Towards a Cost vs. Quality Sweet Spot for Monitoring Networks - Yaseen, Nofel; Arzani, Behnaz; Chintalapudi, Krishna; Ranganathan, Vaishnavi; Vieira Frujeri, Felipe; Hsieh, Kevin; Berger, Daniel S.; Liu, Vincent; Kandula, Srikanth (HotNets 2021, 2021)
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 measurements from a production datacenter network. We also discuss the technical challenges that must be solved when applying these techniques in practice.