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Efficient Microservice Deployment with Dependencies in Multi-Access Edge computing 29

Efficient Microservice Deployment with Dependencies in Multi...

引用

29th IEEE international conference on parallel and distributed Systems, ICPADS 2023

作者： Lu, Shuaibing Yan, Ran Wu, Jie Beijing University of Technology Faculty of Information Technology Beijing China Temple University Center for Networked Computing Philadelphia United States

ISBN: (纸本)9798350330717

In the context of mobile edge computing, efficiently deploying microservices to reduce finish time and enhance user service quality is a challenging task. However, existing research still has certain deficiencies in considering microservice deployment within edge server clusters and communication link constraints. To address this issue, we propose three microservice deployment strategies by offering flexibility and adaptability for various application scenarios. We initially consider two straightforward scenarios: one with unlimited storage resources under the bandwidth constraint, and the other with unlimited bandwidth resources under the storage constraint. For each of these two scenarios, we introduce a novel enhanced graph construction method and design two optimal solutions. For Scenario 3, which involves complex constraints on server capacity, computational capability, and communication resources, we present an optimization method based on main path partitioning and the simulated annealing algorithm. We effectively tackle challenges arising from server capacity, computational capability, and communication resource limitations. Across multiple experimental setups, our approach significantly improves microservice deployment efficiency and overall performance compared to traditional strategies. © 2023 IEEE.

关键词： dependency high-efficient microservice deployment multi-access edge computing

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An Efficient distributed Spatiotemporal Index for parallel Top-k Frequent Terms Query

An Efficient Distributed Spatiotemporal Index for Parallel T...

引用

IEEE international conference on Big Data and Smart computing (BigComp)

作者： Van Le, Hong Takasu, Atsuhiro Natl Inst Informat Tokyo Japan

ISBN: (纸本)9781665421973

As rich user-generated geotagged data, microblogs have been exploited in several data analytic contexts, e.g., popular topic trends, popular site detection, and geo-targeted recommendation. To support such analysis, we developed an efficient multidimensional index structure and parallel processing approaches for top-k frequent spatiotemporal terms query: a common analytic query on geotagged social data. Given a spatiotemporal range, the query aggregates the frequencies of terms among the social posts and identifies the most frequent terms in that range. The present work is different from studies that extract this information from stream data because we focus on large historical datasets. The key challenge is to improve query performance with minimum storage requirements. We propose a distributed index structure that transforms spatiotemporal coordinates into unique codes to generate rowkeys in key-value stores (KVSs) and balances the data distribution across distributed systems. Then, we utilize data localization to calculate sorted term lists (STLs) in parallel. To reduce input/output between KVSs and the client, we theoretically estimate the necessary length of STLs to calculate the top-k frequent terms and send only a part of the STLs to the client. Several experiments, on both real and artificial datasets, showed our approach to have both lower space requirements and better query performance than baseline approaches.

关键词： parallel processing top-k computing spatiotemporal database key-value store geotagged social data

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Privacy-preserving Pedestrian Tracking using distributed 3D LiDARs 21

Privacy-preserving Pedestrian Tracking using Distributed 3D ...

引用

21st IEEE international conference on Pervasive computing and Communications (PerCom)

作者： Ohno, Masakazu Ukyo, Riki Amano, Tatsuya Rizk, Hamada Yamaguchi, Hirozumi Osaka Univ Osaka Japan Tanta Univ Tanta Egypt

ISBN: (纸本)9781665453783

The growing demand for intelligent environments unleashes an extraordinary cycle of privacy-aware applications that makes individuals' life more comfortable and safe. Examples of these applications include pedestrian tracking systems in large areas. Although the ubiquity of camera-based systems, they are not a preferable solution due to the vulnerability of leaking the privacy of pedestrians. In this paper, we introduce a novel privacy-preserving system for pedestrian tracking in smart environments using multiple distributed LiDARs of non-overlapping views. The system is designed to leverage LiDAR devices to track pedestrians in partially covered areas due to practical constraints, e.g., occlusion or cost. Therefore, the system uses the point cloud captured by different LiDARs to extract discriminative features that are used to train a metric learning model for pedestrian matching purposes. To boost the system's robustness, we leverage a probabilistic approach to model and adapt the dynamic mobility patterns of individuals and thus connect their sub-trajectories. We deployed the system in a largescale testbed with 70 colorless LiDARs and conducted three different experiments. The evaluation result at the entrance hall confirms the system's ability to accurately track the pedestrians with a 0.98 F-measure even with zero-covered areas. This result highlights the promise of the proposed system as the next generation of privacy-preserving tracking means in smart environments.

关键词： Point cloud-based recognition LiDAR Privacy-preserving Re-ID Pedestrian tracking

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Harmonic Compensation Analysis of grid-Connected System Having Photovoltaic and Energy Storage Integrated with parallel Active Filter 8

Harmonic Compensation Analysis of Grid-Connected System Havi...

引用

8th international conference on Power and Renewable Energy, ICPRE 2023

作者： Chen, Shiwei Xia, Xuhua Jiang, Lujun Zhang, Yao Fuyang Rongda Complete-Set Elec. Equip. Mfg. Br. of Hangzhou Power Equipment Manufacturing Co. Ltd. Hangzhou City China Hangzhou Dianzi University School of Automation Hangzhou310018 China

ISBN: (纸本)9798350328813

The growing popularity of non-linear loads and distributed generation can create power quality problems in the grid system. In order to ensure stable system operation, the question of how to improve the power quality is unavoidable. In this paper, a PV energy storage integrated parallel active filter is analyzed to compensate for grid current harmonics in the presence of grid current contamination by nonlinear loads. The maximum power point tracking (MPPT) algorithm of PV and the method of coordinating with the power output of the storage battery are investigated. The integration of power quality improvement and clean energy is achieved. Finally, the system is simulated in MATLAB/SIMULINK to verify the feasibility of the system. © 2023 IEEE.

关键词： harmonic compensation MPPT parallel active filter PV

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Joint Optimization of Model Partitioning and Resource Allocation for Edge computing with Intermittently Operating Devices 29

Joint Optimization of Model Partitioning and Resource Alloca...

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29th IEEE international conference on parallel and distributed Systems, ICPADS 2023

作者： Wu, Qi Zhang, Yi Yang, Chenxi Sun, Jin Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing210094 China

ISBN: (纸本)9798350330717

The new intermittent computing paradigm allows for intermittent operation of energy-harvesting devices, posing new challenges to edge intelligence in delivering high-quality computing services. This paper aims at the joint optimization of model partitioning and resource allocation for reducing the latency of deep neural network (DNN) applications on an edge computing system with intermittently operating devices. We establish a rigorous optimization model for the joint optimization problem that takes into account the heterogeneity and intermittent operation of end devices. Tailed for the inference procedure of DNN applications, we develop a family of partitioning rules for decomposing the DNN structure to facilitate computation offloading. We propose a model partitioning and resource allocation algorithm to determine the optimized assignment of computing resources for the DNN tasks offloaded from multiple devices onto the edge server. The proposed algorithm first utilizes the partitioning rules to obtain a preliminary decision on model partitioning, and introduces a greedy-based strategy to determine the final decision on the partitioning points of DNN structures as well as the amount of computing resources assigned for task execution. Simulation results on an edge system with heterogeneous devices, including Raspberry Pi 3B+, Raspberry Pi 4B, and Jetson Xavier NX, demonstrating that the proposed algorithm outperforms baseline methods with shorter latency when executing DNN inference tasks. © 2023 IEEE.

关键词： computation offloading edge intelligence intermittent computing model partitioning resource allocation

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distributed Task Offloading Method Based on Federated Reinforcement Learning in Vehicular Networks with Incomplete Information 1

引用

23rd international conference on Algorithms and Architectures for parallel Processing (ICA3PP)

作者： Cao, Rui Song, Zhengchang Niu, Bingxin Gu, Junhua Li, Chunjie Hebei Univ Technol Sch Artificial Intelligence Tianjin Peoples R China Hebei Univ Technol Hebei Prov Key Lab Big Data Calculat Tianjin Peoples R China Hebei Expressway Grp Ltd Shijiazhuang Hebei Peoples R China

ISBN: (数字)9789819708598

ISBN: (纸本)9789819708581;9789819708598

With the development of Internet of Vehicles, a large number of different types of applications have emerged. However, given the limited computing power of vehicles, many tasks cannot be completed within specified time. The task offloading method based on mobile edge computing (MEC) effectively solves the problem. However, the existing research does not fully consider user needs and has the disadvantages of limited application scope and slow training speed, so it is not suitable for high-speed vehicle scenarios. Considering the heterogeneity of tasks and the different needs of users, this paper first designs a dynamic weighting method for delay and vehicle energy consumption, and then proposes a distributed algorithm FDQN based on Deep Q-Network (DQN) and federated learning. The algorithm does not require any information about the base station and can utilize user collaboration to achieve fast convergence. The experimental results show that our algorithm can achieve best optimization effect under different vehicle energy and speeds.

关键词： Task offloading Federated learning Vehicular network Deep reinforcement learning

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Traceable Ring Signature Scheme in Internet of Vehicles Based on Lattice Cryptography 21

Traceable Ring Signature Scheme in Internet of Vehicles Base...

引用

21st IEEE international Symposium on parallel and distributed Processing with Applications, 13th IEEE international conference on Big Data and Cloud computing, 16th IEEE international conference on Social computing and Networking and 13th international conference on Sustainable computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Liao, Wei Han, Lansheng Chen, Peng Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan China

ISBN: (纸本)9798350329223

Lattice cryptography, as a recognized Cryptosystem that can resist quantum computation, has great potential for development. Lattice based signature scheme is currently a research focus. In this paper, the traceable ring signature scheme based on lattice cryptography in the Internet of Vehicles (IoV) environment is studied. Firstly, users' public and private keys are generated on the lattice, which effectively reduces the key size and time cost. Secondly, the scheme also achieves traceability by adding additional information. The scheme satisfies strong unforgeability, traceability, and anonymity. In addition, the traceable ring signature scheme based on lattice cryptography is applied in the IoV environment to effectively protect user privacy. Model communication can not only protect user privacy, but also trace the signer when necessary, achieving a balance between security and privacy. The simulation results show that the average delay of IoV of our scheme is better than that of similar schemes. The research in this paper improves the security of data transmission in the IoV environment, enables it to adapt to the complex and changeable new network environment, and effectively guarantees the information security of vehicles and owners. © 2023 IEEE.

关键词： Cryptography

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Evaluation method of distributed photovoltaic maximum access capacity based on ADMM 2

Evaluation method of distributed photovoltaic maximum access...

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2023 2nd international conference on Energy and Electrical Power Systems, ICEEPS 2023

作者： Wu, Zihao Fan, Yujiao Li, Wei State Grid Shaanxi Electric Power Research Institute Shaanxi Xi'an710054 China College of Electrical Engineering Xi'An University of Technology Shaanxi Xi'an710048 China

With the increasing penetration rate of distributed photovoltaics in the distribution network, new requirements are put forward for the operation and control of distribution networks. In order to ensure that the distribution network can operate stably within a safe range, it is important to evaluate the maximum capacity of the distribution network that can be connected to distributed photovoltaics. Considering the high cost of centralized computing, high communication requirements, and poor reliability, it is difficult to adapt to the actual needs of the distribution network with large-scale access to distributed photovoltaics. Therefore, this paper proposes an ADMM-based distributed PV maximum access capacity evaluation method. This method takes the distributed photovoltaic access capacity and the active power loss of the distribution network line as the objective function and considers the power flow equation constraints, node voltage constraints, and line current carrying capacity constraints of the distribution network. By simplifying the model, the non-convex optimization problem is transformed into a convex quadratic programming problem, and the ADMM algorithm is used to solve the distributed solution to obtain the access strategy of distributed PV in various regions under different operating conditions. Finally, the IEEE-33 system is used as an example to simulate the calculation, and the experimental results verify the correctness and effectiveness of the method. © Published under licence by IOP Publishing Ltd.

关键词： Electric power distribution

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Reactive Power Optimal Allocation Method Based on Joint Optimization of BPA and SCCP and Economic Analysis 6

Reactive Power Optimal Allocation Method Based on Joint Opti...

引用

6th international conference on Energy, Electrical and Power Engineering, CEEPE 2023

作者： Wang, Zesen Li, Qi Li, Weiyu Kong, Shuaihao Zhao, Zhiyu Liang, Hao Electric Power Research Institute State Grid Jibei Electric Power Company Limited Beijing China

ISBN: (纸本)9798350348279

The renewable energy resources and loads in China are characterized by reverse division. The long-distance transmission of renewable energy power leads to the insufficient support capacity of high proportion of power electronic equipment for the grid voltage at the sending end, which further limits the transmission and consumption of renewable energy. This research adopts the technical idea of joint optimal configuration of distributed condensers and parallel capacitors to improve the voltage support capability of the sending end power grid. Aiming at improving the transmission limit of renewable energy, this research proposes a reactive power optimal configuration method based on the joint optimization of BPA-SCCP, and establishes an economic analysis model to evaluate the technical economy of reactive power configuration scheme. Finally, a large-scale renewable energy and thermal power transmission system through AC/DC lines in the north is taken as an example to verify the effectiveness of the optimal allocation method. The research results show that the reactive power optimal allocation method based on the joint optimization of BPA-SCCP can effectively solve the problem of reactive power optimal allocation in the renewable energy gathering area, and the reactive power optimal allocation scheme based on the joint optimization of distributed condensers and parallel capacitors has good economy. © 2023 IEEE.

关键词： Economic analysis

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AshPipe: Asynchronous Hybrid Pipeline parallel for DNN Training 24

AshPipe: Asynchronous Hybrid Pipeline Parallel for DNN Train...

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7th international conference on High Performance computing in Asia-Pacific Region (HPC Asia)

作者： Hosoki, Ryubu Endo, Toshio Hirofuchi, Takahiro Ikegami, Tsutomu Tokyo Inst Technol Yokohama Kanagawa Japan Natl Inst Adv Ind Sci & Technol Tokyo Japan

ISBN: (纸本)9798400708893

Deep Neural Networks (DNNs) have become increasingly computationally intensive and have larger parameters, requiring efficient parallelization or distribution using multiple accelerators. Pipeline parallelism has been proposed as an effective way to distribute models and improve hardware utilization. However, the problem with pipeline parallelism is the trade-off between speedup and accuracy: synchronous approaches do not provide sufficient speedup, while asynchronous approaches suffer from accuracy degradation due to a different scheme from a single worker. In this paper, we propose AshPipe, a hybrid parallel framework that combines data parallelism and asynchronous pipeline parallelism to achieve efficient speedup for training. The proposed runtime uses the 1F1B schedule and data parallelism, with non-uniform numbers of workers and identical global batch sizes across stages. A Switch parallelism (SP) mechanism is also proposed as an option to mitigate accuracy degradation, which switches over from data parallelism to hybrid parallelism in the course of training. Experimental results show that AshPipe achieves 1.844x the throughput of data parallelism for ViT-H/14 whose parameter size is 632M. With the SP mechanism, AshPipe achieved a 30.2% reduction in training time with comparable accuracy compared to data parallelism when training on the CIFAR100 dataset.

关键词： distributed Deep Learning Data parallelism Pipeline parallelism Hybrid parallelism

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