Efficiently Measuring Bandwidth at All Time Scales

The need to identify correlated traffic bursts at various, and especially fine-grain, time scales has become pressing in modern data centers. The combination of Gigabit link speeds and small switch buffers have led to “microbursts”, which cause packet drops and large increases in latency. Our paper describes the design and implementation of an efficient and flexible end-host bandwidth measurement tool that can identify such bursts in addition to providing a number of other features. Managers can query the tool for bandwidth measurements at resolutions chosen after the traffic was measured. The algorithmic challenge is to support such a posteriori queries without retaining the entire trace or keeping state for all time scales. We introduce two aggregation algorithms, Dynamic Bucket Merge (DBM) and Exponential Bucketing (EXPB). We show experimentally that DBM and EXPB implementations in the Linux kernel introduce minimal overhead on applications running at 10 Gbps, consume orders of magnitude less memory than event logging (hundreds of bytes per second versus Megabytes per second), but still provide good accuracy for bandwidth measures at any time scale. Our techniques can be implemented in routers and generalized to detect spikes in the usage of any resource at fine time scales.