1st International ICST Conference on Performance Evaluation Methodologies and Tools

Research Article

Dynamic estimation of CPU demand of web traffic

  • @INPROCEEDINGS{10.1145/1190095.1190128,
        author={Giovanni  Pacifici and Wolfgang  Segmuller and Mike  Spreitzer and Asser  Tantawi},
        title={Dynamic estimation of CPU demand of web traffic},
        proceedings={1st International ICST Conference on Performance Evaluation Methodologies and Tools},
  • Giovanni Pacifici
    Wolfgang Segmuller
    Mike Spreitzer
    Asser Tantawi
    Year: 2012
    Dynamic estimation of CPU demand of web traffic
    DOI: 10.1145/1190095.1190128
Giovanni Pacifici1,*, Wolfgang Segmuller1,*, Mike Spreitzer1,*, Asser Tantawi1,*
  • 1: IBM T.J. Watson Research Center, P.O. Box 704, Yorktown Heights, NY, USA.
*Contact email: giovanni@us.ibm.com, werewolf@us.ibm.com, mspreitz@us.ibm.com, tantawi@us.ibm.com


Managing the resources in a large Web serving system requires knowledge of the resource needs for service request-s 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 kernel-based 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.