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检索条件"任意字段=25th IEEE International Conference on Distributed Computing Systems"

共 12792 条记录，以下是151-160 订阅

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Token Economy in the Mobility Ecosystem 25

Token Economy in the Mobility Ecosystem

引用

ieee 25th international conference on Intelligent Transportation systems (ITSC)

作者： Al-Dabbas, Khaled Kaltenhaeuser, Bernd Kornmayer, Harald Bogenberger, Klaus Baden Wuerttemberg Cooperat State Univ Dept Tech Management Villingen Schwenningen Germany Baden Wuerttemberg Cooperat State Univ Dept Informat Technol Mannheim Germany Tech Univ Munich Chair Traff Engn & Control Munich Germany

ISBN: (数字)9781665468800

ISBN: (纸本)9781665468800

Information technology is seen as the key to provide solutions for current and future challenges in the transportation and mobility ecosystem. Of those, the distributed Ledger Technology (DLT) is considered as an emerging paradigm to establish a token economy ecosystem without trusted third parties. Hereby, the mobility and transportation infrastructure resources and their usage are regarded as assets, which are transformed into tradabe tokens, managed via distributed ledgers. the access to the infrastructure is restricted and guaranteed to the token owners. the design and implementation of distributed ledgers depends on the requirements of the respective use cases, leading to a DLT-based IT infrastructure consisting of many different distributed ledger implementations. We derive a generic architecture for the future mobility token economy by adapting and applying a DLT design decision process to the mobility ecosystem, including six potential use cases.

关键词： distributed ledger Ecosystems Transportation Information technology

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Prediction of Cloud API Performance Using Uncertainty-Based Fusion of Predictive and Analytical Modeling 25

Prediction of Cloud API Performance Using Uncertainty-Based ...

引用

25th ieee international conferences on High Performance computing and Communications, 9th international conference on Data Science and systems, 21st ieee international conference on Smart City and 9th ieee international conference on Dependability in Sensor, Cloud and Big Data systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Hewavitharana, Jayani Jihan, Nadheesh Jayasinghe, Malith Gunawardana, Kasun Fernando, Duneesha University of Colombo School of Computing Colombo Sri Lanka Ltd. Colombo Sri Lanka

ISBN: (纸本)9798350330014

Performance prediction for cloud applications is important due to multiple reasons, including ensuring service level agreements, cost optimization, and resource provisioning. However, accurately predicting performance is challenging on account of different characteristics of performance data, such as noise, which can occur due to errors in measurements or specific behaviors of cloud applications, and imbalanced data (i.e., regions of missing data), which can occur due to various access patterns of users. Existing prediction methods using analytical, machine learning, as well as hybrid approaches assume uniformly distributed performance data, and these methods have high uncertainty and low accuracy when interpolating and generalizing into areas that do not contain reference data. In this paper, we introduce two novel approaches, Uncertainty-based Sub-modeling, and Uncertainty-based Iterative Sampling, as methods of combining analytical and machine learning approaches based on the uncertainty of predictions (i.e., predictive uncertainty). We focus on improving the predictions in under-represented regions by utilizing the analytical model based on an inference threshold as well as by incorporating data generated using analytical models for machine learning model training. Our approaches outperform their baseline models in 8 and 12 out of 15 cases with respect to RMSE and MAE respectively. Further analysis reveals that our approaches excel in addressing imbalanced data scenarios within performance modeling, outperforming previously proposed machine learning and hybrid models. © 2023 ieee.

关键词： Analytical models

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Workload Prediction for Edge computing 24

Workload Prediction for Edge Computing

引用

25th international conference on distributed computing and Networking, ICDCN 2024

作者： Shah, Kavish Gandhi, Ayush Gandhi, Kalgi Bhise, Minal Distributed Database Group Gujarat Gandhinagar India

ISBN: (纸本)9798400716737

In large-scale edge systems, along with the number of edge devices, the amount of data that needs to be processed has been increasing. Edge systems have limited processing power, memory, and network bandwidth. For such systems, handling resource utilization and data management during query processing is tricky. Moreover, joins being the costliest operations, when accelerated, can improve query processing substantially. the state-of-the-art technique for accelerating hash join, Column Imprint-Hash Join, is a data-aware approach which uses equi-height binning. Proposed Predicted Workload-Aware Column Imprint-Hash Join enhances the query performance and addresses real-time query processing by predicting the upcoming workload in advance. the algorithm is demonstrated on the TPC-H benchmark dataset. It shows an improvement of about 10% for the Percentage of Cachelines Read over CI-HJ, making the algorithm energy-efficient. An overall gain in Query Execution Time of 11% is observed. Although, Algorithm Execution Time shows an overhead of 6% as compared to the state-of-the-art which is due to an additional prediction phase. the proposed technique showcases the effectiveness of query prediction in optimizing edge system performance. © 2024 ACM.

关键词： Forecasting

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Generalized Collective Algorithms for the Exascale Era 25

Generalized Collective Algorithms for the Exascale Era

引用

25th ieee international conference on Cluster computing (CLUSTER)

作者： Wilkins, Michael Wang, Hanming Liu, Peizhi Pham, Bangyen Guo, Yanfei thakur, Rajeev Dinda, Peter Hardavellas, Nikos Northwestern Univ Evanston IL 60201 USA Argonne Natl Lab 9700 S Cass Ave Argonne IL 60439 USA

ISBN: (纸本)9798350307924

Exascale supercomputers have renewed the exigence of improving distributed communication, specifically MPI collectives. Previous works accelerated collectives for specific scenarios by changing the radix of the collective algorithms. However, these approaches fail to explore the interplay between modern hardware features, such as multi-port networks, and software features, such as message size. In this paper, we present a novel approach that uses system-agnostic, generalized (i.e., variable-radix) algorithms to capture relevant features and provide broad speedups for upcoming exascale-class supercomputers. We identify hardware commonalities found on announced exascale systems and three omnipresent communication kernels (binomial tree, ring, and recursive doubling) that can be generalized to better leverage these features, creating 10 total implementations. For each kernel, we develop analytical models to intuit algorithm performance with varying radix values. Experiments on the world's first exascale supercomputer (Frontier at ORNL) and a pre-exascale system (Polaris at ANL) show that our generalized algorithms outperform the baseline open-source and proprietary vendor MPI implementations by a significant margin, up to over 4.5x. We empirically determine optimal algorithms and parameter values, identifying where the analytical models are accurate and where hardware features directly determine performance. Most notably, we show how a single, system-agnostic implementation of a generalized algorithm can optimize for multiple hardware/software features across multiple systems.

关键词： Exascale computing collective communication MPI

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Enhancing AI-Generated Content Efficiency through Adaptive Multi-Edge Collaboration 44

Enhancing AI-Generated Content Efficiency through Adaptive M...

引用

44th ieee international conference on distributed computing systems (ICDCS)

作者： Xu, Changfu Guo, Jianxiong Zeng, Jiandian Meng, Shengguang Chu, Xiaowen Cao, Jiannong Wang, Tian BNU HKBU United Int Coll Guangdong Prov Key Lab IRADS Zhuhai Peoples R China Hong Kong Baptist Univ Hong Kong Peoples R China Guangdong Donghua Faster Software Co Ltd Zhuhai Peoples R China Hong Kong Univ Sci & Technol Guangzhou Guangzhou Peoples R China Hong Kong Polytech Univ Hong Kong Peoples R China Beijing Normal Univ Inst Artificial Intelligence & Future Networks Zhuhai Peoples R China

ISBN: (纸本)9798350386066;9798350386059

the Artificial Intelligence-Generated Content (AIGC) technique has gained significant popularity in creating diverse content. However, the current deployment of AIGC services in a centralized framework leads to high response times. To address this issue, we propose the integration of collaborative Mobile Edge computing (MEC) technology to decrease the processing delay of AIGC services. Nevertheless, existing collaborative MEC methods only facilitate collaborative processing among fixed Edge Servers (ESs), limiting flexibility and resource utilization across heterogeneous ESs for different computing and networking requirements associated with AIGC tasks. this poses challenges for efficient resource allocation. We present an adaptive multi-server collaborative MEC approach tailored for heterogeneous edge environments to achieve efficient AIGC by dynamically allocating task workload across multiple ESs. We formulate our problem as an online linear programming problem aiming to minimize task offloading make-span. this problem is proved to be NP-hard and we propose an online adaptive multi-server selection and allocation algorithm based on deep reinforcement learning that effectively addresses this problem. Additionally, we provide theoretical performance analysis, demonstrating that our algorithm achieves near-optimal solutions within approximate linear time complexity bounds. Finally, experimental results validate the effectiveness of our method by showcasing at least 11.04% reduction in task offloading make-span and a 44.86% decrease in failure rate compared to state-of-the-art methods.

关键词： Collaborative edge computing Adaptive multi-server offloading Workload allocation AIGC Deep Q-Networks

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Cooperative On-Ramp Merging Control of Connected and Automated Vehicles: distributed Multi-Agent Deep Reinforcement Learning Approach 25

Cooperative On-Ramp Merging Control of Connected and Automat...

引用

ieee 25th international conference on Intelligent Transportation systems (ITSC)

作者： Zhou, Shanxing Zhuang, Weichao Yin, Guodong Liu, Haoji Qiu, Chunlong Southeast Univ Sch Mech Engn Nanjing 211189 Jiangsu Peoples R China

ISBN: (数字)9781665468800

ISBN: (纸本)9781665468800

this paper proposes a cooperative merging control strategy of connected and automated vehicles (CAVs) using distributed multi-agent Deep Deterministic Policy Gradient (MADDPG). First, the on-ramp merging scenario and vehicle model are built, considering the safe merging distances and acceleration limits. Second, the MADDPG is adopted to learn the cooperative control strategy considering the rear-end safety, lateral safety, and vehicle energy consumption. A distributed architecture is proposed to improve training efficiency. Finally, several on-ramp merging scenarios are simulated. Simulation results show that the distributed MADDPG merging strategy reduces energy consumption by 7.4% and travel time by 5.3% compared to the regular merging strategy.

关键词： Connected and Automated Vehicles (CAVs) On-Ramp Merging distributed Architecture Multi-Agent Deep Reinforcement Learning

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A Low BER Cooperative-adaptive-equalizer for Serial Receiver in HPC Networks 25

A Low BER Cooperative-adaptive-equalizer for Serial Receiver...

引用

作者： Mingche, Lai Fangxu, Lv Geng, Zhang Chaolong, Xu School of Computer National University of Defense Technology State Key Laboratory of High Performance Computing Changsha410073 China

ISBN: (纸本)9798350330014

With the evolution of high-performance computer and data center interconnect communication rates from 53.125Gbps to 106.25Gbps, high-speed serial interfaces face a dramatic increase in BER challenges, which seriously affects interconnect performance and system stability. To address the challenge of high BER of 106.25Gbps PAM4 receiver, this paper proposes a cooperative adaptive equalizer structure, which adopts three equalizers and adaptive cooperative equalization algorithm to achieve low BER under large insertion loss conditions. In addition, a blind adaptive equalization algorithm based on judgment feedback equalizer is proposed to shorten the link training time. In this paper, a 12nm CMOS process is used to design a receiver based on cooperative adaptive equalizer. Simulation shows that the receiver with cooperative adaptive equalizer can support BER less than e-12 for a de-emphasized l06.25Gbps PAM4 signal after 34.3dB @ 26.5625G Hz channel, and the convergence period is less then 0.2 us. © 2023 ieee.

关键词： System stability

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Developing a Performance Criterion to Assess Dating Error in the Personified distributed Information-Measuring System 25

Developing a Performance Criterion to Assess Dating Error in...

引用

25th international conference on Soft computing and Measurements, SCM 2022

作者： Romantsova, Natalia V. Tsareva, Anna V. Tyarkin, Yaroslav A. Saint-Petersburg Electrotechnical University 'Leti' Information-Measuring Systems and Technology Department Saint Petersburg Russia

In the paper authors present the development of a performance criterion to assess error when making indirect measurements in the personified distributed information-measuring system with autonomous measuring modules a... 详细信息

ISBN: (纸本)9781665496698

关键词： Errors

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Safety Analysis of Rail Transit Redundant Structure in Cloud computing Environment Based on Graph and Bayesian theory 25

Safety Analysis of Rail Transit Redundant Structure in Cloud...

引用

ieee 25th international conference on Intelligent Transportation systems (ITSC)

作者： Kang, Yumeng Bu, Bing Gao, Bing Beijing Jiaotong Univ State Key Lab Rail Traff Control & Safety Beijing 100044 Peoples R China

ISBN: (数字)9781665468800

ISBN: (纸本)9781665468800

the migration of train control systems to cloud computing is an important trend of urban rail transit. A traditional redundant structure (T-RS) is a safety related vital part of the train control system. In this paper, the safety of the redundant structure in cloud computing environment (CCRS) is researched. Firstly, a deployment scheme of the CCRS is presented, the types of failures, especially those related to cloud computing, are analyzed. then, a model is setup based on graph theory which takes the physical nodes, virtual machines and fault-tolerant mechanisms of cloud computing into consideration. After that, the safety of the redundant structure is analyzed using a minimal spanning tree method and Bayesian algorithm. Lastly, some optimization measures on the deployment of the redundant structure are proposed and verified through numerical analysis, according to which the redundant structure in cloud computing environment can be safer than the traditional redundant structure.

关键词： Rails Cloud computing Fault tolerance Numerical analysis Computational modeling Fault tolerant systems Graph theory

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FLEDGY: Federated Learning for Edge Devices Accommodating GPU Diversity 48

FLEDGY: Federated Learning for Edge Devices Accommodating GP...

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48th Annual ieee international Computers, Software, and Applications conference (COMPSAC) - Digital Development for a Better Future

作者： Prabono, Aria Ghora Goh, Yang Fan Kobayashi, Mei EAGLYS Inc Tokyo Japan

ISBN: (纸本)9798350376975;9798350376968

We propose a federated learning framework designed to effectively utilize diverse GPU architectures in edge devices. It offers wider compatibility with various GPU platforms than conventional approaches which tend to focus on CUDA-based systems for enhancing the feasibility and cost-efficiency of machine learning in edge computing scenarios. Our framework is structured for straightforward deployment, addressing the complexities of initial federated learning project setups. We also built tensor manipulation library from scratch as the core of this framework. this contribution extends the reach of federated learning, enabling more flexible and inclusive approaches in distributed machine learning environments.

关键词： federated learning machine learning edge computing

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