Dynamic resource allocation for shared data centers using online measurements

Abhishek Chandra, Weibo Gong, Prashant Shenoy

Research output: Contribution to journalArticlepeer-review

110 Scopus citations


Since web workloads are known to vary dynamically with time, in this paper, we argue that dynamic resource allocation techniques are necessary to provide guarantees to web applications running on shared data centers. To address this issue, we use a system architecture that combines online measurements with prediction and resource allocation techniques. To capture the transient behavior of the application workloads, we model a server resource using a timedomain description of a generalized processor sharing (GPS) server. This model relates application resource requirements to their dynamically changing workload characteristics. The parameters of this model are continuously updated using an online monitoring and prediction framework. This framework uses time series analysis techniques to predict expected workload parameters from measured system metrics. We then employ a constrained non-linear optimization technique to dynamically allocate the server resources based on the estimated application requirements. The main advantage of our techniques is that they capture the transient behavior of applications while incorporating nonlinearity in the system model. We evaluate our techniques using simulations with synthetic as well as real-world web workloads. Our results show that these techniques can judiciously allocate system resources, especially under transient overload conditions.

Original languageEnglish (US)
Pages (from-to)381-398
Number of pages18
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
StatePublished - Dec 1 2003


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