Dynamic resource allocation for shared data centers using online measurements

Abstract

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 time-domain 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.

A Pattern-Based Prediction Model for Dynamic Resource Provisioning in Cloud Environment

“…To deal with time-varying workloads, more recent work applied adaptive control theory, in which models were automatically adapted to changes using online system identification. Model-based research efforts [28] [29] [30] [31] [32] have been trying to model computer systems from different perspectives. Bennani et al [33] predicts the response time and throughput for both online and batch workloads using multiclass open queuing networks.…”

“…Liu et al [34] used AR models to map CPU entitlement to the mean response time with a fixed workload. Chandra et al [28] modeled the resource using a time-domain queuing model which relates the resource requirements to its workload. Some of these approaches made simplifying assumptions, such as using a single queue to model the whole system, which could fail to capture complexities of the relationship between application workload and resource usage.…”