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

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

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

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

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

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

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

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

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

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