University of Utah Research Foundation

Joe H Novak

Sneha Kumar Kasera

Ryan Stutsman

Improved techniques for dynamically responding to a fluctuating workload. Resources are reactively scaled for memory-intensive applications and automatically adapted to in response to workload changes without requiring pre-specified thresholds. A miss ratio curve (MRC) is generated for an application based on application runtime statistics. This MRC is then modeled as a hyperbola. An area on the hyperbola is identified as satisfying a flatten threshold. A resource allocation threshold is then established based on the identified area. This resource allocation threshold indicates how many resources are to be provisioned for the application. The resources are scaled using a resource scaling policy that is based on the resource allocation threshold.

Auto-scaling cloud-based memory-intensive applications

A method for calculating a drop probability can comprise determining, based on measurements within a network architecture of the computer system, a current packet delay and current link utilization within network architecture. The method can also comprise predicting a change in the packet delay within the network architecture at a predefined time interval in the future. Additionally, the method can comprise predicting a change in the link utilization within the network architecture at a predefined time interval in the future. Further, the method can comprise computing a drop probability that will ensure that the ratio of the predicted change in the packet delay over the predicted change in the link utilization approximates an predetermined ideal.

Growing community of inventors

Sandy, UT, United States of America

Joe H Novak