An Approach to Flexible QoS Routing Active Networks

1. An Approach to Flexible QoS Routing Active Networks Proceedings of the Fourth International Workshop on Active Middleware Services(AMS’02) 謝志峰2002/11/14

2. Outline • Introduction • QoS Support in Active Networks • AQR (Active QoS Routing) operation • Simulation of AQR • Conclusions

3. Introduction(1/2) • Active Network(AN) are investigated since several years, attempting to satisfy the increasing needs of highly customizable protocol mechanisms. • AQR:The paper combines the concept of AN with suitable QoS routing mechanisms to form a novel approach called “Active QoS Routing(AQR)”.

4. Introduction(2/2) • Three major concepts and terms in the context of AN will be referred to frequently in the remainder: • Active Applications(AA): It denote user-provided communicating applications which make use of AN • Execution Environment(EE):The runtime system available on an AN node is coined as EE • NodeOS:The abstract machine on which all developer of customizations for an AN can rely is called NodeOS

5. QoS Support in Active Networks • Mechanisms which are usually associated with layers 3 or 4 : We find Active Congestion Control, which reduces the feedback delay for congestion control mechanisms by moving endpoint algorithms into the network. • Mechanisms which transfer application layer functionality into the network : Intelligent dropping of packets that correspond to specific frames of a video stream.

6. AQR (Active QoS Routing) operation (1/3) • 1.The AQR sender calculates all non- cyclic paths to the destination form the link state routing table. • 2. A probing packet carrying the QoS requirements, code for QoS calculation, the sender and receiver’s addresses and a list of visited nodes is sent to each first hop of these paths.

7. AQR operation (2/3) • 3. Upon receiving an AQR probing packet, an AQR-compliant transit node executes the AA code,which • Check if the minimum QoS requirements found in the packet can be met , • Compares and updates the QoS data, • Adds itself to the list of already visited nodes, and • executes the code of the AQR sender, starting at step2 ---- except that no probing packets are sent to the source or to any other already visited node.

8. AQR operation (3/3) • 4. Only packets which conform to the minimum QoS requirements reach the AQR receiver, where a list of valid paths is generated. After a predefined period, the best path is chosen and communicated to the sender

9. Simulation of AQR(1/10) • We performed two series of simulations with the “ns” network simulator. • In all of our simulators, the nominal bandwidth of all links was 1.5Mbit/s, packet sizes of all packets including measurement packets were 500 bytes. • Delay between probing packet ”waves” was set to approximately 2 RTTs , and we generally used a simulation time of 360 seconds.

10. Simulation of AQR (2/10) • The goal of Figure 1 was to study the behaviour of delay based AQR in a somewhat realistic scenario. • The sender was at node 9, the receiver was at node 45.

11. Simulation of AQR (3/10) • One such result is depicted in fig.2 . • We chose this scenario because it shows a significant delay reduction(approx. 20%) despite a number of path changes. Shortest Path AQR

12. Simulation of AQR (4/10) • We chose to use a somewhat less realistic but more controllable scenario by mean of a 15-node topology, which is shown in fig.3. • Using node 5 as a sender and node 13 as a receiver. • We studied the behaviour of AQR both with (greedy) TCP background traffic and exponentially distrially UDP background traffic.

13. Simulation of AQR (5/10) • Figure 4 shows the delay of a constant bit rate AQR stream with TCP background traffic. • AQR based on bandwidth measurements alone not only increases the average delay but also jitter. Bandwidth Shortest Path Delay

14. Simulation of AQR (6/10) • In table 1(TCP background traffic) delay increased by approx. 9% in comparison with shortest path routing, jitter increased by 44%. • In table 2(UDP background traffic) The throughput increased by 27% in comparison with shortest path routing.The average delay increased by 8% and jitter increased by 87%.

15. Simulation of AQR (7/10) Shortest Path • We now focus on a mixture (called”AQR-new”) of both parameters, where a delay threshold limits the choice of paths. • “AQR-old” denotes AQR solely relying on delay. AQR-new AQR-old AQR-new

16. Simulation of AQR (8/10) • There was no other drastic change in the delay or throughput results (see table 3) ; as could be expected, the average delay was notably 15%smaller than the average delay of shortest path routing (TCP background traffic).

17. Simulation of AQR (10/10) • Unresponsive background traffic yields a different result, which is depicted in figure 7. (UDP background traffic) Shortest Path AQR-new

18. Simulation of AQR • The main advantage of AQR-new with unresponsive background traffic lies in a throughput enhancement which was as high as 33% in our simulations. • This enhancement is due to a smaller packet loss ratio. The average delay was reduced by 36%.

19. Conclusions • We have proposed AQR as an approach to combing Active Networks with QoS routing. • In the variant finally proposed, AQR combines a consideration of both bandwidth and delay for finding optimal paths. • This variant showed considerable improvements over shortest-path routing under various load combinations and characteristics.

20. Future and related work • We can research related topic with Active Network. • We can plan to consider multi-domain routing. • We can research different topic with AQR.