Dynamic Estimation of CPU Demand of Web Traffic

Managing the resources in a large Web serving system requires knowledge of the resource needs for service requests of various kinds, and these needs may change over time. Assessing resource needs is commonly performed using techniques such as offline profiling, application instrumentation, and kernelbased instrumentation. Little attention has been given to the dynamic estimation of dynamic resource needs, relying only on external and high-level measurements such as overall resource utilization and request rates. We consider the problem of dynamically estimating dynamic CPU demands of multiple kinds of requests using CPU utilization and throughput measurements. We formulate the problem as a linear regression problem and obtain its basic solution. However, in practice one is faced with issues such as insignificant flows, collinear flows, space and temporal variations, and background noise. In order to deal with such issues, we present several mechanisms such as data aging, flow rejection, flow combining, noise reduction, and smoothing. We implemented these techniques in a Work Profiler component that we delivered as part of a broader system management product. We present experimental results from using this component in scenarios inspired by real-world usage of that product; our technique produces estimates that are roughly within a factor of 2 of the right answer, for the request flows that draw significant CPU power.