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检索条件"任意字段=24th IEEE International Conference on Distributed Computing Systems"

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When the Edge Meets Transformers: distributed Inference with Transformer Models 44

When the Edge Meets Transformers: Distributed Inference with...

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

作者： Hu, Chenghao Li, Baochun Univ Toronto Dept Elect & Comp Engn Toronto ON Canada

ISBN: (纸本)9798350386066;9798350386059

Transformer models achieved significant breakthroughs in a wide variety of applications, yet their exorbitant computation costs pose significant challenges when it comes to deploying these models for inference, especially on resource-constrained edge devices. In this paper, we introduce the concept of cross-device distributed inference to transformer models, which accelerates the speed of inference by distributing its workload among multiple edge devices. Unlike previous work designed for multi-GPU environments, the challenge of distributing inference workload on edge devices includes not only limited computation power, but also low bandwidth connections to exchange intermediate results. To address these challenges, we propose Voltage, a distributed inference system tailored for edge devices. By exploiting the inherent parallelizability of the input sequence, Voltage partitions the transformer inference workload based on positions to accelerate the inference speed. We also analyze the relationship between the partition settings and the computation complexity, which allows Voltage to adaptively select the most efficient computation scheme. To demonstrate its effectiveness and generalizability, the performance of Voltage has been evaluated in the context of well-known transformer models, and in a variety of experimental settings. Our results show that Voltage significantly outperforms tensor parallelism by reducing the communication size by 4x, thereby accelerate the inference speed by up to 32.2% compared with single device deployment.

关键词： distributed system distributed inference transformer models

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Enhancing Simulation Efficiency in Semiconductor Optical Amplifier (SOA) Models: A Comparative Study of First and Second Order Approaches 24

Enhancing Simulation Efficiency in Semiconductor Optical Amp...

引用

24th ieee international conference on Communication Technology, ICCT 2024

作者： Qi, Yingchuan Zhao, Wenbo Dong, Jincheng Wang, Shuo Ma, Shuanger Zhang, Qian Zhang, Lu Shandong Zhike Intelligence Computing Inc. Jinan China Shandong University Jinan China Zhejiang University Hangzhou China

ISBN: (纸本)9798350363760

this study evaluates the numerical performance of first and second-order nonlinear dynamic semiconductor optical amplifier (SOA) models, including the distributed traveling-wave (TW) and lumped models. We find that while the first-order method is sufficient for the lumped SOA model, the second-order method significantly improves simulation efficiency for the TW-SOA model. these insights enhance the optimization of SOA applications in various advanced optical systems. © 2024 ieee.

关键词： Optical transmitters

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AdapCC: Making Collective Communication in distributed Machine Learning Adaptive 44

AdapCC: Making Collective Communication in Distributed Machi...

引用

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

作者： Zhao, Xiaoyang Zhang, Zhe Wu, Chuan Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China

ISBN: (纸本)9798350386066;9798350386059

As deep learning (DL) models continue to grow in size, there is a pressing need for distributed model learning using a large number of devices (e.g., GPUs) and servers. Collective communication among devices/servers (for gradient synchronization, intermediate data exchange, etc.) introduces significant overheads, rendering major performance bottlenecks in distributed learning. A number of communication libraries, such as NCCL, Gloo and MPI, have been developed to optimize collective communication. Predefined communication strategies (e.g., ring or tree) are largely adopted, which may not be efficient or adaptive enough for inter-machine communication, especially in cloud-based scenarios where instance configurations and network performance can vary substantially. We propose AdapCC, a novel communication library that dynamically adapts to resource heterogeneity and network variability for optimized communication and training performance. AdapCC generates communication strategies based on run-time profiling, mitigates resource waste in waiting for computation stragglers, and executes efficient data transfers among DL workers. Experimental results under various settings demonstrate 2x communication speed-up and 31% training throughput improvement with AdapCC, as compared to NCCL and other representative communication backends.

关键词： distributed training collective communication

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Large-Scale Causal Data Replication for Stateful Edge Applications 44

Large-Scale Causal Data Replication for Stateful Edge Applic...

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

作者： Fouto, Pedro Preguica, Nuno Leitao, Joao Univ Lisbon NOVA LINCS Lisbon Portugal Univ Lisbon NOVA Lisbon Portugal

ISBN: (纸本)9798350386066;9798350386059

Edge computing is becoming an increasingly popular paradigm, with modern Internet services leveraging hundreds of edge locations to serve their users. However, existing data replication solutions are not designed to operate in this environment, which restricts the edge components of Internet services to operate as read-only caches and entry points for accessing data centers, severely limiting the benefits extracted from the edge. this paper presents Arboreal, a novel distributed data management system for cloud and edge infrastructures that enables stateful edge applications to be deployed with full (read and write) local access to application data, overcoming the limitations of existing solutions. Arboreal's data replication protocol allows it to automatically and dynamically replicate data across edge locations according to application needs, while providing global causal+ consistency. By relying on a hierarchical topology, Arboreal scales to hundreds of edge locations, while recovering from failures in a decentralized and localized manner, without compromising consistency or durability guarantees. Evaluation shows that the scalability of Arboreal heavily outperforms state-of-the-art solutions, while the dynamic replication mechanism allows to effectively support a wide variety of edge scenarios including mobile clients.

关键词： edge computing causal consistency data replication distributed storage

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Real-time Model Predictive Control for Techno-Economical Battery Management in distributed Power Grids Using Day-Ahead Optimization 24

Real-time Model Predictive Control for Techno-Economical Bat...

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24th EEEIC international conference on Environment and Electrical Engineering and 8th I and CPS Industrial and Commercial Power systems Europe, EEEIC/I and CPS Europe 2024

作者： Shojaei, Fatemeh Vahidnia, Arash Amani, Ali Moradi School of Engineering Rmit University Electrical and Electronic Engineering Melbourne Australia

ISBN: (纸本)9798350355185

this study presents a comprehensive approach to enhancing the efficiency and reliability of distributed energy resources (DER) within distributed networks through the integration of battery and photovoltaic systems. Leveraging real-world data, our method focuses on predictive optimization, technical considerations, and economic aspects for a holistic solution. By utilizing the state of charge (SoC) of batteries as the foundational model, our system employs day-ahead optimal data for Model Predictive Control (MPC). the MPC dynamically tracks optimal references, ensuring precise, real-time, and adaptive control strategies. the suggested method has been applied to the ieee 33-BUS distributed network, showcasing the efficacy of this controller through two distinct scenarios. the approach prioritizes predictive optimization, technical considerations, economic factors, and voltage analysis across all buses, delivering a comprehensive solution. © 2024 ieee.

关键词： State of charge

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Multilayer Magnetic Domain Wall MTJ-based Spiking Neural Network 24

Multilayer Magnetic Domain Wall MTJ-based Spiking Neural Net...

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ieee 24th international conference on Nanotechnology (NANO)

作者： Lone, Aijaz H. Rahimi, Daniel N. Fariborzi, Hossein Setti, Gianluca King Abdullah Univ Sci & Technol Dept Elect & Comp Engn Thuwal Saudi Arabia

ISBN: (纸本)9798350386257;9798350386240

Spintronic devices, especially the magnetic tunnel junction and magnetic domain wall-based devices, hold significant promise for applications in energy-efficient data storage and Unconventional computing architectures. We present a novel multilayer spintronic neuromorphic device based on spin-orbit torque-driven domain wall dynamics. the typical leaky integrate and fire LIF neuron-like characteristics are realized using the combination of SOT and demagnetization energy effects. the device characteristics are modelled as the modified LIF model. We test the spiking neuron model for the classification of the MNIST dataset by emulating a 3-layer spiking neural network SNN-based on the DW-MTJ LIF neuron model. the network achieves classification accuracy above 96% thus the proposed device can be integrated with the CMOS for energy efficient neuromorphic computing.

关键词： Spiking neural networks (SNN) Leaky-integrate and fire neurons Domain wall devices Magnetic tunnel junction Spintronics Neuromorphic computing

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Evasion attacks on ML in domains with nonlinear constraints 24

Evasion attacks on ML in domains with nonlinear constraints

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24th ieee/ACM international Symposium on Cluster, Cloud, and Internet computing (CCGrid)

作者： Krzyszton, Mateusz Bok, Bartosz Zakieta, Pawel Kolodziej, Joanna Res & Acad Comp Network NASK PIB Dept Distributed Syst Warsaw Poland Cracow Univ Technol Dept Comp Sci Krakow Poland

ISBN: (纸本)9798350377521;9798350377514

Cybersecurity efforts aim to protect computer systems, especially complex systems with dynamically changing architectures like mobile computational clouds, from digital attacks. the use of machine learning (ML) models in these systems to analyze and process data, allocate resources or monitor and manage infrastructure is increasingly the field of hackers and the target of sophisticated and increasingly difficult-to-detect attacks. Adversarial machine learning (AML) is a class of techniques and methods that attempt to "fool" classical ML algorithms by using, among others, deceptive data or modifying the parameters of cyber-physical systems (CPS). this paper presents a new generic evasion attack model for generating targeted attacks on the ML binary classifiers in domains with nonlinear constraints. We used GD (Gradient Descent) and Adam (Adaptive Moment Estimation) optimization algorithms as the most important internal components of two variants of our attack model. Such a model was then evaluated in an experimental analysis. the achieved results show that the new generic evasion attack model effectively generates targeted attacks on ML binary classifiers, utilizing GD and Adam optimization algorithms. this model was evaluated through experimental analysis, demonstrating its potential in cybersecurity contexts. through this approach, the research contributes significantly to the field of adversarial machine learning by presenting a novel method that challenges the robustness of ML models under specific nonlinear constraints.

关键词： nonlinear constraints machine learning evasion attacks optimization

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EdgStr: Automating Client-Cloud to Client-Edge-Cloud Transformation 44

EdgStr: Automating Client-Cloud to Client-Edge-Cloud Transfo...

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

作者： An, Kijin Tilevich, Eli Samsung Res Software Engn Team Seoul South Korea Virginia Tech Software Innovat Lab Dept Comp Sci Blacksburg VA USA

ISBN: (纸本)9798350386066;9798350386059

To harness the potential of edge resources, two-tier client-cloud applications require transformation into three-tier client-edge-cloud applications. Such transformations are hard for programmers to perform correctly by hand. Many cloud services maintain a runtime state that needs to be replicated at the edge. Once replicated, this state must then be synchronized efficiently and correctly. To facilitate the transition to edge computing, we present a framework that automatically transforms client-cloud apps to their client-edge-cloud versions. Our framework, EdgStr, automatically replicates cloud-based services at the edge. EdgStr synchronizes the replicated service state by relying on a third-party Conflict-Free Replicated Data Type (CRDT). It generates code that connects service state changes to CRDT update operations, thus ensuring that the state changes at each replica eventually converge to the same replicated state. As an evaluation, we applied EdgStr to transform representative distributed mobile apps for deployment in dissimilar network and device setups. EdgStr correctly replicates cloud services (targeting the important domain of ***), deploying the resulting replicas on an ad-hoc edge cluster, hosted by Raspberry PI devices. As long as eventual consistency is congruent with the functionality of a cloud service, EdgStr can automatically replicate this service and deploy the replicas at the edge, thus offering the performance benefits of edge-based execution, without the high costs of manual program transformation.

关键词： Edge computing Program Analysis & Transformation Replicated State

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distributed and Efficient Request Scheduling in Collaborative Edge computing 44

Distributed and Efficient Request Scheduling in Collaborativ...

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

作者： Liang, Yuzhu Huang, Fengyi Mei, Yaxin Zhang, Guangxue Zeng, Jiandian Wang, Tian Beijing Normal Univ Adv Inst Nat Sci Zhuhai Peoples R China BNU HKBU United Int Coll Fac Sci & Technol Zhuhai Peoples R China

ISBN: (纸本)9798350386066;9798350386059

Cloud computing typically involves transferring users' requests to centralized cloud servers, a process that is inherently fraught with substantial delays due to the unpredictable nature of network transmissions. this inherent latency issue presents considerable challenges to applications that are highly sensitive to delay. We propose leveraging edge collaboration to minimize latency by enabling efficient user request scheduling within geographical proximities. However, cross-regional edge collaboration faces challenges due to the lack of real-time resource knowledge across regions, a problem we have identified as NP-hard. To address this, we introduce a model to connect edge nodes globally, thereby accurately reflecting their resource status. By employing an enhanced Dijkstra algorithm, we optimize the request routing process, achieving a notable reduction in delays compared to baseline methods, thus enhancing performance across various test scenarios.

关键词： distributed network request scheduling collaborative edge computing

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Adaptive User-Centric Entanglement Routing in Quantum Data Networks 44

Adaptive User-Centric Entanglement Routing in Quantum Data N...

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

作者： Wang, Lei Bi, Jieming Xu, Jie Univ Miami Dept Elect & Comp Engn Coral Gables FL 33146 USA

ISBN: (纸本)9798350386066;9798350386059

distributed quantum computing (DQC) holds immense promise in harnessing the potential of quantum computing by interconnecting multiple small quantum computers (QCs) through a quantum data network (QDN). Establishing long-distance quantum entanglement between two QCs for quantum teleportation within the QDN is a critical aspect, and it involves entanglement routing - finding a route between QCs and efficiently allocating qubits along that route. Existing approaches have mainly focused on optimizing entanglement performance for current entanglement connection (EC) requests. However, they often overlook the user's perspective, wherein the user making EC requests operates under a budget constraint over an extended period. Furthermore, both QDN resources (quantum channels and qubits) and the EC requests, reflecting the DQC workload, vary over time. In this paper, we present a novel user-centric entanglement routing problem that spans an extended period to maximize the entanglement success rate while adhering to the user's budget constraint. To address this challenge, we leverage the Lyapunov drift-plus-penalty framework to decompose the long-term optimization problem into per-slot problems, allowing us to find solutions using only the current system information. Subsequently, we develop efficient algorithms based on continuous-relaxation and Gibbs-sampling techniques to solve the per-slot entanglement routing problem. theoretical performance guarantees are provided for both the per-slot and long-term problems. Extensive simulations demonstrate that our algorithm significantly outperforms baseline approaches in terms of entanglement success rate and budget adherence.

关键词： Quantum Networks distributed Quantum computing Entanglement Routing

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