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POMACS V8, N3, December 2024 Editorial

proceedings OF THE acm ON measurement AND ANALYSIS OF COMPUTING systems 2024年第3期8卷 1-1页

作者： Oh, Sewoong Sitaraman, Ramesh k. VAN Houdt, Benny Univ Washington Seattle WA 98195 USA Univ Massachusetts Amherst MA USA Akamai Technol Cambridge MA USA Univ Antwerp Antwerp Belgium

The proceedings of the acm on measurement and Analysis of Computing systems (POMACS) focuses on the measurement and performance evaluation of computer systems and operates in close collaboration with the acm Special Interest Group sigmetrics. All papers in this issue of POMACS will be presented at the acm sigmetrics 2025 conference on June 9-13, 2025, in Stony Brook, New York, *** papers in this issue were selected during the summer submission round by the 93 members of the acm sigmetrics 2025 program committee via a rigorous review process. Each paper was conditionally accepted (and shepherded), or allowed to be resubmitted to one of the subsequent two sigmetrics deadlines, or rejected (with resubmission allowed only after a year).POMACS is publishing 20 papers out of 110 submissions in this issue, of which 3 had previously received a one-shot revision decision. Submissions typically receive at least three reviews. While most papers were decided in the online discussion phase, borderline cases were extensively discussed during the online program committee meeting. Based on the indicated track(s), roughly 18% of the submissions were in the Theory track, 39% were in the measurement & Applied modeling track, 27% were in the systems track, and 16% were in the Learning *** individuals contributed to the success of this issue of POMACS. We thank the authors, who submitted their best work to sigmetrics/POMACS. We would also like to thank the program committee members who provided constructive feedback in their reviews to authors and participated in the online discussions and program committee meeting. Further, we thank the several external reviewers who provided their expert opinions on specific submissions that required additional input. Finally, we are also grateful to the acm sigmetrics Chair, Mor Harchol-Balter, to the sigmetrics Organization Committee and to the sigmetrics Executive Committee for their ongoing efforts and initiatives for creating an exciti

关键词： computer systems measurement modeling performance analysis

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A Comparative Analysis of Ookla Speedtest and measurement Labs Network Diagnostic Test (NDT7) 23

A Comparative Analysis of Ookla Speedtest and Measurement La...

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2023 acm sigmetrics International conference on measurement and modeling of computer systems, sigmetrics 2023

作者： Macmillan, Kyle Mangla, Tarun Saxon, James Marwell, Nicole P. Feamster, Nick University of Chicago Chicago United States

ISBN: (纸本)9798400700743

Consumers, regulators, and ISPs all use client-based "speed tests"to measure network performance, both in single-user settings and in aggregate. Two prevalent speed tests, Ookla's Speedtest and measurement Lab's Network Diagnostic Test (NDT), are often used for similar purposes, despite having significant differences in both the test design and implementation, and in the infrastructure used to perform measurements. In this paper, we present the first-ever comparative evaluation of Ookla and NDT7 (the latest version of NDT), both in controlled and wide-Area settings. Our goal is to characterize when and to what extent these two speed tests yield different results, as well as the factors that contribute to the differences. To study the effects of the test design, we conduct a series of controlled, in-lab experiments under a comprehensive set of network conditions and usage modes (e.g., TCP congestion control, native vs. browser client). Our results show that Ookla and NDT7 report similar speeds under most in-lab conditions, with the exception of networks that experience high latency, where Ookla consistently reports higher throughput. To characterize the behavior of these tools in wide-Area deployment, we collect more than 80,000 pairs of Ookla and NDT7 measurements across nine months and 126 households, with a range of ISPs and speed tiers. This first-of-its-kind paired-Test analysis reveals many previously unknown systemic issues, including high variability in NDT7 test results and systematically under-performing servers in the Ookla network. © 2023 Owner/Author.

关键词： Laboratories

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Poster: The Use of Machine Learning in Electrical Impedance Tomography - A Variable Frequency Approach 24

Poster: The Use of Machine Learning in Electrical Impedance ...

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22nd acm conference on Embedded Networked Sensor systems, SenSys 2024

作者： Kulisz, Monika Rymarczyk, Tomasz Klosowski, Grzegorz Niderla, Konrad Oleszek, Michal Faculty of Management Lublin University of Technology Lublin Poland Institute of Computer Science and Innovative Technologies WSEI University Research and Development Center Netrix S.A. Lublin Poland

ISBN: (纸本)9798400706974

This study presents a novel technique for reconstructing the internal structures of industrial tank reactors using electrical impedance tomography (EIT). The method uses three different measurement vectors, each corresponding to different electrical frequencies - -100 kHz, 50 kHz, and 10 kHz - -to improve the accuracy and reliability of EIT reconstructions. The goal was to get the most out of both the resistive and reactive data from the EIT system by using machine learning methods that took frequency-specific data into account. This data was shown as complex numbers. To process the multi-frequency data collected from the measurements, an LSTM network was used. The results show that the multi-frequency model significantly outperforms single-frequency methods in terms of reconstruction accuracy. © 2024 Copyright held by the owner/author(s).

关键词： Electric impedance tomography

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BehavIoT: Measuring Smart Home IoT Behavior Using Network-Inferred Behavior Models 23

BehavIoT: Measuring Smart Home IoT Behavior Using Network-In...

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acm on Internet measurement conference (IMC)

作者： Hu, Tianrui Dubois, Daniel J. Choffnes, David Northeastern Univ Boston MA 02115 USA

ISBN: (纸本)9798400703829

Smart home IoT platforms are typically closed systems, meaning that there is poor visibility into device behavior. Understanding device behavior is important not only for determining whether devices are functioning as expected, but also can reveal implications for privacy (e.g., surreptitious audio/video recording), security (e.g., device compromise), and safety (e.g., denial of service on a baby monitor). While there has been some work on identifying devices and a handful of activities, an open question is what is the extent to which we can automatically model the entire behavior of an IoT deployment, and how it changes over time, without any privileged access to IoT devices or platform messages. In this work, we demonstrate that the vast majority of IoT behavior can indeed be modeled, using a novel multi-dimensional approach that relies only on the (often encrypted) network traffic exchanged by IoT devices. Our key insight is that IoT behavior (including cross-device interactions) can often be captured using relatively simple models such as timers (for periodic behavior) and probabilistic state-machines (for user-initiated behavior and devices interactions) during a limited observation phase. We then propose deviation metrics that can identify when the behavior of an IoT device or an IoT system changes over time. Our models and metrics successfully identify several notable changes in our IoT deployment, including a camera that changed locations, network outages that impact connectivity, and device malfunctions.

关键词： Smart Home IoT measurement Techniques Behavior modeling

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The First 5G-LTE Comparative Study in Extreme Mobility 22

The First 5G-LTE Comparative Study in Extreme Mobility

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2022 acm sigmetrics/IFIP PERFORMANCE Joint International conference on measurement and modeling of computer systems, sigmetrics/PERFORMANCE 2022

作者： Pan, Yueyang Li, Ruihan Xu, Chenren Peking University Beijing China

ISBN: (纸本)9781450391412

5G claims to support mobility up to 500 km/h according to the 3GPP standard. However, its field performance under high-speed scenes remains in mystery. In this paper, we conduct the first large-scale measurement campaign on a high-speed railway route operating at the maximum speed of 350 km/h, with full coverage of LTE and 5G (NSA and SA) along the track. Our study consumed 1788.8 GiB of cellular data in six months, covering the three major carriers in China and the recent standardized QUIC protocol. Based on our dataset, we reveal the key characteristics of 5G and LTE in extreme mobility in terms of throughput, RTT, loss rate, signal quality, and physical resource utilization. We further develop a taxonomy of handovers in both LTE and 5G and carry out the link-layer latency breakdown analysis. Our study pinpoints the deficiencies in the user equipment, radio access network, and core network which hinder seamless connectivity and better utilization of 5G's high bandwidth. Our findings highlight the directions of the next step in the 5G evolution. © 2022 Owner/Author.

关键词： high-speed rail extreme mobility handover measurement 5g

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A Comprehensive Empirical Study of Query Performance Across GPU DBMSes 22

A Comprehensive Empirical Study of Query Performance Across ...

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2022 acm sigmetrics/IFIP PERFORMANCE Joint International conference on measurement and modeling of computer systems, sigmetrics/PERFORMANCE 2022

作者： Suh, Young-Kyoon An, Junyoung Tak, Byungchul Na, Gap-Joo Kyungpook National University Daegu Korea Republic of School of Computer Science and Engineering Kyungpook National University Korea Republic of Electronics and Telecommunications Research Institute Daejeon Korea Republic of

ISBN: (纸本)9781450391412

In recent years, GPU database management systems (DBMSes) have rapidly become popular largely due to their remarkable acceleration capability obtained through extreme parallelism in query evaluations. However, there has been relatively little study on the characteristics of these GPU DBMSes for a better understanding of their query performance in various contexts. To fill this gap, we have conducted a rigorous empirical study to identify such factors and to propose a structural causal model, including key factors and their relationships, to explicate the variances of the query execution times on the GPU DBMSes. To test the model, we have designed and run comprehensive experiments and conducted in-depth statistical analyses on the obtained data. As a result, our model achieves about 77% amount of variance explained on the query time and indicates that reducing kernel time and data transfer time are the key factors to improve the query time. Also, our results show that the studied systems still need to resolve several concerns such as bounded processing within GPU memory, lack of rich query evaluation operators, limited scalability, and GPU under-utilization. © 2022 Owner/Author.

关键词： query time gpu dbms performance evaluation causal model

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Understanding the confounding factors of inter-domain routing modeling 22

Understanding the confounding factors of inter-domain routin...

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22nd acm Internet measurement conference (IMC)

作者： Kastanakis, Savvas Giotsas, Vasileios Suri, Neeraj Univ Lancaster Lancaster England

ISBN: (纸本)9781450392594

The Border Gateway Protocol (BGP) is a policy-based protocol, which enables Autonomous systems (ASes) to independently define their routing policies with little or no global coordination. AS-level topology and AS-level paths inference have been long-standing problems for the past two decades, yet, an important question remains open: "which elements of Internet routing affect the AS-path inference accuracy and how much do they contribute to the error?". In this work, we: (1) identify the confounding factors behind Internet routing modeling, and (2) quantify their contribution on the inference error. Our results indicate that by solving the first-hop inference problem, we can increase the exact-path score from 33.6% to 84.1%, and, by taking geolocation into consideration, we can refine the accuracy up to 94.6%.

关键词： Gateways (computer networks)

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StarAngle: User Orientation Sensing with Beacon Phase measurements of Multiple Starlink Satellites 24

StarAngle: User Orientation Sensing with Beacon Phase Measur...

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22nd acm conference on Embedded Networked Sensor systems, SenSys 2024

作者： Rathi, Raghav Zhang, Zhenghao Dept. of Computer Science Florida State University TallahasseeFL United States

ISBN: (纸本)9798400706974

Low Earth Orbit (LEO) satellite networks have been growing very rapidly in recent years. In this paper, we propose a novel method, called StarAngle, which estimates user orientation with the beacon signals of Starlink satellites. StarAngle measures the beacon phase difference between two receiving antennas because the phase difference is a function of the user orientation. The phase measurements are compared with mathematical calculations based on known orbital parameters of Starlink satellites and the value that leads to the best agreement is used as the estimation. We overcome challenges due to asynchronous clocks in our commodity antennas by subtracting the phase measurements of one satellite by another which cancels the biases caused by clock mismatch. We experimentally test StarAngle in 10 locations under challenging weather conditions and our results show that the median estimation error is 7.5 degrees. Our results also confirm that the phase information of Starlink satellites can be measured reliably and may be used to support other applications in addition to user orientation estimation. © 2024 Copyright held by the owner/author(s).

关键词： Phase measurement

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Wi2DMeasure: WiFi-based 2D Object Size measurement 24

Wi2DMeasure: WiFi-based 2D Object Size Measurement

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22nd acm conference on Embedded Networked Sensor systems, SenSys 2024

作者： Wang, Xuanzhi Wang, Junzhe Niu, Kai Xiong, Jie Zhang, Fusang Yi, Enze Yu, Anlan Yao, Zhiyun Zhang, Daqing School of Computer Science Peking University Beijing China Xiaomi Corporation Beijing China Microsoft Research Asia University of Massachusetts Amherst Shanghai China Institute of Software Chinese Academy of Sciences Beijing China SAMOVAR Telecom SudParis IP Paris Évry-Courcouronnes France

ISBN: (纸本)9798400706974

While a large range of sensing applications such as activity sensing and vital sign monitoring have been realized with WiFi sensing, using commercial WiFi devices to obtain fine-grained size information of objects remains challenging due to the narrow bandwidth of WiFi. Very recent studies attempted to measure object sizes using WiFi signals. However, these systems are still far from practical with a lot of limitations including requiring multiple transceiver pairs and can only measure one-dimensional size, hindering their real-life adoption. Also, these systems rely on Channel State Information (CSI) to work, which is only available on few commercial WiFi cards. In this work, we propose to employ a new channel data, i.e., Beamforming Feedback Information (BFI), widely available on almost all new generation WiFi cards for fine-grained size measurement. Through thoroughly analyzing the mathematical relationship between BFI and CSI, we show how to use BFI to achieve fine-grained size measurement. We propose a novel method to accurately measure the two-dimensional size of an object using a single transceiver pair by identifying the positions of singularities when the object passes through the diffraction zone of the transceiver pair. Experiment results show that Wi2DMeasure can accurately measure the two-dimensional size of objects under various conditions, achieving a small median error of only 3.7 mm. © 2024 Copyright is held by the owner/author(s).

关键词： Wi-Fi

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SimNet: Accurate and High-Performance computer Architecture Simulation using Deep Learning

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proceedings OF THE acm ON measurement AND ANALYSIS OF COMPUTING systems 2022年第2期6卷 1–24页

作者： Li, Lingda Pandey, Santosh Flynn, Thomas Liu, Hang Wheeler, Noel Hoisie, Adolfy Brookhaven Natl Lab Upton NY 11973 USA Stevens Inst Technol Hoboken NJ 07030 USA Lab Phys Sci College Pk NY USA

While cycle-accurate simulators are essential tools for architecture research, design, and development, their practicality is limited by an extremely long time-to-solution for realistic applications under investigation. This work describes a concerted effort, where machine learning (ML) is used to accelerate microarchitecture simulation. First, an ML-based instruction latency prediction framework that accounts for both static instruction properties and dynamic processor states is constructed. Then, a GPU-accelerated parallel simulator is implemented based on the proposed instruction latency predictor, and its simulation accuracy and throughput are validated and evaluated against a state-of-the-art simulator. Leveraging modern GPUs, the ML-based simulator outperforms traditional CPU-based simulators significantly.

关键词： computer architecture simulation deep learning GPU

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