1. By Krishnamurthy et Al. Presented by David Girsault
Integration of QoS-enabled Distributed Object Computing Middleware for Developing Next-Generation Distributed Applications
By Krishnamurthy et Al.
Presented by David Girsault

2. Introduction Distributed frameworks
Beginning => improve inter-operability
Now => demand for better QoS
What needs for resource management?
Before run-time => already exists
During run-time => research

3. Overview of the Adaptive QuO-Middleware
QuO = Quality Objects
Allows specifying
QoS requirements
Control and monitoring information
Behavior for dealing with run-time problems
RPC  contract + QoS guaranty
Middleware in charge of QoS respect

4. Unified Middleware-Centric Qos API
Many != QoS frameworks
Standardization necessary
Need for an API for QoS
Separation between application and protocol
Translate QoS parameters to the network
Notification system for environment change

5. ACE QoS API Two implementations: Windows, Unix
Extensibility: easy to add network protocols
Portability: easy to modify for a platform
Event Notification: when QoS change
QoS capabilities: resource reservation system

6. Application to the CORBA Audio/Video Streaming Service
QoS parameters:
Application class (interactive or not)
Media types (audio, video…)
Adaptation policies (coeff. of compression)
CORBA A/V streaming service:
Flexible and portable  CORBA
Efficient low-level transport

7. The TAO A/V Streaming Service QoS Framework
QoS Mapping:
translation between network-level and application-level
QoS Monitoring:
application adaptation to resources at run-time
QoS-Based transport API:
Guarantees required bandwidth

8. Shipboard Dissemination of Unmanned Air Vehicle Video
MPEG data path:
UAV
Onboard distribution center
Video display host

9. Adaptation and Performance Under Load
Adaptation vs Non-adaptation
Testing  overloading distribution center
A/V Streams vs Ad-Hoc TCP
Results: similar performance
Time latency irregular with A/V streams

10. Conclusion QoS-enabled middleware: Resource variation at run time
Decreases time and effort for QoS requirements
Increases modularity and portability
Resource variation at run time
System response improvement
Better service to end users