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Fiat Lux: Illuminating IPv6 Apportionment With Different Datasets

proceedings of the acm on measurement and analysis of computing systems 2023年第1期7卷 1-24页

作者： Hsu, Amanda Li, Frank Pearce, Paul Georgia Inst Technol Atlanta GA 30332 USA

IPv6 adoption continues to grow, making up more than 40% of client traffic to Google globally. While the ubiquity of the IPv4 address space makes it comparably easier to understand, the vast and less studied IPv6 address space motivates a variety of works detailing methodology to collect and analyze IPv6 properties, many of which use knowledge from specific data sources as a lens for answering research questions. Despite such work, questions remain on basic properties such as the appropriate prefix size for different research tasks. Our work fills this knowledge gap by presenting an analysis of the apportionment of the IPv6 address space from the ground-up, using data and knowledge from numerous data sources simultaneously, aimed at identifying how to leverage IPv6 address information for a variety of research tasks. Utilizing WHOIS data from RIRs, routing data, and hitlists, we highlight fundamental differences in apportionment sizes and structural properties depending on data source and examination method. We focus on the different perspectives each dataset offers and the disjoint, heterogeneous nature of these datasets when taken together. We additionally leverage a graph-based analysis method for these datasets that allows us to draw conclusions regarding when and how to intersect the datasets and their utility. The differences in each dataset's perspective is not due to dataset problems but rather stems from a variety of differing structural and deployment behaviors across RIRs and IPv6 providers alike. In light of these inconsistencies, we discuss network address partitioning, best practices, and considerations for future IPv6 measurement and analysis projects.

关键词： IPv6 Network measurement IP Allocation IP Apportionment

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Near-Optimal Stochastic Bin-Packing in Large Service systems with Time-Varying Item Sizes

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proceedings of the acm on measurement and analysis of computing systems 2023年第3期7卷 1-46页

作者： Hong, Yige Xie, Qiaomin Wang, Weina Carnegie Mellon Univ Pittsburgh PA 15213 USA Univ Wisconsin Madison Madison WI 53706 USA

In modern computing systems, jobs' resource requirements often vary over time. Accounting for this temporal variability during job scheduling is essential for meeting performance goals. However, theoretical understanding on how to schedule jobs with time-varying resource requirements is limited. Motivated by this gap, we propose a new setting of the stochastic bin-packing problem in service systems that allows for time-varying job resource requirements, also referred to as 'item sizes' in traditional bin-packing terms. In this setting, a job or 'item' must be dispatched to a server or 'bin' upon arrival. Its resource requirement may vary over time while in service, following a Markovian assumption. Once the job's service is complete, it departs from the system. Our goal is to minimize the expected number of active servers, or 'non-empty bins', in steady state. Under our problem formulation, we develop a job dispatch policy, named Join-Reqesting-Server (JRS). Broadly, JRS lets each server independently evaluate its current job configuration and decide whether to accept additional jobs, balancing the competing objectives of maximizing throughput and minimizing the risk of resource capacity overruns. The JRS dispatcher then utilizes these individual evaluations to decide which server to dispatch each arriving job to. The theoretical performance guarantee of JRS is in the asymptotic regime where the job arrival rate scales large linearly with respect to a scaling factor r. We show that JRS achieves an additive optimality gap of O(root r) in the objective value, where the optimal objective value is o(r). When specialized to constant job resource requirements, our result improves upon the state-of-the-art Theta(r) optimality gap. Our technical approach highlights a novel policy conversion framework that reduces the policy design problem into a single-server problem.

关键词： stochastic bin-packing large service systems policy conversion asymptotic optimality

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AraSync: Precision Time Synchronization in Rural Wireless Living Lab 30

AraSync: Precision Time Synchronization in Rural Wireless Li...

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30th International Conference on Mobile computing and Networking, acm MobiCom 2024

作者： Nadim, Md Islam, Taimoor Ul Reddy, Salil Zhang, Tianyi Meng, Zhibo Afzal, Reshal Babu, Sarath Ahmad, Arsalan Qiao, Daji Arora, Anish Zhang, Hongwei Iowa State University United States Ohio State University United States

ISBN: (纸本)9798400704895

Time synchronization is a critical component in network operation and management, and it is also required by Ultra-Reliable, Low-Latency Communications (URLLC) in next-generation wireless systems such as those of 5G, 6G, and Open RAN. In this context, we design and implement AraSync as an end-to-end time synchronization system in the ARA wireless living lab to enable advanced wireless experiments and applications involving stringent time constraints. We make use of Precision Time Protocol (PTP) at different levels to achieve synchronization accuracy in the order of nanoseconds. Along with fiber networks, AraSync enables time synchronization across the AraHaul wireless x-haul network consisting of long-range, high-capacity mmWave and microwave links. In this paper, we present the detailed design and implementation of AraSync, including its hardware and software components and the PTP network topology. Further, we experimentally characterize the performance of AraSync from spatial and temporal dimensions. Our measurement and analysis of the clock offset and mean path delay show the impact of the wireless channel and weather conditions on the PTP synchronization accuracy. © 2024 Copyright is held by the owner/author(s). Publication rights licensed to acm.

关键词： 5G mobile communication systems

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PyGim : An Efficient Graph Neural Network Library for Real Processing-In-Memory Architectures

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proceedings of the acm on measurement and analysis of computing systems 2024年第3期8卷 1-36页

作者： Giannoula, Christina Yang, Peiming Fernandez, Ivan Yang, Jiacheng Durvasula, Sankeerth Li, Yu xin Sadrosadati, Mohammad Luna, Juan gomez Mutlu, Onur Pekhimenko, Gennady Univ Toronto Toronto ON Canada Swiss Fed Inst Technol Zurich Switzerland Vector Inst Toronto ON Canada CentML Toronto ON Canada Barcelona Supercomp Ctr Barcelona Spain Univ Politecn Cataluna Barcelona Spain NVIDIA Zurich Switzerland

Graph Neural Networks (GNNs) are emerging models to analyze graph-structure data. GNN execution involves both compute-intensive and memory-intensive kernels. The latter kernels dominate execution time, because they are significantly bottlenecked by data movement between memory and processors. Processing-In-Memory (PIM) systems can alleviate this data movement bottleneck by placing simple processors near or inside to memory arrays. This work investigates the potential of PIM systems to alleviate the data movement bottleneck in GNNs, and introduces PyGim, an efficient and easy-to-use GNN library for real PIM systems. We propose intelligent parallelization techniques for memory-intensive kernels of GNNs tailored for real PIM systems, and develop an easy-to-use Python API for them. PyGim employs a cooperative GNN execution, in which the compute- and memory-intensive kernels are executed in processor-centric and memory-centric computing systems, respectively, to fully exploit the hardware capabilities. PyGim integrates a lightweight autotuner to tune the parallelization strategy of the memory-intensive kernel of GNNs and enable high programming ease. We extensively evaluate PyGim on a real-world PIM system that has 16 PIM DIMMs with 1992 PIM cores connected to a Host CPU. In GNN inference, we demonstrate that it outperforms prior state-of-the-art PIM works by on average 4.38x (up to 7.20x), and state-of-the-art PyTorch running on Host by on average 3.04x (up to 3.44x). PyGim improves energy efficiency by 2.86x (up to 3.68x) and 1.55x (up to 1.75x) over prior PIM and PyTorch Host schemes, respectively. In memory-intensive kernel of GNNs, PyGim provides 11.6x higher resource utilization in PIM system than that of PyTorch library (optimized CUDA implementation) in GPU systems. Our work provides useful recommendations for software, system and hardware designers. PyGim is publicly and freely available at https://***/CMU-SAFARI/PyGim facilitate the widespread use of PIM

关键词： machine learning graph neural networks sparse matrix-matrix multi- plication library multicore processing-in-memory near-data processing memory systems data movement bottleneck DRAM benchmarking real-system characterization workload characterization

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Poster: Building Comprehensive Telecommunications Datasets During a Major Climatic Event 24

Poster: Building Comprehensive Telecommunications Datasets D...

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acm Internet measurement Conference (IMC)

作者： Bertholdo, Leandro Marcio Paredes, Renan Barreto Marcos, Pedro de Botelho Univ Fed Rio Grande do Sul Porto Alegre RS Brazil Fundacao Univ Fed Rio Grande Rio Grande Brazil

ISBN: (纸本)9798400705922

In May 2024, Rio Grande do Sul, Brazil's southernmost state, endured a month of severe rainfall, leading to extensive infrastructure damage in more than 400 cities. This major climatic event caused widespread disruptions to the state's infrastructure, including roads, bridges, electrical plants, data centers, communication systems, and homes, affecting millions of people. In this paper, we describe the datasets we are collecting from various sources, including official reports, IXP connectivity and routing information, Internet quality measurements, and failure reports from data centers, the ISPs assotiation in the region, and the Brazilian National Research and Education Network. These datasets are being used to provide the first analyses of how these events affected the resilience of the Communication Technology (ICT) infrastructure in Rio Grande do Sul. We plan to make these datasets available to the community for future studies on the climate impact on communications infrastructure and to better identify the challenges faced when resilience is critically tested. Additionally, we hope this data can outline improvements in ICT infrastructure to enhance operations and aid in the reconstruction process.

关键词： climatic event analysis

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Numerical Fuzz: A Type System for Rounding Error analysis

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proceedings OF THE acm ON PROGRAMMING LANGUAGES-PacmPL 2024年第PLDI期8卷 1954-1978页

作者： Kellison, Ariel E. Hsu, Justin Cornell Univ Ithaca NY 14853 USA

Algorithms operating on real numbers are implemented as floating-point computations in practice, but floating-point operations introduce roundoff errors that can degrade the accuracy of the result. We propose Lambda(num), a functional programming language with a type system that can express quantitative bounds on roundoff error. Our type system combines a sensitivity analysis, enforced through a linear typing discipline, with a novel graded monad to track the accumulation of roundoff errors. We prove that our type system is sound by relating the denotational semantics of our language to the exact and floating-point operational semantics. To demonstrate our system, we instantiate Lambda(num) with error metrics proposed in the numerical analysis literature and we show how to incorporate rounding operations that faithfully model aspects of the IEEE 754 floating-point standard. To show that Lambda(num) can be a useful tool for automated error analysis, we develop a prototype implementation for Lambda(num) that infers error bounds that are competitive with existing tools, while often running significantly faster. Finally, we consider semantic extensions of our graded monad to bound error under more complex rounding behaviors, such as non-deterministic and randomized rounding.

关键词： Floating point Roundoff error Linear type systems

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Characterizing User Platforms for Video Streaming in Broadband Networks 24

Characterizing User Platforms for Video Streaming in Broadba...

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acm Internet measurement Conference (IMC)

作者： Wang, Yifan Lyu, Minzhao Sivaraman, Vijay Univ New South Wales Sydney NSW Australia

ISBN: (纸本)9798400705922

Internet Service Providers (ISPs) bear the brunt of being the first port of call for poor video streaming experience. ISPs can benefit from knowing the user's device type (e.g., Android, iOS) and software agent (e.g., native app, Chrome) to troubleshoot platform-specific issues, plan capacity and create custom bundles. Unfortunately, encryption and NAT have limited ISPs' visibility into user platforms across video streaming providers. We develop a methodology to identify user platforms for video streams from four popular providers, namely YouTube, Netflix, Disney, and Amazon, by analyzing network traffic in real-time. First, we study the anatomy of the connection establishment process to show how TCP/QUIC and TLS handshakes vary across user platforms. We then develop a classification pipeline that uses 62 attributes extracted from the handshake messages to determine the user device and software agent of video flows with over 96% accuracy. Our method is evaluated and deployed in a large campus network (mimicking a residential broadband network) serving users including dormitory residents. analysis of 100+ million video streams over a four-month period reveals insights into the mix of user platforms across the video providers, variations in bandwidth consumption across operating systems and browsers, and differences in peak hours of usage.

关键词： Network traffic analysis user platform identification video streaming TLS fingerprinting

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Towards Disaggregated NDP Architectures for Large-scale Graph Analytics

Towards Disaggregated NDP Architectures for Large-scale Grap...

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2024 Workshops of the International Conference for High Performance computing, Networking, Storage and analysis, SC Workshops 2024

作者： Lee, Suyeon Rao, Vishal Gavrilovska, Ada Georgia Institute of Technology Atlanta United States

ISBN: (纸本)9798350355543

The performance of large-scale graph analytics is limited by the capacity and performance of the memory subsystem on the platforms on which they execute. In this paper, we first discuss the limitations of existing approaches to scaling graph processing, and describe how they can be addressed via the use of disaggregated solutions with near-data processing (NDP) capabilities. Using observations from experimental analysis of the trade-offs for different types of graphs and analytics kernels, we identify the systems-level mechanisms that will be required by future graph analytics frameworks for disaggregated NDP architectures. © 2024 IEEE.

关键词： computational memory disaggregated systems graph analytics near-data processing

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Poster: Modified Dynamic Beta RED - A New AQM Algorithm for Internet Congestion Control 23

Poster: Modified Dynamic Beta RED - A New AQM Algorithm for ...

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acm on Internet measurement Conference (IMC)

作者： Gimenez, Angel Bonastre, Oscar M. Valero, Jose Amigo, Jose M. Miguel Hernandez Univ Elche Spain

ISBN: (纸本)9798400703829

In this work we present a performance study of modified Dynamic Beta RED (mDBetaRED), a new Active Queue Management (AQM) RED-type algorithm based on dynamical variants with the ability to adapt to the characteristics of mixed networks. As well, we rely on real campus traffic traces to build a traffic model on which we evaluate our proposal under the NS3 simulator. The parameters of our AQM algorithm are dynamically adjusted so that the queue length remains stable around a predetermined reference value and according to changing network traffic conditions. We present a performance comparison of mDBetaRED with other AQM algorithms and diverse flows of Internet. Of all the AQM algorithms compared, the mDBetaRED algorithm offers the best throughput while effectively controlling delay and stability.

关键词： design and analysis of algorithms mathematics of computing network algorithms network performance analysis network performance evaluation

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GREEM: An Open-Source Energy measurement Tool for Video Processing 24

GREEM: An Open-Source Energy Measurement Tool for Video Proc...

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15th acm Multimedia systems Conference (acm MMSys)

作者： Bauer, Christian Afzal, Samira Linder, Sandro Prodan, Radu Timmerer, Christian Alpen Adria Univ Klagenfurt Inst Informat Technol ITEC Klagenfurt Austria Alpen Adria Univ Klagenfurt Christian Doppler Lab ATHENA Klagenfurt Austria

ISBN: (纸本)9798400704123

Addressing climate change requires a global decrease in greenhouse gas (GHG) emissions. In today's digital landscape, video streaming significantly influences internet traffic, driven by the widespread use of mobile devices and the rising popularity of streaming platforms. This trend emphasizes the importance of evaluating energy consumption and the development of sustainable and eco-friendly video streaming solutions with a low Carbon Dioxide (CO2) footprint. We developed a specialized tool, released as an open-source library called GREEM, addressing this pressing concern. This tool measures video encoding and decoding energy consumption and facilitates measurement testbeds. It monitors the computational impact on hardware resources and offers various analysis cases. GREEM is helpful for developers, researchers, service providers, and policymakers interested in minimizing the energy consumption of video encoding and streaming.

关键词： measurement Energy CO2 emission Monitoring Open source

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