Multi-Resource/Multi-Dimension QoS Optimization

Q-RAM Overview

The QoS-based Resource Allocation Model (Q-RAM) is an analytical approach for satisfying multiple quality-of-service dimensions in a resource-constrained environment. Using this model, available system resources can be apportioned across multiple applications such that the net utility that accrues to the end-users of those applications is maximized.

Application Context

Multimedia systems using audio and video streams can provide better audio/video quality at higher resolution and/or very low end-to-end delays. Tracking applications can track objects at higher precision and accuracy if radar tracks are generated and processed at higher frequencies. In many cases, computationally intensive algorithms can provide better results than their less-demanding counterparts. Even interactive systems can provide excellent response times to users if more processing and I/O resources are made available. Conversely, many applications can still prove to be useful and acceptable in practice even though the resources needed for their maximal performance are not available. For instance, a 30 frames/second video rate would be ideal for human viewing, but a smooth 12 fps video rate suffices under many conditions. The QoS-based Resource Allocation Model (Q-RAM) addresses the following question: "how does one allocate available resources to multiple concurrent applications?".

Q-RAM Novelty

The novelty of Q-RAM is that it allows multiple Quality of Service requirements such as timeliness, cryptography and reliable data delivery to be addressed and traded off against each other. It also allows resources to be traded off against each other to obtain the same level of QoS along a particular dimension. For example, video at a certain frame rate can be transmitted in raw form, consuming minimal CPU cycles and high network bandwidth. Alternatively, the video can be compressed, consuming significant CPU cycles but consuming less network bandwidth. Q-RAM provides a framework to make such resource and QoS tradeoffs across multiple applications. Both discrete and continuous QoS dimensions have been studied.

Visual Q-RAM

Installing the Tcl Plugin and Amaranth Policy

In order to run Visual Q-RAM, you will need to download and install the tcl/tk plug-in from Scriptics. You will also need to create and enable an "amaranth" policy for the plugin. This will allow the plugin to create a connection to the QoS Optimization Server. Follow the steps bellow to enable the policy:
  • Locate and change directories to the policy configuration directory for the Tcl Plugin. On Unix systems this is typically "~/.netscape/tclplug/2.0/config", on Windows systems it is typically "C:\tclplug\2.0\config".
  • Copy the file "outside.cfg" to the file "amaranth.cfg".
  • Edit the file "amaranth.cfg", change the lines:
       section hosts ports
       disallow * *
       section hosts ports
       allow "" *
  • To enable the Amaranth policy edit the file plugin.cfg, add the line:
              allow amaranth
    somewhere after the "section policies" line, but before the next section.
Visual Q-RAM is a tcl/tk applet (tclet) for the visual presentation of multi-dimensional QoS optimization problems and their solutions. Problems can be entered manually through the interface, or loaded from a file. By communicating with the Q-RAM server, an optimial solution to the displayed problem can be obtained and displayed.

The interface is comprised of nine main sections. These sections and their functions are:

Select an interface size to start Visual Q-RAM:

Small interface for monitor resolutions up to 1024x780

Large interface for monitor resolutions 1280x1024 or larger


*Chen Lee
"On Quality of Service Management"
Ph.D. thesis, Carnegie Mellon University, August 1999
Also in Technical Report CMU-CS-99-165
Abstract Postscript Adobe PDF Citation

* Chen Lee, John Lehoczky, Dan Siewiorek, Raj Rajkumar and Jeff Hansen
"A Scalable Solution to the Multi-Resource QoS Problem"
To appear in the 20th IEEE Real-Time Systems Symposium, December 1999.
Abstract Postscript Adobe PDF Citation

* Chen Lee, John Lehoczky, Raj Rajkumar and Dan Siewiorek
"On Quality of Service Optimization with Discrete QoS Options"
In Proceedings of the IEEE Real-time Technology and Applications Symposium , June 1999
Abstract Postscript Adobe PDF Citation

* Raj Rajkumar, Chen Lee, John Lehoczky and Dan Siewiorek
"Practical Solutions for QoS-based Resource Allocation Problems"
In Proceedings of the IEEE Real-Time Systems Symposium, December 1998
Abstract Postscript Adobe PDF Citation

* Raj Rajkumar, Chen Lee, John Lehoczky and Dan Siewiorek
"A Resource Allocation Model for QoS Management"
In Proceedings of the IEEE Real-Time Systems Symposium, December 1997
Abstract Postscript Adobe PDF Citation

Jeffery Hansen
Last modified: Sat Oct 23 13:44:38 EDT 1999