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Heuristics are ubiquitous in computer systems. Examples include congestion control, adaptive bit rate streaming, scheduling, load balancing, and caching. In some domains, theoretical proofs have provided clarity on the conditions where a heuristic is guaranteed to work well. This has not been possible in all domains because proving such guarantees can involve combinatorial reasoning making it hard, cumbersome and error-prone. In this paper we argue that computers should help humans with the combinatorial part of reasoning. We model reasoning questions as ∃∀ formulas [1] and solve them using the counterexample guided inductive synthesis (CEGIS) framework. As preliminary evidence, we prototype CCmatic, a tool that semi-automatically synthesizes congestion control algorithms that are provably robust. It rediscovered a recent congestion control algorithm that provably achieves high utilization and bounded delay under a challenging network model. It also found previously unknown variants of the algorithm that achieve different throughput-delay trade-offs. 

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. 

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. 

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. 

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.