Elastically Scaling Control Channels in Network Measurement With Escala

In network measurement, data plane switches measure traffic and report events (e.g., heavy hitters) to the control plane via control channels. The control plane makes decisions to process events. However, current network measurement suffers from two problems. First, when traffic bursts occur, massive events are reported in a short time so that the control channels may be overloaded due to limited bandwidth capacity. Second, only a few events are reported in normal cases, making control channels underloaded and wasting network resources. In this paper, we propose <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\textsf {Escala}$ </tex-math></inline-formula> to provide the elastic scaling of control channels at runtime. The key idea is to dynamically migrate event streams among control channels to regulate the loads of these channels. <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\textsf {Escala}$ </tex-math></inline-formula> offers two components, including an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\textsf {Escala}$ </tex-math></inline-formula> monitor that detects scaling situations based on realtime network statistics, and an optimization framework that makes scaling decisions to eliminate overload and underload situations. We have implemented a prototype of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\textsf {Escala}$ </tex-math></inline-formula> on Tofino-based switches. Extensive experiments show that <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\textsf {Escala}$ </tex-math></inline-formula> achieves timely elastic scaling while preserving high application-level accuracy.