SCHEDULE: NOV 10-16, 2012
When viewing the Technical Program schedule, on the far righthand side is a column labeled "PLANNER." Use this planner to build your own schedule. Once you select an event and want to add it to your personal schedule, just click on the calendar icon of your choice (outlook calendar, ical calendar or google calendar) and that event will be stored there. As you select events in this manner, you will have your own schedule to guide you through the week.
Host Load Prediction in a Google Compute Cloud with a Bayesian Model
SESSION: Cloud Computing
EVENT TYPE: Papers
TIME: 2:00PM - 2:30PM
SESSION CHAIR: Manish Parashar
AUTHOR(S):Sheng Di, Derrick Kondo, Walfredo Cirne
ROOM:355-D
ABSTRACT:
Prediction of host load in Cloud systems is critical for achieving service-level agreements. However, accurate prediction of host load in Clouds is extremely challenging because it fluctuates drastically at small timescales. We design a prediction method based on Bayes model to predict the mean load over a long-term time interval, as well as the mean load in consecutive future time intervals. We identify novel predictive features of host load that capture the expectation, predictability, trends and patterns of host load. We also determine the most effective combinations of these features for prediction. We evaluate our method using a detailed one-month trace of a Google data center with thousands of machines. Experiments show that the Bayes method achieves high accuracy with a mean squared error of 0.0014. Moreover, the Bayes method improves the load prediction accuracy by 5.6-50% compared to other state-of-the-art methods based on moving averages, auto-regression, and/or noise filters.
Chair/Author Details:
Manish Parashar (Chair) - Rutgers University
Sheng Di - INRIA
Derrick Kondo - INRIA
Walfredo Cirne - Google
Click here to download .ics calendar file
Click here to download .vcs calendar file
Click here to add event to your Google Calendar
Host Load Prediction in a Google Compute Cloud with a Bayesian Model
SESSION: Cloud Computing
EVENT TYPE:
TIME: 2:00PM - 2:30PM
SESSION CHAIR: Manish Parashar
AUTHOR(S):Sheng Di, Derrick Kondo, Walfredo Cirne
ROOM:355-D
ABSTRACT:
Prediction of host load in Cloud systems is critical for achieving service-level agreements. However, accurate prediction of host load in Clouds is extremely challenging because it fluctuates drastically at small timescales. We design a prediction method based on Bayes model to predict the mean load over a long-term time interval, as well as the mean load in consecutive future time intervals. We identify novel predictive features of host load that capture the expectation, predictability, trends and patterns of host load. We also determine the most effective combinations of these features for prediction. We evaluate our method using a detailed one-month trace of a Google data center with thousands of machines. Experiments show that the Bayes method achieves high accuracy with a mean squared error of 0.0014. Moreover, the Bayes method improves the load prediction accuracy by 5.6-50% compared to other state-of-the-art methods based on moving averages, auto-regression, and/or noise filters.
Chair/Author Details:
Manish Parashar (Chair) - Rutgers University
Sheng Di - INRIA
Derrick Kondo - INRIA
Walfredo Cirne - Google
Click here to download .ics calendar file