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About the special issue on: "distributed Complex systems: Governance, engineering, and Maintenance"

JOURNAL OF software-EVOLUTION AND PROCESS 2022年第10期34卷

作者： Braione, Pietro Briola, Daniela De Angelis, Guglielmo Gallo, Francesco Poggi, Francesco Quattrocchi, Giovanni Univ Milano Bicocca Milan Italy IASI CNR Rome Italy Univ LAquila Laquila Italy CNR ICST Rome Italy Politecn Milan Milan Italy

The volume at hand presents the Special Issue on "distributed Complex systems: Governance, engineering, and Maintenance". The Special Issue has been originally conceived within the context of the 2(nd) international Workshop on Governing Adaptive and Unplanned systems of systems (GAUSS 2020), one of the co-located events of the 31(st) international symposium on software Reliability engineering (ISSRE 2020). The authors of the best papers at GAUSS 2020 have been invited to submit an extended version of their previous work. In addition, the editors opened the submission also to all the other researches working on technical and managerial solution for governing distributed Complex systems. Ultimate objective of the editors with this Special Issue is to promote discussions focusing on anticipating, mitigating, or reacting to scenarios that were unplanned or under-specified at design time.

关键词： autonomous reaction dynamic integration dynamic evolution systems of systems unplanned scenarios

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A software-defined Tensor Streaming Multiprocessor for Large-scale Machine Learning 22

A Software-defined Tensor Streaming Multiprocessor for Large...

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49th IEEE/ACM Annual international symposium on Computer Architecture (ISCA)

作者： Abts, Dennis Kimmell, Garrin Ling, Andrew Kim, John Boyd, Matt Bitar, Andrew Parmar, Sahil Ahmed, Ibrahim DiCecco, Roberto Han, David Thompson, John Bye, Michael Hwang, Jennifer Fowers, Jeremy Lillian, Peter Murthy, Ashwin Mehtabuddin, Elyas Tekur, Chetan Sohmers, Thomas Kang, Kris Maresh, Stephen Ross, Jonathan Groq Inc Mountain View CA 94041 USA Groq Inc KAIST Mountain View CA 94041 USA

ISBN: (纸本)9781450386104

We describe our novel commercial software-defined approach for large-scale interconnection networks of tensor streaming processing (TSP) elements. The system architecture includes packaging, routing, and flow control of the interconnection network of TSPs. We describe the communication and synchronization primitives of a bandwidth-rich substrate for global communication. This scalable communication fabric provides the backbone for large-scale systems based on a software-defined Dragonfly topology, ultimately yielding a parallel machine learning system with elasticity to support a variety of workloads, both training and inference. We extend the TSP's producer-consumer stream programming model to include global memory which is implemented as logically shared, but physically distributed SRAM on-chip memory. Each TSP contributes 220 MiBytes to the global memory capacity, with the maximum capacity limited only by the network's scale - the maximum number of endpoints in the system. The TSP acts as both a processing element (endpoint) and network switch for moving tensors across the communication links. We describe a novel software-controlled networking approach that avoids the latency variation introduced by dynamic contention for network links. We describe the topology, routing and flow control to characterize the performance of the network that serves as the fabric for a large-scale parallel machine learning system with up to 10,440 TSPs and more than 2 TeraBytes of global memory accessible in less than 3 microseconds of end-to-end system latency.

关键词： Machine Learning Tensor Streaming Processor Dragonfly software Scheduling

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Coverage Probability of distributed CoMP UAV-Assisted Cellular Networks

Coverage Probability of Distributed CoMP UAV-Assisted Cellul...

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international symposium on parallel and distributed Processing with Applications, ISPA

作者： Yan Li Qingmin Long Qiang Tang Shiming He Bing Xiong School of Computer and Communication Engineering Changsha University of Science and Technology Changsha China Science and Technology on Information System Engineering Laboratory National University of Defense Technology Changsha China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

It is well-established that terrestrial communication systems may fail during emergencies such as earthquakes, tsunamis, and floods. Fortunately, with the rapid advancement of unmanned aerial vehicle (UAV) network technology, deploying UAV nodes as aerial base stations (BSs) is assuming an increasingly crucial role in facilitating downlink transmissions and restoring ground communication capabilities. However, a single UAV node is not sufficient to meet the requirements. Inspired by distributed communication, we introduce a performance analysis framework based on stochastic geometry to analyze the distributed coordinated multi-point (CoMP) UAV-assisted communication network. Specifically, we assume that all UAV nodes follow a homogeneous Poisson point process (PPP) and maintain a constant altitude. The entire space is tessellated by multiple hexagons, with multiple UAV nodes within each hexagonal region working together to serve terrestrial user equipments (UEs). For this region-centric cooperative model, we derive an exact expression for the coverage probability to quantify the performance improvement enabled by UAVs, analyze the upper bound of the coverage probability, and provide a simplified approximation. We then compare this model to a user-centric model. Our numerical findings demonstrate that the cooperation of UAV nodes can significantly enhance the coverage probability and save spectrum resources.

关键词： Analytical models Upper bound Spectral efficiency Earthquakes Stochastic processes Probability Autonomous aerial vehicles Tsunami Terrestrial communications Numerical models

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Adversarial Attack against Intrusion Detectors in Cyber-Physical systems With Minimal Perturbations

Adversarial Attack against Intrusion Detectors in Cyber-Phys...

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international symposium on parallel and distributed Processing with Applications, ISPA

作者： Mingqiang Bai Puzhuo Liu Fei Lv Dongliang Fang Shichao Lv Weidong Zhang Limin Sun Beijing Key Laboratory of IOT Information Security Technology Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Ant Group Hangzhou China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Cyber-Physical systems (CPS) are crucial for critical infrastructure sectors such as electricity, water, and transportation. Machine Learning (ML) and Deep Learning (DL)-based Intrusion Detection systems (IDS) are widely used in CPS for security monitoring. Attack and defense confrontation is an eternal topic, leading to increased research on adversarial attacks against IDS. However, most existing research on CPS adversarial attacks focuses on improving evasion capabilities without ensuring the preservation of malicious attack functionality. To address this problem, we propose a Conditional Wasserstein GAN (CWGAN) based framework to generate adversarial examples that can not only evade IDS detection but also impose constraints on the specified target sensors to preserve the original attack functionality. Evaluation results demonstrate that our approach can effectively preserve the intended malicious functionality by significantly reducing the perturbations to specific target sensors. Specifically, we achieve an average reduction of 96.93% and 90.59%, and a maximum reduction of 93.26% and 95.94% compared to the state-of-the-art JSMA and GAN based methods, respectively, while maintaining largely unchanged evasion capabilities against IDS.

关键词： Water Actuators Perturbation methods Transportation Cyber-physical systems Sensor phenomena and characterization Generative adversarial networks Sensor systems Critical infrastructure Security

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PML-MPI: A Pre-Trained ML Framework for Efficient Collective Algorithm Selection in MPI

PML-MPI: A Pre-Trained ML Framework for Efficient Collective...

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Mingzhe Han Goutham Kalikrishna Reddy Kuncham Ben Michalowicz Rahul Vaidya Mustafa Abduljabbar Aamir Shafi Hari Subramoni Dhabaleswar K. DK Panda Department of Computer Science and Engineering The Ohio State University Columbus Ohio USA

ISBN: (数字)9798350364606

ISBN: (纸本)9798350364613

The Message Passing Interface is the de facto standard in high-performance computing (HPC) for inter-process communication. MPI libraries employ numerous algorithms for each collective communication pattern whose behavior is largely affected by the underlying hardware, communication pattern, message size, and number of processes involved. Choosing the “best” algorithm for every possible scenario is a non-trivial task. MPI libraries primarily depend on heuristics for algorithm selection on previously unseen clusters, often resulting in evident slowdowns. Although offline micro-benchmarking tools can exhaustively identify optimal algorithms for all configurations, this is an excessively time-consuming approach. Machine Learning (ML) emerged as an alternate approach. However, most ML-based approaches employ online methods that introduce additional runtime overhead, which makes this impractical at scale. To address this challenge, we propose a pre-trained ML framework that eliminates runtime overhead. Our model requires only a quick inference for each new cluster without necessitating model retraining. It incorporates various hardware features to enhance its adaptability across diverse clusters. Our model's training utilizes tuning data from a broad range of architectures, promoting its versatility and our proposed system exhibits up to 6.3% speedup over default heuristics on systems of up to 1024 cores while significantly minimizing model overhead in comparison to existing methodologies.

关键词： Adaptation models Machine learning algorithms Runtime software algorithms Clustering algorithms Benchmark testing Libraries

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In-Depth Evaluation of a Lower-Level Direct-Verbs API on InfiniBand-based Clusters: Early Experiences

In-Depth Evaluation of a Lower-Level Direct-Verbs API on Inf...

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Benjamin Michalowicz Kaushik Kandadi Suresh Bharath Ramesh Aamir Shafi Hari Subramoni Mustafa Abduljabbar Dhabaleswar Panda Department of Computer Science and Engineering The Ohio State University Columbus USA

Many High-Performance Computing (HPC) clusters around the world use some variation of InfiniBand interconnects, all of which are powered by the ‘‘Verbs’’ API. Verbs supply a quick, efficient, and developer-friendly method of passing data buffers between nodes through their interconnect(s). In more recent years, the MLX5-DV (Direct Verbs) API has made itself known as a method of providing mechanisms to access and expose low-level structures and buffers to a developer. In principle, MLX5-DV is meant to give improved performance over Verbs thanks to the removal of intermediate layers of software abstraction. In this paper, we examine the inner workings of what this means for potential performance improvement and how MLX5-DV compares to its higher-level counterpart. In addition, we will offer insights on how application developers and MPI programmers can use this to their advantage based on initial experiences with benchmark and application-level results.

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Adaptive Multi-Layered Non-Terrestrial Network with Integrated FSO and RF Communications for Enhanced Global Connectivity

Adaptive Multi-Layered Non-Terrestrial Network with Integrat...

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ACIS international Conference on software engineering, Artificial Intelligence, Networking, and parallel/distributed Computing (SNPD)

作者： Osei Wusu Brempong Jnr Okechukwu Clement Agomuo Junaid Hussain Muzamal Khalert school of computing University of Utah Salt Lake City Utah USA dept. of Operations and Information Systems University of Utah Salt Lake City Utah USA Fast School of Computing Fast - NUCES Lahore Pakistan

ISBN: (数字)9798350391954

ISBN: (纸本)9798350391961

This paper introduces the Adaptive Multi-Layered Non-Terrestrial Network (AMLT-NTN), an architecture that integrates satellite, High Altitude Platform Stations (HAPS), and Unmanned Aerial Vehicles (UAVs). It leverages a combination of Free-Space Optical (FSO) and Radio Frequency (RF) communications, tailored for specific operational altitudes to enhance connectivity in remote and disaster-stricken regions. The AMLT-NTN tackles the complexities of dynamic power allocation and link selection by incorporating real-time optimization algorithms. This significantly boosted the network’s robustness and adaptability to environmental challenges and demand fluctuations. Simulations in OMNeT++ highlighted a quantifiable enhancement, with up to a 30% increase in throughput and a 40% decrease in latency, outstripping conventional NTN. The AMLT-NTN architecture demonstrates unparalleled resilience, consistently delivering high service levels across various conditions. Looking ahead, this research paves the way for integrating emerging communication technologies and scaling the architecture for widespread adoption. The proposed AMLT-NTN offers transformative solutions for rural connectivity and rapid disaster response, thus poised to impact global digital inclusion.

关键词： Radio frequency Fluctuations Adaptive systems Heuristic algorithms software algorithms Computer architecture Throughput

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A V-shaped distributed Array for Drone-Borne Signal Positioning Using Low-Cost SDRs

A V-shaped Distributed Array for Drone-Borne Signal Position...

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IEEE international symposium on Intelligent Signal Processing and Communication systems (ISPACS)

作者： Sheng-Chun Chiu Min-Rong Ye Shou-Sheu Lin Department of Computer and Communication Engineering National Kaohsiung University of Science and Technology Kaohsiung Taiwan

ISBN: (数字)9798350389210

ISBN: (纸本)9798350389227

This paper proposes a two-dimensional coordinate positioning system based on a V-shaped distributed antenna array. The system employs two low-cost dual-channel software-defined radio (SDR) platforms, where each SDR functions as an independent uniform linear array (ULA). These ULAs form a distributed antenna array in a V-shaped configuration. The received signals are processed using the Multiple Signal Classification (MUSIC) algorithm to estimate two independent directions of arrival (DOA) of target signal. By applying triangulation principles, the system is able to calculate the DOA and range of the target simultaneously. Most previous works typically conduct short-range experiments in indoor environments. To overcome the low reception sensitivity of off-the-shelf, low-cost SDRs, a drone-borne SDR with a power amplifier is used to extend the testing range. We conducted real-world outdoor flight tests, and the results show that the accuracy of range and angle estimation can reach $\mathbf{9.91\%}$ and $\mathbf{8.3 6\%}$, respectively.

关键词： Phased arrays Direction-of-arrival estimation Accuracy Array signal processing Dipole antennas Transmitting antennas Signal processing algorithms Multiple signal classification Testing software radio

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GitFL: Uncertainty-Aware Real-Time Asynchronous Federated Learning Using Version Control 44

GitFL: Uncertainty-Aware Real-Time Asynchronous Federated Le...

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44th IEEE Real-Time systems symposium, RTSS 2023

作者： Hu, Ming Xia, Zeke Yan, Dengke Yue, Zhihao Xia, Jun Huang, Yihao Liu, Yang Chen, Mingsong School of Computer Science and Engineering Nanyang Technological University Singapore MoE Engineering Research Center of SW/HW Co-Design Tech. and App. East China Normal University China East China Normal University Moe International Joint Lab of Trustworthy Software China

ISBN: (纸本)9798350328578

As a promising distributed machine learning paradigm that enables collaborative training without compromising data privacy, Federated Learning (FL) has been increasingly used in large-scale A IoT (Artificial Intelligence of Things) system design. However, due to the lack of efficient management of straggling devices, existing FL methods greatly suffer from the problems of long response time (e.g., training and communication latency) and low inference accuracy. Things become even worse when taking various uncertain factors (e.g., network delays, performance variances caused by process variation) existing in AIoT scenarios into account. To address this issue, this paper proposes a novel asynchronous FL framework named GitFL, whose implementation is inspired by the famous version control system Git. Unlike traditional FL, the cloud server of GitFL maintains a master model (i.e., the global model) together with a set of branch models indicating the trained local models committed by selected devices, where the master model is updated based on both all the pushed branch models and their version information, and only the branch models after the pull operation are dispatched to devices. By using our proposed Reinforcement Learning (RL)-based device selection mechanism, a pulled branch model with an older version will be more likely to be dispatched to a faster and less frequently selected device for the next round of local training. In this way, GitFL enables both effective controls of model staleness and adaptive load balance of versioned models among straggling devices, thus avoiding performance deterioration while ensuring real-time performance. Comprehensive experimental results on well-known models and datasets show that, compared with state-of-the-art asynchronous and synchronous FL methods, GitFL can achieve up to 2.64X training acceleration and 7.88 % inference accuracy improvements in various uncertain scenarios. © 2023 IEEE.

关键词： Data privacy

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Benefits of Optimistic parallel Discrete Event Simulation for Network-on-Chip Simulation

Benefits of Optimistic Parallel Discrete Event Simulation fo...

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international Workshop/symposium on distributed Simulation and Real-Time Applications (DS-RT)

作者： Maximilian Bremer Nirmalendu Patra Tan Nguyen Dilip Vasudevan Cy Chan Applied Mathematics and Computational Research Division Lawrence Berkeley National Laboratory Berkeley CA USA

The end of Moore's law has placed a two-fold demand on hardware simulation. Firstly, efficient co-design requires fast simulation of hardware systems in order to vet proposed designs. Secondly, modern simulator platforms need to become increasingly concurrent as well. To address these challenges, we develop an optimistic time warp-based parallelization for the Structural Simulation Toolkit (SST). Our optimistic PDES engine hides synchronization costs by speculatively executing tasks, leading to better compute resource utilization on modern multicore architectures. Given the significant engineering effort to make custom existing hardware models reversible, we also develop a new SST component, called escher. The escher workflow instruments arbitrary SST applications and replays event traces through the different SST parallelizations. This is a useful tool for hardware engineers who want to understand the benefits of optimistic parallelization before undertaking the significant software engineering effort to make their hardware models reversible. We demonstrate this workflow by generating traces for a tiled mesh-NOC architecture and show a 2.1x to 3.7x speed-up using our optimistic SST parallelization versus the current conservative SST parallelization.

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