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检索条件"任意字段=IFIP International Conference on Network and Parallel Computing"

共 17279 条记录，以下是291-300 订阅

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The Blind and the Elephant: A Preference-aware Edge Video Analytics Scheduler for Maximizing System Benefit 24

The Blind and the Elephant: A Preference-aware Edge Video An...

引用

53rd international conference on parallel Processing (ICPP)

作者： Zhang, Liang Zhu, Hongzi Li, Yunzhe Shen, Jiangang Guo, Minyi Shanghai Jiao Tong Univ Shanghai Peoples R China

ISBN: (纸本)9798400717932

Video analytics is the killer workload in edge computing, which involves the scheduler's complex decisions to balance analysis performance (latency and accuracy) and resource consumption (network, computation, and energy). Traditional schedulers address this as a single-objective optimization problem with fixed weights, unable to precisely capture unknown system preferences due to intricate pricing rules across various service levels and resource costs, consequently leading to suboptimal system benefit like monetary gain. In this paper, we propose a Bayesian optimization-driven multi-objective scheduler, PaMO, that can proactively explore the system pricing preference by pairwise comparing outcome vectors of all objectives. Moreover, PaMO designs a heuristic scheduling algorithm with a zero-delay jitter guarantee to avoid performance degradation caused by resource contention and uses a revised Bayesian optimization algorithm to make video configuration and scheduling decisions. Experiments on real video analytics workloads show that PaMO can achieve up to 53.9% benefit gain compared to state-of-the-art scheduling methods.

关键词： Adaptive configuration Video analytics Edge computing Periodic scheduling Bayesian optimization Multi-objective optimization

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Scheduling Machine Learning Compressible Inference Tasks with Limited Energy Budget 24

Scheduling Machine Learning Compressible Inference Tasks wit...

引用

53rd international conference on parallel Processing (ICPP)

作者： Barros, Tiago da Silva Ferre, Davide Giroire, Frederic Aparicio-Pardo, Ramon Perennes, Stephane Univ Cote Azur INRIA I3S Sophia Antipolis France Univ Cote Azur CNRS I3S INRIA Sophia Antipolis France Univ Cote Azur CNRS I3S Sophia Antipolis France

ISBN: (纸本)9798400717932

Advancements in cloud computing have boosted Machine Learning as a Service (MLaaS), highlighting the challenge of scheduling tasks under latency and deadline constraints. Neural network compression offers the latency and energy consumption reduction in data centers, aligning with efforts to minimize cloud computing's carbon footprint, despite some accuracy loss. This paper investigates the Deadline Scheduling with Compressible Tasks - Energy Aware (DSCT-EA) problem, which addresses the scheduling of compressible machine learning tasks on several machines, with different speeds and energy efficiencies, under an energy budget constraint. Solving DSCT-EA involves determining both the machine on which each task will be processed and its processing time, a problem that has been proven to be NP-Hard. We formulate DSCT-EA as a Mixed-Integer Programming (MIP) problem and also provide an approximation algorithm for solving it. The efficacy of our approach is demonstrated through extensive experimentation, revealing its superiority over traditional scheduling techniques. It allows to save up to 70% of the energy budget of image classification tasks, while only losing 2% of accuracy compared to when not using compression.

关键词： scheduling energy budget neural network compression deadlines

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Analog Compute in Memory and Breaking Digital Number Representations 30

Analog Compute in Memory and Breaking Digital Number Represe...

引用

30th ifip/IEEE international conference on Very Large Scale Integration (VLSI-SoC)

作者： Laubeuf, Nathan Interuniv Microelect Ctr IMEC Leuven Belgium

ISBN: (数字)9781665490054

ISBN: (纸本)9781665490054

Analog in Memory computing (AiMC) is a promising solution to achieve energy efficient deployment of Deep Neural networks (DNNs). However, to this day, the accuracies of DNNs deployed using AIMC do not compare favorably with digital alternatives. In this work, we illustrate that these lacking performance can be partly attributed to mismatched arithmetic assumption. We show that correcting those assumptions results in more robust and accurate DNNs on analog platforms. By further leveraging insights from AiMC related methods, we question modern neural network quantization techniques, revealing, in the process, alternative multiplication-less modes of executions for DNNs.

关键词： Deep learning Quantization (signal) Neural networks Very large scale integration Solids In-memory computing Hardware

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Implementing network Transformation Based Algorithms for a Special Case of Scheduling Jobs with Periodic Due Dates on parallel Machines 13

Implementing Network Transformation Based Algorithms for a S...

引用

Joint 13th international conference on Soft computing and Intelligent Systems and 25th international Symposium on Advanced Intelligent Systems, SCIS and ISIS 2024

作者： Toyoda, Kazusa Karuno, Yoshiyuki Graduate School of Science and Technology Kyoto Institute of Technology Kyoto Japan Kyoto Institute of Technology Faculty of Mechanical Engineering Kyoto Japan

ISBN: (纸本)9798350373332

In this paper, we revisit a scheduling problem of just-in-time jobs with periodic due dates on parallel identical machines with multiple service slots, and address a novel imple-mentation issue of a network transformation based exact algorithm for a polynomially solvable case. Due to the periodic just-in-time constraint and a restriction on service slot occupation, the number of service slots required for processing each job can be viewed as a virtual processing time of the job. The special case of polynomially solvable contains two kinds of jobs such that one kind of a job requires exactly one service slot for the processing, and the other kind of a job requires two consecutive service slots, and further the number of the second kind of jobs is assumed to be a fixed integer. The objective is to minimize the total charge, which is a variant of the total weighted completion time. Any instance of the special case of the scheduling problem is known to be equivalently transformed to a series of instances of the minimum cost flow problem, and an exact algorithm for the minimum cost flow problem can find an optimal solution for the special case of the scheduling problem in polynomial time. In this paper, we implement the network transformation based exact algorithm, and attempt to estimate the service slot, in which each job of the second kind is completed in an optimal solution, for reducing the execution time of the network transformation based exact algorithm. We also demonstrate the quality of the estimation of service slot empirically, and report the numerical results. © 2024 IEEE.

关键词： Scheduling algorithms

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Analysis of Time Synchronization Challenges in Digital Twins for Edge-Enabled Data Centers in Smart Cities Scenario 33

Analysis of Time Synchronization Challenges in Digital Twins...

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33rd Euromicro international conference on parallel, Distributed, and network-Based Processing, PDP 2025

作者： Rinaldi, Stefano Iacono, Salvatore Dello Bellagente, Paolo Pasetti, Marco University of Brescia Department of Information Engineering Brescia Italy

ISBN: (纸本)9798331524937

The rapid adoption of Internet of Things (IoT) technologies and digital twins (DTs) is revolutionizing smart cities by facilitating improved real-time monitoring, simulation, and optimization of urban infrastructures. In data centers, digital twins offer virtual representations of physical infrastructure, enabling real-time monitoring, optimization of energy consumption, cooling efficacy, and resource utilization. This integration seeks to improve energy efficiency and sustainability in smart cities. In this context, edge computing is becoming essential solutions to mitigate latency, bandwidth, and privacy issues linked to centralized cloud system. A significant challenge in utilizing digital twins for edge-enabled data centers is attaining accurate time synchronization between the digital twin and its physical equivalent. Precise synchronization is crucial for real-time state representation, facilitating efficient monitoring, control, and optimization. The distributed characteristics of edge devices, varying network conditions, and constrained computational resources present considerable synchronization challenges. Factors such as clock drift, network jitter, and protocol constraints can result in temporal inconsistencies, compromising the reliability of digital twin applications. This paper offers an extensive examination of time synchronization issues in digital twins for edge-enabled data centers within smart city contexts. The study examines essential factors affecting synchronization accuracy and reliability, identifies significant bottlenecks, and suggests strategies to alleviate their impact. The results aid in the creation of effective synchronization mechanisms, facilitating the implementation of dependable digital twin systems. © 2025 IEEE.

关键词： Mobile edge computing

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Energy-Aware Smart Task Scheduling in Edge computing networks with A3C 1

引用

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

作者： Wang, Dan Liu, Liang Ge, Binbin Qi, Junjie Zhao, Zehui Zhongguancun Lab Beijing Peoples R China Beijing Univ Posts & Telecommun Beijing Peoples R China

ISBN: (数字)9789819708345

ISBN: (纸本)9789819708338;9789819708345

With the rapid development of the Internet of Things, a huge amount of data is generated from mobile users. Mobile Edge computing (MEC) extends cloud computing capabilities to the edge of the network which enables real-time and low-latency services for mobile users. However, how to intelligently schedule tasks to save energy consumption in an edge computing environment remains a challenge. In this paper, we formulate a dynamic offloading optimization problem to find out how to minimize system energy consumption based on a two-layer heterogeneous edge cloud cellular architecture. In addition, we model the problem as a Markov Decision Process (MDP) and employ the asynchronous advantage actor-critic (A3C) algorithm to propose a suitable scheduling strategy. The simulation results verify the effectiveness of our proposed method.

关键词： task scheduling Asynchronous Advantage Actor-critic (A3C) Mobile Edge computing (MEC)

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23rd international conference on Algorithms and Architectures for parallel Processing, ICA3PP 2023

23rd International Conference on Algorithms and Architecture...

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23rd international conference on Algorithms and Architectures for parallel Processing, ICA3PP 2023

ISBN: (纸本)9789819708000

The proceedings contain 165 papers. The special focus in this conference is on Algorithms and Architectures for parallel Processing. The topics include: MDCF: Multiple Dynamic Cuckoo Filters for LSM-Tree;a Game Theory Based Task Offloading Scheme for Maximizing Social Welfare in Edge computing;research on Dos Attack Simulation and Detection in Low-Orbit Satellite network;malware Detection Method Based on Visualization;TOC: Joint Task Offloading and Computation Reuse in Vehicular Edge computing;Distributed Task Offloading for IoAV Using DDP-DQN;a Task Offloading and Resource Allocation Optimization Method in End-Edge-Cloud Orchestrated computing;multi-agent Cooperative Intrusion Detection Based on Generative Data Augmentation;an Uncertainty-Aware Auction Mechanism for Federated Learning;Long Short-Term Deterministic Policy Gradient for Joint Optimization of Computational Offloading and Resource Allocation in MEC;query Optimization Mechanism for Blockchain-Based Efficient Data Traceability;research on the Evolution Path of network Hotspot Events Based on the Event Evolutionary Graph;Task Offloading in UAV-Assisted Vehicular Edge computing networks;path Planning of Coastal Ships Based on Improved Hybrid A-Star;data Augmentation Method Based on Partial Noise Diffusion Strategy for One-Class Defect Detection Task;K Asynchronous Federated Learning with Cosine Similarity Based Aggregation on Non-IID Data;MPQUIC Transmission Control Strategy for SDN-Based Satellite network;an Energy Prediction Method for Energy Harvesting Wireless Sensor with Dynamically Adjusting Weight Factor;Bayesian Optimization for Auto-tuning Convolution Neural network on GPU;persistent Sketch: A Memory-Efficient and Robust Algorithm for Finding Top-k Persistent Flows;faCa: Fast Aware and Competition-Avoided Balancing for Data Center network;Optimizing GNN Inference Processing on Very Long Vector Processor;GDTM: Gaussian Differential Trust Mechanism for Optimal Recommender System;privacy-Enhanced Dyna

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DRL-Based Optimization Algorithm for Wireless Powered IoT network 1

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

作者： Zhu, Mingjie Zhang, Shubin Chi, Kaikai Zhejiang Univ Technol Hangzhou Peoples R China

ISBN: (数字)9789819708086

ISBN: (纸本)9789819708079;9789819708086

The extensive applications of Internet of Things (IoT) bring development and prosperity to multiple industries and greatly influent all aspects of human lives, which rely on the timely computation and communication. However, the limited battery and computing capacity of the common IoT units cannot satisfy the need of processing computation intensive and time sensitive tasks. As a result, the combination of wireless power transmission (WPT) and mobile edge computing (MEC) provides a promising approach of dealing with aforementioned problems. The Hybrid Access Point (HAP) transmits radio frequency (RF) energy to provide power transmission for wireless devices (WDs), and processes tasks offloaded from WDs with the equipped edge computing servers (ECSs). In this paper, we consider a wireless powered mobile edge computing network containing an HAP, multiple WDs. By jointly optimizing energy transmission time duration, partial offloading decisions and transmission power allocation strategy, we obtain the maximum of the sum computation rate (SCR). Firstly, we formulate this as a non-convex problem which is hard to address. Secondly, we decompose the problem into a top-problem of optimizing the energy transmission time duration and a sub-problem of optimizing the partial offloading decisions and transmission power allocation strategy. Finally, we design a DRL-based offloading algorithm, which applies a DNN network with exploring and updating strategy, to address the top-problem and propose an effective optimizing algorithm to address the sub-problem. Extensive numerical results reveal that the proposed algorithm reaches near-optimal SCR and greatly reduces time latency and complexity compared to benchmark algorithms.

关键词： Mobile-edge computing wireless power transfer reinforcement learning partial offloading resource allocation

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23rd international conference on Algorithms and Architectures for parallel Processing, ICA3PP 2023

23rd International Conference on Algorithms and Architecture...

引用

23rd international conference on Algorithms and Architectures for parallel Processing, ICA3PP 2023

ISBN: (纸本)9789819708079