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检索条件"任意字段=14th International Conference on Parallel and Distributed Computing Systems"

共 8133 条记录，以下是41-50 订阅

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A Deep Q-Learning Framework for Enhanced QoE and Energy Optimization in Fog computing 20

A Deep Q-Learning Framework for Enhanced QoE and Energy Opti...

引用

20th Annual international conference on distributed computing in Smart Sensor systems and the Internet of things (DCOSS-IoT)

作者： Sumona, Sabiha Tasnim Hasan, Syed Sabbir Tamzid, Abu Yousuf Roy, Palash Razzaque, Md Abdur Mahmud, Redowan Green Univ Bangladesh Dept Comp Sci & Engn Dhaka Bangladesh Univ Dhaka Dept Comp Sci & Engn Green Networking Res Grp Dhaka Bangladesh Curtin Univ Sch Elect Engn Comp & Math Sci Perth WA Australia

ISBN: (纸本)9798350369458;9798350369441

Fog computing, an evolution of cloud computing, has become increasingly popular for its ability to lessen the burden of such a centralized computing paradigm by distributing tasks generated by IoT across fog layers. Effectively managing real-time, delay-sensitive, and diverse IoT applications to enhance the Quality-of-Experience (QoE) presents significant challenges due to the dispersed nature and limited resources of fog nodes. Previous studies in fog computing task offloading have typically focused on either energy consumption or service delay. this paper introduces an optimization framework for task offloading within fog computing environments that aims to balance improved user QoE with reduced energy consumption, employing Mixed-Integer Linear Programming (MILP). Given the NP-hard nature of this framework, we have devised a Deep Q-Learning (DQL) based model for task offloading, termed ELTO-DQL, which aims for near-optimal solutions in polynomial time. Experimental results indicate that the ELTO-DQL model enhances energy efficiency and QoE by up to 19% and 15% respectively, outperforming contemporary benchmarks.

关键词： Fog computing Task offloading energy consumption Deep Q-Learning Quality-of-Experience

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Tangram: High-resolution Video Analytics on Serverless Platform with SLO-aware Batching 44

Tangram: High-resolution Video Analytics on Serverless Platf...

引用

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

作者： Peng, Haosong Zhan, Yufeng Li, Peng Xia, Yuanqing Beijing Inst Technol Sch Automat Beijing Peoples R China Univ Aizu Sch Comp Sci & Engn Aizu Wakamatsu Fukushima Japan

ISBN: (纸本)9798350386066;9798350386059

Cloud-edge collaborative computing paradigm is a promising solution to high-resolution video analytics systems. the key lies in reducing redundant data and managing fluctuating inference workloads effectively. Previous work has focused on extracting regions of interest (RoIs) from videos and transmitting them to the cloud for processing. However, a naive Infrastructure as a Service (IaaS) resource configuration falls short in handling highly fluctuating workloads, leading to violations of Service Level Objectives (SLOs) and inefficient resource utilization. Besides, these methods neglect the potential benefits of RoIs batching to leverage parallel processing. In this work, we introduce Tangram, an efficient serverless cloud-edge video analytics system fully optimized for both communication and computation. Tangram adaptively aligns the RoIs into patches and transmits them to the scheduler in the cloud. the system employs a unique "stitching" method to batch the patches with various sizes from the edge cameras. Additionally, we develop an online SLO-aware batching algorithm that judiciously determines the optimal invoking time of the serverless function. Experiments on our prototype reveal that Tangram can reduce bandwidth consumption and computation cost up to 74.30% and 66.35%, respectively, while maintaining SLO violations within 5% and the accuracy loss negligible.

关键词： video analytics batching inference serverless computing

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A Comprehensive Review of Islanding Detection Methods for Both AC and DC Microgrids 14

A Comprehensive Review of Islanding Detection Methods for Bo...

引用

14th international conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2023

作者： Kumar, Prabhat Peram, Anusha Kumari, Pooja Kumar, Deepak Kumar, Niranjan Electrical Engineering Department NIT Jamshedpur Jharkhand India

Modernizing grid connectivity laws is necessary for the electric power systems’ rapid proliferation of distributed generators (DGs), especially as microgrids (MGs) proliferate and speed up the decentralization of networks. therefore, the rapid detection of unintentional islanding is much more necessary either for DGs operating in grid-connected mode or for MGs which have reliability for getting transition into Islanded mode. An overview of approaches for detecting and preventing islands in power systems is provided in this work. the two main categories for detecting islanding conditions for a distributed generation (DG) distribution system are classical and modern methods. For utility-side islanding detection, a modern technique is used, but for DG-side islanding detection, a classical technique is used. there are three other categories of classical techniques: hybrid, active, and passive. these methods for DG islanding detection are described and examined. © Grenze Scientific Society, 2023.

关键词： distributed power generation

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Accelerating distributed ML Training via Selective Synchronization 25

Accelerating Distributed ML Training via Selective Synchroni...

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25th IEEE international conference on Cluster computing (CLUSTER)

作者： Tyagi, Sahil Swany, Martin Indiana Univ Bloomington Bloomington IN 47405 USA

ISBN: (纸本)9798350307924

In distributed training, deep neural networks (DNNs) are launched over multiple workers concurrently and aggregate their local updates on each step in bulk-synchronous parallel (BSP) training. However, BSP does not linearly scale-out due to high communication cost of aggregation. To mitigate this overhead, alternatives like Federated Averaging (FedAvg) and Stale-Synchronous parallel (SSP) either reduce synchronization frequency or eliminate it altogether, usually at the cost of lower final accuracy. In this paper, we present SelSync, a practical, low-overhead method for DNN training that dynamically chooses to incur or avoid communication at each step either by calling the aggregation op or applying local updates based on their significance. We propose various optimizations as part of SelSync to improve convergence in the context of semi-synchronous training. Our system converges to the same or better accuracy than BSP while reducing training time by up to 14x.

关键词： Cluster computing distributed systems Data-parallel training Federated learning Deep learning

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Modular Platform For Collaborative, distributed Sensor Fusion 14

Modular Platform For Collaborative, Distributed Sensor Fusio...

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14th ACM/IEEE international conference on Cyber-Physical systems (ICCPS) part of CPS-IoT Week

作者： Spencer, R. Zelter, Nate Hunt, David Angell, Kristen Pajic, Miroslav Duke Univ Durham NC 27706 USA

ISBN: (纸本)9798400700361

Leading autonomous vehicle (AV) platforms and testing infrastructures are, unfortunately, proprietary and closed-source. thus, it is difficult to evaluate how well safety-critical AVs perform and how safe they truly are. Similarly, few platforms exist for much-needed multi-agent analysis. To provide a starting point for analysis of sensor fusion and collaborative & distributed sensing, we design an accessible, modular sensing platform with AVstack [9]. We build collaborative and distributed camera-radar fusion algorithms and demonstrate an evaluation ecosystem of AV datasets, physics-based simulators, and hardware in the physical world. this three-part ecosystem enables testing next-generation configurations that are prohibitively challenging in existing development platforms.

关键词： Ecosystems

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Compute Continuum: What Lies Ahead?

Compute Continuum: What Lies Ahead?

引用

29th international conference on parallel and distributed computing (Euro-Par)

作者： Nardelli, Matteo Russo, Gabriele Russo Cardellini, Valeria Bank Italy Rome Italy Univ Roma Tor Vergata Rome Italy

ISBN: (纸本)9783031506833;9783031506840

Modern computing environments are evolving towards the compute continuum paradigm, which promises to manage the heterogeneity and dynamism of geographically spread computing resources, supporting the execution of distributed and pervasive applications. As a complete understanding of all the implications and challenges posed by this new paradigm is still far, we give an overview of the key features and concepts of the compute continuum and discuss the most relevant research opportunities we envision in the field.

关键词： Compute continuum IoT-edge-cloud distributed systems

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DICFaaS: parallel and Scalable Data Integrity Computation for Serverless systems 30

DICFaaS: Parallel and Scalable Data Integrity Computation fo...

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30th IEEE international conference on parallel and distributed systems, ICPADS 2024

作者： Xiong, Tianchen Peng, Bo Shanghai Jiao Tong University School of Software Shanghai China Shanghai Key Laboratory of Scalable Computing and Systems Shanghai China

ISBN: (纸本)9798331515966

Serverless computing is extensively employed in distributed storage computing scenarios owing to its dynamic responsiveness and on-demand allocation capabilities. Within distributed storage computing, data integrity computation holds significant importance. Nevertheless, current serverless systems fail to consider the CPU-intensive nature of data integrity computation, leading to prolonged CPU occupancy, increased latency, and substantial performance deterioration, particularly in parallel scenarios. therefore, there is an imperative need for a solution that can efficiently handle this kind of computation in serverless systems. In this paper, we introduce DICFaaS, a high-performance serverless system specialized for parallel and scalable data integrity computation. DICFaaS can recognize the offloading potential of servers and automatically offload CPU-intensive data integrity computation. the offloading function of DICFaaS can be easily integrated into other existing serverless systems. We evaluate the effectiveness of DICFaaS for data integrity computation compared to state-of-the-art serverless systems. For example, DICFaaS delivers up to 17 × latency improvement over AWS Lambda and up to 42 × latency improvement over OpenFaaS when the data size is 200M. It also saves up to 6 0% CPU overhead at runtime compared to OpenFaaS. © 2024 IEEE.

关键词： Data Integrity Computation Lossless Hardware Offloading Serverless systems Storage Computation

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Understanding the IO Performance Gap between OS-level and VM-level Containers in High-Density Deployment 44

Understanding the IO Performance Gap between OS-level and VM...

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

作者： Li, Wentai Zhou, Kaijun Shi, Jiacheng Chen, Xingman Liu, Yadong Wang, Luyuan Gu, Jinyu Shanghai Jiao Tong Univ Inst Parallel & Distributed Syst IPADS Shanghai Peoples R China Huawei Technol Co Ltd Shenzhen Peoples R China Beijing Inst Spacecraft Syst Beijing Peoples R China

ISBN: (纸本)9798350386066;9798350386059

Containers are widely deployed in clouds. there are two common container architectures: operating system-level (OS-level) container and virtual machine-level (VM-level) container. Typical examples are runc and Kata. It is well known that VM-level containers provide better isolation than OS-level containers, but at a higher overhead. Although there are quantitative analyses of the performance gap between these two container architectures, they rarely discuss the performance gap under the constrained resources provisioned to containers. Since the high-density deployment of containers is demanding in the cloud, each container is provisioned with limited resources specified by the cgroup mechanism. In this paper, we provide an in-depth analysis of the storage and network (two key aspects) performance differences between runc and Kata under varying resource constraints. We identify configuration implications that are crucial to performance and find that some of them are not exposed by the Kata interfaces. Based on that, we propose a profiling tool to automatically offer configuration suggestions for optimizing container performance. Our evaluation shows that the auto-generated configuration can improve the performance of MySQL by up to 107% in the TPCC benchmark compared with the default Kata setup.

关键词： Container Virtual Machine Performance Analysis Storage Network

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Enabling High-Performance EOV Blockchains via Transaction Ordering Exploration 44

Enabling High-Performance EOV Blockchains via Transaction Or...

引用

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

作者： Yu, Mei Zhao, Yihan Wang, Jianrong Hu, Dengcheng Liu, Xiulong Li, Qi Li, Keqiu Tianjin Univ Coll Intelligence & Comp Tianjin Peoples R China

ISBN: (纸本)9798350386066;9798350386059

An innovative architecture called execute-order-validate (EOV) has been proposed by Hyperledger Fabric that enables concurrent processing of transactions. However, the architecture suffers from issues such as excessive invalid transactions and serialization limitations in scenarios with high transaction conflicts, which restrict its applicability in real-time and high-performance settings. To address the aforementioned limitations, we propose ParFabric to enhance the EOV architecture. Firstly, we analyze four essential characteristics required for the transaction reordering algorithm within this architecture. We propose a heuristic dynamic reordering algorithm to reduce the number of invalid transactions. this is achieved through real-time identification and early abortion of transactions based on weighted pre-ordering and the construction of a transaction conflict graph. Secondly, leveraging the transaction conflict graph, we introduce a novel optimal block packing strategy based on transaction dependencies. this strategy replaces the total transaction order with partial order, enabling parallel validation and commit at the block level, thereby leading to increased system throughput while reducing transaction latency. Experimental results indicate that, ParFabric demonstrates excellent performance in terms of vertical scaling of peers. Additionally, at the same infrastructure cost, ParFabric provides 2.2x and 1.6x higher throughput than FabricPlusPlus and FabricSharp in high-conflict scenarios.

关键词： Permissioned Blockchain Concurrency Control Transaction Conflict distributed Architecture

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A Shapley value-based distributed AC OPF Approach for Redispatch Congestion Cost Allocation 23

A Shapley value-based Distributed AC OPF Approach for Redisp...

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14th ACM international conference on Future Energy systems (e-Energy)

作者： Bauer, Rebecca Dai, Xinliang Hagenmeyer, Veit Karlsruhe Inst Technol Inst Automat & Appl Informat Karlsruhe Germany

ISBN: (纸本)9798400700323

the progressing energy transition induces a growing need for redispatch congestion management, and, thereby, a fair distribution of its respective costs among the different system operators. In this light, a very recent paper uses the Shapley value as such a fair allocation rule to assign redispatch congestion costs to system operators. However, this approach is based on DC optimal power flow (OPF) and requires the sharing of detailed grid models from all system operators. this is not preferred by them due to data privacy concerns. W.r.t. real-world implementation, the present paper extends the method by using AC OPF problem formulations for more realistic results, and solving them by using a distributed optimization algorithm, i.e., Augmented Lagrangian based Alternating Direction Inexact Newton method (aladin), for preserving data privacy. Simulation results of an illustrative example show great potential of the proposed distributed approach in the aspects of both solution accuracy and computing time. this makes the presented approach generically feasible for real applications in the energy transition.

关键词： Shapley value optimal power flow game theory ALADIN distributed optimization algorithm

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