Continuously monitoring a wide variety of performance and fault metrics has become a crucial part of operating large-scale datacenter networks. In this work, we ask whether we can reduce the costs to monitor - in terms of collection, storage and analysis - by judiciously controlling how much and which measurements we collect. By positing that we can treat almost all measured signals as sampled time-series, we show that we can use signal processing techniques such as the Nyquist-Shannon theorem to avoid wasteful data collection. We show that large savings appear possible by analyzing tens of popular measurement systems from a production datacenter network. We also discuss some challenges that must be solved when applying these techniques in practice.

Incident routing is critical for maintaining service level objectives in the cloud: the time-to-diagnosis can increase by 10x due to mis-routings. Properly routing incidents is challenging because of the complexity of today's data center (DC) applications and their dependencies. For instance, an application running on a VM might rely on a functioning host-server, remote-storage service, and virtual and physical network components. It is hard for any one team, rule-based system, or even machine learning solution to fully learn the complexity and solve the incident routing problem. We propose a different approach using per-team Scouts. Each teams' Scout acts as its gate-keeper --- it routes relevant incidents to the team and routes-away unrelated ones. We solve the problem through a collection of these Scouts. Our PhyNet Scout alone --- currently deployed in production --- reduces the time-to-mitigation of 65% of mis-routed incidents in our dataset.

When monitoring a network, operators rarely have a finegrained and complete view of the network's state. Instead, today's network monitoring tools generally only measure a single device or path at a time; whole-network metrics are a composition of these independent measurements, i.e., an afterthought. Such tools fail to fully answer a wide range of questions. Is my load balancing algorithm taking advantage of all available paths evenly? How much of my network is concurrently loaded? Is application traffic synchronized? These types of concurrent network behavior are challenging to capture at fine granularity as they involve coordination across the entire network. At the same time, understanding them is essential to the design of network switches, architectures, and protocols.

Wireless LANs (WLANs) carry a diversemix of traffic, ranging from delay-sensitive real-time applications to bulk transfers. Using existing QoS mechanisms in high speed WLANs (e.g., 802.11n/ac) presents a tradeoff between maximizing the performance of real-time applications and achieving high throughput. We propose SlickFi, a service differentiation scheme for high-speed WLANs that addresses this tradeoff by leveraging existing dual radio WiFi access points (APs). SlickFi opportunistically adapts channel width on a per-frame basis based on application demands and communicates traffic information across radios to control aggregate frame sizes for improving channel efficiency. SlickFi can be readily deployed on commodity devices using only driver-level changes at the AP-side. We implemented SlickFi in the ath9k driver and using real testbed experiments, show that it can improve aggregate throughput by up to 1.8x and 2.2x over 802.11n and 802.11e, respectively and the PSNR of 1080p videos by up to 3.4x and 1.7x over 802.11n and 802.11e, respectively.