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1. Raising the Level of Abstraction for Sketch-Based Network Telemetry with SketchPlan

   https://doi.org/10.1145/3646547.3689016  

   Srivastava, Milind; Hung, Shao-Tse; Namkung, Hun; Lin, Kate Ching-Ju; Liu, Zaoxing; Sekar, Vyas (November 2024, ACM)

   While sketch-based network telemetry is attractive, realizing its potential benefits has been elusive in practice. Existing sketch so- lutions offer low-level interfaces and impose high effort on op- erators to satisfy telemetry intents with required accuracies. Ex- tending these approaches to reduce effort results in inefficient deployments with poor accuracy-resource tradeoffs. We present SketchPlan, an abstraction layer for sketch-based telemetry to re- duce effort and achieve high efficiency. SketchPlan takes an en- semble view across telemetry intents and sketches, instead of ex- isting approaches that consider each intent-sketch pair in isola- tion. We show that SketchPlan improves accuracy-resource trade- offs by up-to 12x and up-to 60x vs. baselines, in single-node and network-wide settings. SketchPlan is open-sourced at: https: //github.com/milindsrivastava1997/SketchPlan 

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2. Sketchy With a Chance of Adoption: Can Sketch-Based Telemetry Be Ready for Prime Time?

   Liu, Zaoxing; Namkung, Hun; Agarwal, Anup; Manousis, Antonis; Steenkiste, Peter; Seshan, Srinivasan; Sekar, Vyas (June 2021, IEEE Conference on Network Softwarization NetSoft)

   null (Ed.)

   Sketching algorithms or sketches have emerged as a promising alternative to the traditional packet sampling-based network telemetry solutions. At a high level, they are attractive because of their high resource efficiency and provable accuracy guarantees. While there have been significant recent advances in various aspects of sketching for networking tasks, many fundamental challenges remain unsolved that are likely stumbling blocks for adoption. Our contribution in this paper is in identifying and formulating these research challenges across the ecosystem encompassing network operators, platform vendors/developers, and algorithm designers. We hope that these serve as a necessary fillip for the community to enable the broader adoption of sketch-based telemetry. 

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3. HeteroSketch: Coordinating Network-wide Monitoring in Heterogeneous and Dynamic Networks

   Agarwal, Anup; Liu, Zaoxing; Seshan, Srinivasan (April 2022, 19th USENIX Symposium on Networked Systems Design and Implementation (NSDI 22))

   Network monitoring and measurement have always been critical components of network management. Recent developments in sketch-based monitoring techniques and the deployment opportunities arising from the increasing programmability of network elements (e.g., programmable switches, SmartNICs, and software switches) have made the possibility of accurate, detailed, network-wide telemetry tantalizingly within reach. However, the wide heterogeneity of the programmable hardware and dynamic changes in both resources available and resources needed for monitoring over time make existing approaches to network-wide monitoring impractical. We present HeteroSketch, a framework that consists of two main components: (1) a profiling tool that automatically quantifies the capabilities of arbitrary hardware by predicting their performance for sketching algorithms, and (2) an optimization framework that decides placement of measurement tasks and resource allocation for devices to meet monitoring goals while considering heterogeneous device capabilities. HeteroSketch enables optimized deployments for large networks (> 40,000 nodes) using a novel clustering approach and enables prompt responses to network topology, traffic, query, and resource dynamics. Our evaluation shows that HeteroSketch reduces resource overheads by 20-60% compared to prior art, while maintaining monitoring performance, coverage, and accuracy. 

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4. HeteroSketch: Coordinating Network-wide Monitoring in Heterogeneous and Dynamic Networks

   Agarwal, Anup; Liu, Zaoxing; Seshan, Srinivasan (April 2022, SENIX Symposium on Networked Systems Design and Implementation)

   Network monitoring and measurement have always been critical components of network management. Recent developments in sketch-based monitoring techniques and the deployment opportunities arising from the increasing programmability of network elements (e.g., programmable switches, SmartNICs, and software switches) have made the possibility of accurate, detailed, network-wide telemetry tantalizingly within reach. However, the wide heterogeneity of the programmable hardware and dynamic changes in both resources available and resources needed for monitoring over time make existing approaches to network-wide monitoring impractical. We present HeteroSketch, a framework that consists of two main components: (1) a profiling tool that automatically quantifies the capabilities of arbitrary hardware by predicting their performance for sketching algorithms, and (2) an optimization framework that decides placement of measurement tasks and resource allocation for devices to meet monitoring goals while considering heterogeneous device capabilities. HeteroSketch enables optimized deployments for large networks (> 40,000 nodes) using a novel clustering approach and enables prompt responses to network topology, traffic, query, and resource dynamics. Our evaluation shows that HeteroSketch reduces resource overheads by 20-60% compared to prior art, while maintaining monitoring performance, coverage, and accuracy. 

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5. HeteroSketch: Coordinating Network-wide Monitoring in Heterogeneous and Dynamic Networks

   Agarwal, Anup; Liu, Zaoxing; Seshan, Srinivasan (April 2022, 19th USENIX Symposium on Networked Systems Design and Implementation (NSDI'22))

   Network monitoring and measurement have always been critical components of network management. Recent developments in sketch-based monitoring techniques and the deployment opportunities arising from the increasing programmability of network elements (e.g., programmable switches, SmartNICs, and software switches) have made the possibility of accurate, detailed, network-wide telemetry tantalizingly within reach. However, the wide heterogeneity of the programmable hardware and dynamic changes in both resources available and resources needed for monitoring over time make existing approaches to network-wide monitoring impractical. We present HeteroSketch, a framework that consists of two main components: (1) a profiling tool that automatically quantifies the capabilities of arbitrary hardware by predicting their performance for sketching algorithms, and (2) an optimization framework that decides placement of measurement tasks and resource allocation for devices to meet monitoring goals while considering heterogeneous device capabilities. HeteroSketch enables optimized deployments for large networks (> 40,000 nodes) using a novel clustering approach and enables prompt responses to network topology, traffic, query, and resource dynamics. Our evaluation shows that HeteroSketch reduces resource overheads by 20−60% compared to prior art, while maintaining monitoring performance, coverage, and accuracy. 

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