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Design Automation Conference

作者： Zihan Jiang Fubing Mao Yapu Guo Xu Liu Haikun Liu Xiaofei Liao Hai Jin Wei Zhang National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Department of Electronic and Computer Engineering HKUST Hong Kong

Streaming graph processing needs to timely evaluate continuous queries. Prior systems suffer from massive redundant computations due to the irregular order of processing vertices influenced by updates. To address this issue, we propose ACGraph, a novel streaming graph processing approach for monotonic graph algorithms. It maintains dependence trees during runtime, and makes affected vertices processed in a top-to-bottom order in the hierarchy of the dependence trees, thus normalizing the state propagation order and coalescing of multiple propagation to the same vertices. Experimental results show that ACGraph reduces the number of updates by 50% on average, and achieves the speedup of 1.75~7.43× over state-of-the-art systems.

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Simulating the Spread of Infection in Networks with Quantum computers

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Physical Review Applied 2023年第6期19卷 064035-064035页

作者： Xiaoyang Wang Yinchenguang Lyu Changyu Yao Xiao Yuan Center on Frontiers of Computing Studies Peking University Beijing 100871 China Center of High Energy Physics Peking University Beijing 100871 China Department of Modern Physics University of Science and Technology of China Hefei 230026 China School of Computer Science Peking University Beijing 100871 China

We propose to use quantum computers to simulate infection spreading in networks. We first show the analogy between the infection distribution and spin-lattice configurations with Ising-type interactions. Then, since the spreading process can be modeled as a classical Markovian process, we show that the spreading process can be simulated using the evolution of a quantum thermal dynamic model with a parameterized Hamiltonian. In particular, we analytically and numerically analyze the evolution behavior of the Hamiltonian, and prove that the evolution simulates a classical Markovian process, which describes the well-known epidemiological stochastic susceptible and infectious (SI) model. A practical method to determine the parameters of the thermal dynamic Hamiltonian from epidemiological inputs is exhibited. As an example, we simulate the infection spreading process of the SARS-Cov-2 variant Omicron in a small-world network.

关键词： Medical physics Network diffusion Quantum simulation Stochastic processes Viral diseases Viruses Ising model Markovian processes

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APP: Enabling Soft Real-Time Execution on Densely-Populated Hybrid Memory System

APP: Enabling Soft Real-Time Execution on Densely-Populated ...

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Design Automation Conference

作者： Zheng-Wei Wu Yun-Chih Chen Yuan-Hao Chang Tei-Wei Kuo Department of Computer Science and Information Engineering National Taiwan University Institute of Information Science Academia Sinica Taiwan Department of Computer Science Mohamed bin Zayed University of Artificial Intelligence NTU High Performance and Scientific Computing Center National Taiwan University

Memory swapping was considered slow and evil, but swapping to Ultra Low-Latency storage like Optane has become a promising solution to save power and cost, helping densely-populated edge server to overcome its DRAM capacity bottleneck. However, the lack of integration between CPU scheduling and memory paging causes soft real-time tasks running on edge servers to miss deadlines under heavy memory multiplexing. We propose APP (Adaptive Page Pinning), lightweight protection of working set memory to ensure meeting soft real-time task deadlines without starving other non-real-time tasks. Experiments show that APP alleviates thrashing in memory-intensive tasks and upholds soft real-time task deadlines.

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LOPO: An Out-of-order Layer Pulling Orchestration Strategy for Fast Microservice Startup

LOPO: An Out-of-order Layer Pulling Orchestration Strategy f...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Lin Gu Junhao Huang Shaoxing Huang Deze Zeng Bo Li Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China School of Computer Science China University of Geosciences Wuhan China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong

Container based microservices have been widely applied to promote the cloud elasticity. The mainstream Docker containers are structured in layers, which are organized in stack with bottom-up dependency. To start a microservice, the required layers are pulled from a remote registry and stored on its host server, following the layer dependency order. This incurs long microservice startup time and hinders the performance efficiency. In this paper, we discover that, for the first time, the layer pulling order can be adjusted to accelerate the microservice startup. Specifically, we address the problem on microservice layer pulling orchestration for startup time minimization and prove it as NP-hard. We propose a Longest-chain based Out-of-order layer Pulling Orchestration (LOPO) strategy with low computational complexity and guaranteed approximation ratio. Through extensive real-world trace driven experiments, we verify the efficiency of our LOPO and demonstrate that it reduces the microservice startup time by 22.71% on average in comparison with state-of-the-art solutions.

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PSMiner: A Pattern-Aware Accelerator for High-Performance Streaming Graph Pattern Mining

PSMiner: A Pattern-Aware Accelerator for High-Performance St...

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Design Automation Conference

作者： Hao Qi Yu Zhang Ligang He Kang Luo Jun Huang Haoyu Lu Jin Zhao Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China Zhejiang Lab Zhejiang-HUST Joint Research Center for Graph Processing China Department of Computer Science University of Warwick United Kingdom

Streaming Graph Pattern Mining (GPM) has been widely used in many application fields. However, the existing streaming GPM solution suffers from many unnecessary explorations and isomorphism tests, while the existing static GPM ones require many repetitive operations to compute the full graph. In this paper, we propose a pattern-aware incremental execution approach and design the first streaming GPM accelerator called PSMiner, which integrates multiple optimizations to reduce redundant computation and improve computing efficiency. We have conducted extensive experiments. The results show that compared with the state-of-the-art software and hardware solutions, PSMiner achieves the average speedups of 770.9× and 60.4×, respectively.

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Condition Monitoring and Maintenance Management with Grid-Connected Renewable Energy Systems

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computers, Materials & Continua 2022年第8期72卷 3999-4017页

作者： Md.Mottahir Alam Ahteshamul Haque Mohammed Ali Khan Nebras M.Sobahi Ibrahim Mustafa Mehedi Asif Irshad Khan Department of Electrical and Computer Engineering(ECE) King Abdulaziz UniversityJeddah 21589Saudi Arabia Advance Power Electronics Research Lab Department of Electrical EngineeringJamia Millia IslamiaNew Delhi 110025India Department of Electrical Power Engineering Faculty of Electrical Engineering and CommunicationBrno University of TechnologyBrno 61600Czech Republic Center of Excellence in Intelligent Engineering Systems(CEIES) King Abdulaziz UniversityJeddah 21589Saudi Arabia Computer Science Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah 21589Saudi Arabia

The shift towards the renewable energy market for carbon-neutral power generation has encouraged different governments to come up with a plan of *** with the endorsement of renewable energy for harsh environmental conditions like sand dust and snow,monitoring and maintenance are a few of the prime *** problems were addressed widely in the literature,but most of the research has drawbacks due to long detection time,and high misclassification *** to overcome these drawbacks,and to develop an accurate monitoring approach,this paper is motivated toward the understanding of primary failure concerning a grid-connected photovoltaic(PV)system and highlighted along with a brief overview on existing fault detection *** on the drawback a data-driven machine learning approach has been used for the identification of fault and indicating the maintenance unit regarding the operation and maintenance ***,the system was tested with a 4 kWp grid-connected PV system,and a decision tree-based algorithm was developed for the identification of a *** results identified 94.7%training accuracy and 14000 observations/sec prediction speed for the trained classifier and improved the reliability of fault detection nature of the grid-connected PV operation.

关键词： Fault detection machine learning fault ride through

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Enhancing the reliability and accuracy of wireless sensor networks using a deep learning and blockchain approach with DV-HOP algorithm for DDoS mitigation and node localization

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EURASIP Journal on Wireless Communications and Networking 2025年第1期2025卷 1-39页

作者： Kaur, Bhupinder Prashar, Deepak Mrsic, Leo Almogren, Ahmad Rehman, Ateeq Ur Altameem, Ayman Hussen, Seada School of Computer Science Engineering Lovely Professional University Phagwara India Jadara University Research Center Jadara University Irbid Jordan Algebra University Zagreb Croatia Department of Computer Science College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia School of Computing Gachon University Seongnam-si Republic of Korea Department of Natural and Engineering Sciences College of Applied Studies and Community Services King Saud University Riyadh Saudi Arabia Department of Electrical Power Adama Science and Technology University Adama Ethiopia

Wireless sensor networks (WSNs) are subject to distributed denial-of-service (DDoS) attacks that impact data dependability, mobility of nodes, and energy drain. The remedy to these challenges in this work is a solution based on deep learning integrated with a blockchain-aided distance-vector hop (DV-HOP) localization algorithm for reliable and secure node localization. Incorporating a blockchain ledger makes the network more trustworthy as it verifies usual and unusual system activities, whereas the DV-HOP algorithm mitigates localization inaccuracies and enhances node placement. The system is evaluated according to different performance measures like localization error, accuracy ratio, average localization error (ALE), probability of location, false positive rate (FPR), false negative rate (FNR), energy utilization, network stability, node failure rate, node recovery rate, and malicious node detection rate. Experimental results reveal improved security, accuracy, and efficiency with 17% FPR and 15% FNR, outperforming the conventional methods. This model enhances WSN performance in different environments via precise data transmission from the source to the destination. The results confirm that integrating deep learning with blockchain and DV-HOP increases network robustness, thus making WSNs more secure against security attacks while reducing energy consumption and localization accuracy. The proposed model presents a strong solution for real-world applications in wireless network environments.

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Fuzzy-Based Weighted Federated Machine Learning Approach for Sustainable Energy Management with IoE Integration

Fuzzy-Based Weighted Federated Machine Learning Approach for...

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IEEE Symposium on Systems and Information Engineering Design, SIEDS

作者： Taher M. Ghazal Jamshaid Iqbal Janjua Sagheer Abbas Areej Fatima Muhammad Saleem Muhammad Adnan Khan Abdulrahman Alqarafi Computer Science Department Centre for Cyber Physical Systems Khalifa University Abu Dhabi UAE Center for Cyber Security Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Selangor Malaysia School of Computer Sciences National College of Business Administration & Economics Lahore Pakistan Al-Khawarizmi Institute of Computer Science University of Engineering & Technology (UET) Lahore Pakistan Department of Computer Science Bahria University Lahore Pakistan Department of Computer Science LGU Lahore Pakistan School of Computer Science Minhaj University Lahore Pakistan School of Computing Skyline University College Sharjah UAE Riphah School of Computing and Innovation Riphah International University Lahore Pakistan College of Computer Science and Engineering Taibah University Medina Saudi Arabia

ISBN: (数字)9798350385144

ISBN: (纸本)9798350385151

The considerable surge in energy consumption due to population growth and the use of new technology has posed significant challenges to energy security and the environment. Managing energy consumption on the consumer side is crucial as the demand for energy continues to increase. Consumers need to monitor their daily usage and understand consumption standards to prepare themselves for energy efficiency and cost savings. To address these challenges, a Smart Energy Management System (SEMS) based on the Internet of Energy (IoE) has been developed. This system uses day-ahead planning and accurate energy availability forecasting to manage energy demand in a smart environment with high sustainable energy penetration. The Fuzzy based Weighted Federated Learning (FWFML) approach is integrated into the system to optimize energy management. The SEMS system monitors energy usage at the user level, and a review of management plans has been introduced to improve energy efficiency in terms of 93.5% accuracy, and 6.5% miss-rate. This research work also highlights future challenges in developing SEMS to increase energy efficiency in a more robust manner.

关键词： Energy consumption Costs Smart buildings Sociology Energy efficiency Energy management systems Surges

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AgilePkgC: An Agile System Idle State Architecture for Energy Proportional Datacenter Servers 22

AgilePkgC: An Agile System Idle State Architecture for Energ...

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Proceedings of the 55th Annual IEEE/ACM International Symposium on Microarchitecture

作者： Georgia Antoniou Haris Volos Davide B. Bartolini Tom Rollet Yiannakis Sazeides Jawad Haj Yahya Computer Architecture Laboratory Department of Computer Science University of Cyprus Computing Systems Lab Huawei Zurich Research Center Rivos Inc. Mountain View California United States

ISBN: (纸本)9781665462723

Modern user-facing applications deployed in data-centers use a distributed system architecture that exacerbates the latency requirements of their constituent microservices (30--250μs). Existing CPU power-saving techniques degrade the performance of these applications due to the long transition latency (order of 100μs) to wake up from a deep CPU idle state (C-state). For this reason, server vendors recommend only enabling shallow core C-states (e.g., CC1) for idle CPU cores, thus preventing the system from entering deep package C-states (e.g., PC6) when all CPU cores are idle. This choice, however, impairs server energy proportionality since power-hungry resources (e.g., IOs, uncore, DRAM) remain active even when there is no active core to use them. As we show, it is common for all cores to be idle due to the low average utilization (e.g., 5 -- 20%) of datacenter servers running user-facing *** propose to reap this opportunity with AgilePkgC (APC), a new package C-state architecture that improves the energy proportionality of server processors running latency-critical applications. APC implements PC1A (package C1 agile), a new deep package C-state that a system can enter once all cores are in a shallow C-state (i.e., CC1) and has a nanosecond-scale transition latency. PC1A is based on four key techniques. First, a hardware-based agile power management unit (APMU) rapidly detects when all cores enter a shallow core C-state (CC1) and triggers the system-level power savings control flow. Second, an IO Standby Mode (IOSM) places IO interfaces (e.g., PCIe, DMI, UPI, DRAM) in shallow (nanosecond-scale transition latency) low-power modes. Third, a CLM Retention (CLMR) mode rapidly reduces the CLM (Cache-and-home-agent, Last-level-cache, and Mesh network-on-chip) domain's voltage to its retention level, drastically reducing its power consumption. Fourth, APC keeps all system PLLs active in PC1A to allow nanosecond-scale exit latency by avoiding PLL re-locking ove

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AlphaChimp: Tracking and Behavior Recognition of Chimpanzees

arXiv

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arXiv 2024年

作者： Ma, Xiaoxuan Lin, Yutang Xu, Yuan Kaufhold, Stephan P. Terwilliger, Jack Meza, Andres Zhu, Yixin Rossano, Federico Wang, Yizhou Center on Frontiers of Computing Studies School of Computer Science Peking University China Yuanpei College Peking University China Department of Cognitive Science University of California San Diego United States Institute for Artificial Intelligence Peking University China Nat'l Eng. Research Center of Visual Technology Nat'l Key Lab of General Artificial Intelligence Peking University China

Understanding non-human primate behavior is crucial for improving animal welfare, modeling social behavior, and gaining insights into both distinctly human and shared behaviors. Despite recent advances in computer vision, automated analysis of primate behavior remains challenging due to the complexity of their social interactions and the lack of specialized algorithms. Existing methods often struggle with the nuanced behaviors and frequent occlusions characteristic of primate social dynamics. This study aims to develop an effective method for automated detection, tracking, and recognition of chimpanzee behaviors in video footage. Here we show that our proposed method, AlphaChimp, an end-to-end approach that simultaneously detects chimpanzee positions and estimates behavior categories from videos, significantly outperforms existing methods in behavior recognition. AlphaChimp achieves approximately 10% higher tracking accuracy and a 20% improvement in behavior recognition compared to state-of-the-art methods, particularly excelling in the recognition of social behaviors. This superior performance stems from AlphaChimp's innovative architecture, which integrates temporal feature fusion with a Transformer-based self-attention mechanism, enabling more effective capture and interpretation of complex social interactions among chimpanzees. Our approach bridges the gap between computer vision and primatology, enhancing technical capabilities and deepening our understanding of primate communication and sociality. We release our code and models at project page and hope this will facilitate future research in animal social dynamics. This work contributes to ethology, cognitive science, and artificial intelligence, offering new perspectives on social intelligence. Copyright © 2024, The Authors. All rights reserved.

关键词： Mammals

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