on Progress in Informatics and computing (IEEE PIC)

作者： Lu, Chang Feng, Dayu Zhu, Jie Huang, Haiping Nanjing Univ Posts & Telecommun Nanjing Peoples R China Jiangsu Key Lab Big Data Secur & Intelligent Proc Nanjing Peoples R China Chinese Acad Sci Inst Comp Technol State Key Lab Comp Architecture Beijing Peoples R China Jiangsu High Technol Res Key Lab Wireless Sensor Nanjing Peoples R China

ISBN: (纸本)9781665426558

As a NP-hard problem, it is always baffling to figure out a scheduling strategy to arrange the interconnected tasks of a workflow on the infinite number of resources in the cloud environment so that the workflow can be addressed efficiently and robustly. This paper focuses on scheduling the workflow's tasks on the cloud resources with less rental cost of resources while the whole schedule length (makespan) will not exceed the given deadline. As one of the most popular evolutionary algorithms, particle swarm optimization (PSO) has been successfully applied for the workflow scheduling problem. Inspired by the idea of multiple groups and the distributed parallel computing, we develop an enhanced PSO algorithm for the workflow scheduling problem in clouds. Besides, a pretreatment strategy is adopted to simplify the workflow's structure. The experimental results demonstrate that our proposal has good performance on improving the algorithm's searching ability and finding better solutions.

关键词： Workflow scheduling cloud computing particle swarm optimization multiple groups

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Entropy and Mobility-Based Model Assignment for Multi-Model Vehicular Federated Learning

Entropy and Mobility-Based Model Assignment for Multi-Model ...

引用

Federated Learning Technologies and Applications (FLTA), international conference on

作者： Wellington Lobato Joahannes B. D. Da Costa Luis F. G. Gonzalez Eduardo Cerqueira Denis Rosário Christoph Sommer Leandro A. Villas Universidade Estadual de Campinas (UNICAMP) Institute of Computing (IC) Brazil Federal University of Pará (UFPA) Institute of Natural Sciences (ICEN) Brazil Faculty of Computer Science TU Dresden Germany

ISBN: (数字)9798350354812

ISBN: (纸本)9798350354829

Machine Learning (ML) is extensively employed for key functions of Connected and Autonomous Vehicles (CAVs), where many models are executed simultaneously within a vehicle to provide diverse applications, from perception to planning and control. One of the most appealing ML approaches for CAVs is Federated Learning (FL) due to its privacy-preserving nature and distributed learning capabilities. However, current FL approaches mostly focus on single-model training and are unsuitable for parallel training of multiple models. Multi-model FL involves training multiple ML models to perform different tasks, often simultaneously, to meet the demands of different applications within the same context. In this way, this work introduces MELRO, an FL model assignment algorithm based on link duration, training latency, and data entropy from CAVs. MELRO balances computing resources and addresses high vehicle mobility while considering the heterogeneity of data and availability of resources in CAVs. The assignment algorithm takes advantage of data transmitted periodically by CAVs, such as beacons, to calculate link duration and training latency, define the model assignment matrix for CAVs, and consider data entropy. Finally, MELRO increases accuracy for FL applications by at least 11.76% while reducing training latency by 25% and maintaining computational resource usage.

关键词： Training Computer aided instruction Connected vehicles Federated learning Distance learning Computational modeling Entropy Data models Planning Context modeling

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distributed ADMM Based on Sparse Computation and Allreduce Communication

Distributed ADMM Based on Sparse Computation and Allreduce C...

引用

IEEE international conference on Big Data and Cloud computing (BdCloud)

作者： Zeyu Zhang Yongmei Lei Dongxia Wang Guozheng Wang School of Computer Engineering and Science Shanghai University Shanghai China School of Information Science and Engineering Hunan Institute of Science and Technology Yueyang China

The distributed alternating direction method of mul-tipliers (ADMM) is an effective algorithm to solve large-scale op-timization problems. However, there are still massive computation and communication cost in distributed ADMM when processing high-dimensional data. To solve this problem, we propose a distributed ADMM with sparse computation and Allreduce communication (SCAC-ADMM) which can process high-dimensional data effectively. In the algorithm, each node optimizes a sub-model of the target model in parallel. Then, the target model is obtained by aggregating all sub-models. The features in the sub-model are named associated features. In SCAC-ADMM, we first design a selecting method of associated features to determine the composition of each sub-model. This method can limit the dimension of the sub-model by setting appropriate parameters, so as to limit the computation cost. Secondly, to reduce the communication traffic caused by transmitting high-dimensional parameters, we propose a novel Allreduce communication model which can only aggregate associated parameters in sub-models. Experiments on high-dimensional datasets show that SCAC-ADMM has less computation cost and higher communication efficiency than traditional distributed ADMM. When solving large-scale logistic regression problem, SCAC-ADMM can reduce the system time by 73% compared with traditional distributed ADMM.

关键词： Costs Computational modeling Design methodology Aggregates distributed databases Filtering algorithms Convex functions

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A Mathematical Framework for Optimizing Lexical Analysis on Multi-Core Architectures

A Mathematical Framework for Optimizing Lexical Analysis on ...

引用

Sustainable Expert Systems (ICSES), international conference on

作者： Vaikunta Pai T Priyadarshini P B Sunil Kamath Shreyas Vivek Manjunatha Nethravathi P S Department of ISE NITTE (Deemed to be University) NMAM Institute of Technology (NMAMIT) Nitte India Department of Computer Applications St Joseph Engineering College Mangaluru India School of Information Technology Deakin University Waurn Ponds VIC Australia Department of CSE Manipal Institute of Technology Manipal Academy of Higher Education (MAHE) Manipal India Department of CSE Shree Devi Institute of Technology Mangaluru India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

In the field of multi-core computing systems, optimizing the distribution of tasks across multiple cores is essential to harness maximum performance potential. Traditional static task allocation methods, often employed in lexical analysis-the initial and vital phase of compiling and interpreting source code-frequently result in imbalanced workloads and suboptimal resource utilization. This paper introduces an innovative and sophisticated mathematical model for dynamic task allocation specifically tailored to lexical analysis, aiming to revolutionize how tasks are distributed across multiple cores. By employing advanced dynamic allocation techniques, our proposed model not only ensures balanced workload distribution but also scales efficiently with varying computational demands. This novel approach significantly enhances the efficiency of lexical analysis in multi-core environments, leading to substantial improvements in processing times and resource utilization. Extensive experimental results validate the superiority of our method, showcasing dramatic reductions in processing latency and a more effective exploitation of multi-core capabilities. This dynamic model not only advances the field of lexical analysis but also sets a new benchmark for task allocation strategies in multi-core computing systems, promising broader applicability and future innovations in compiler design and resource management.

关键词： Adaptation models Analytical models Multicore processing Computational modeling Scalability parallel processing Dynamic scheduling Mathematical models Computational efficiency Resource management

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SEIZE User Desired Moments: Runtime Inspection for parallel Dataflow Systems 40

SEIZE User Desired Moments: Runtime Inspection for Parallel ...

引用

40th IEEE international conference on distributed computing Systems (ICDCS)

作者： Li, Youfu Interlandi, Matteo Psallidas, Fotis Wang, Wei Zaniolo, Carlo Univ Calif Los Angeles Los Angeles CA 90095 USA Microsoft Redmond WA USA

ISBN: (纸本)9781728170022

In Data-Intensive Scalable computing (DISC) Systems, data transformations are concealed by exposed APIs, and intermediate execution moments are masked under dataflow transitions. Consequently, many crucial features and optimizations (e.g., debugging, data provenance, runtime skew detection) are not well-supported. Inspired by our experience in implementing features and optimizations over DISC systems, we present SEIZE, a unified framework that enables dataflow inspection wiretapping the data-path with listening logic -in MapReduce-style programming model. We generalize our lessons learned by providing a set of primitives defining dataflow inspection, orchestration options for different inspection granularities, and operator decomposition and dataflow puncutation strategy for dataflow intervention. We demonstrate the generality and flexibility of the approach by deploying SEIZE in both Apache Spark and Apache Flink. Our experiments show that, the overhead introduced by the inspection logic is most of the time negligible (less than 5% in Spark and 10% in Flink).

关键词： parallel Dataflow Runtime Inspection User Desired Moments Apache Spark Apache fink

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RIFFLE: A distributed Network Operating System with Re-configurability for Multiple Domains

RIFFLE: A Distributed Network Operating System with Re-confi...

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IEEE international Symposium on parallel and distributed Processing with Applications and IEEE international conference on Ubiquitous computing and Communications (ISPA/IUCC)

作者： Boyang Zhou Chunming Wu Shuangxi Chen Zhen Li Hong Fan Zhejiang Lab Intelligent Networks Institute Hangzhou China School of Intelligent Science and Technology Hangzhou Institute for Advanced Study UCAS Hangzhou China College of Computer Science and Technology Zhejiang University Hangzhou China Jiaxing Key Laboratory of Industrial Internet Jiaxing China Research Center for Intelligent Technology Standardization Zhejiang Lab Hangzhou China Department of Electrical Engineering Shanghai University of Electric Power Shanghai China

The distributed network control in software-defined networking is greatly challenged by the design demands of evolving a running distributed control plane (DCP), and by the complexity of handling the control state dynamics. In this paper, we propose RIFFLE to address the problem using a distributed network operating system approach. RIFFLE enables the evolvability of control states of DCP deployed on a global scale. RIFFLE handles spatial and temporal dynamics that occur when updating and migrating the distributed control states, which overcomes the critical barrier to the evolvability. It fills the gap by enabling temporal reconfigurability in DCP. Moreover, RIFFLE reduces the complexities of building and maintaining network control services in DCP by enabling componentization and abstracting the underlying network dynamics. We evaluated the prototype RIFFLE through experiments running in PlanetLab. The results show that the prototype scales well for 135 nodes. We also validate that RIFFLE ensures the continuity and low content request delay for the supported the information-centric network supported during cache update periods owing to the enabled evolvability.

关键词： Runtime Roads Decentralized control Information-centric networking Prototypes Ubiquitous computing Dynamic scheduling

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