1. Implementation and simulation of Scheduling Algorithms in OPNET
Project by: Itamar Cohen
Supervisor: Nir Arad

2. AGENDA
Introduction
Applications
Algorithms: RR, WRR, WFQ
Summary

3. MOTIVATION Scheduling algorithms are used in:
Computer networks, operating systems, real time applications.

4. MOTIVATION (cont.) Uses for nowadays networks:
Suppliers of services are committed to guarantee a fixed service level, which can be checked under a few pre-defined parameters.
This is called SLA (Service Level Agreement).
But the client would like to get much more…
A possibility to choose his favorite
application, which will get a higher percentage of his bandwidth.
This is called QoS (Quality of Service).

5. SCHEDULING ALGORITHMS
Plenty of algorithms try to solve in different ways the problem of one server, which has to choose in real time the next client to be served, among a few clients.
Each algorithm is good for some case, but bad for other cases. No algorithm is good for all the possible scenarios.
The OPNET modeler gives us an excellent ability to test each algorithm under plenty of different scenarios.

6. THE PROJECT’S AIMS
This project implements OPNET standard packages for the following scheduling algorithms: RR, WRR and WFQ.
Each algorithm was simulated under a few
interesting scenarios.
The generic attitude of the implementation enables the user to simulate each algorithm under plenty of other scenarios.

7. Round Robin (RR) The simplest algorithm.
The most important parameters which were checked are:
ETE Delay
Backlog.

8. RR – basic 2

9. RR - Interesting scenarios
A source with smaller packets’ size
A source whose packets are smaller then those of the rest of the sources will get a worse service; The reason is that the RR is a packetized model, which doesn’t consider different packets’ sizes.

10. RR – different packets’ sizes

11. RR – different packets’ sizes (cont.)
The bad service causes increasing ETE Delay and backlog.
Only after a long run the server takes advantage of the smaller packets, and stops this increase.

12. RR - different packets’ sizes – ETE Delay

13. RR - different packets’ sizes - backlog

14. RR – another problematic scenario
Misses his turn
When one source is unlucky enough to send its packets just after its turn in the current round passed, it will have to wait for a whole round till it will be served.
When there are plenty of active sources, this wait time is not negligible at all.
The ETE Delay of the “unlucky” source is a few times bigger then that of the other sources.
The backlogs, however, are almost identical.

15. RR – “Misses his turn”

16. “Misses his turn” – ETE Delay

17. “Misses his turn” - backlogs

18. Weighted Round Robin (WRR)
A more sophisticated algorithm, which solves the problem of “one source faster”.
The user can promote different weights for different sources.

19. WRR

20. WRR – different packets’ sizes
If the user knows in advance the packets’ average size of each source, he can promote it to improve performances.
The normalized weight is, therefore:
(int)(promoted weight / avg. packet size)
Let us examine the effect on the scenario of one source with smaller packets’ size, demonstrated in the RR context.

21. WRR - different packets’ sizes (cont’)

22. WRR - different packets’ sizes (cont’)

23. WRR – Pros.
WRR solves the problem of one source, which sends smaller packets.
WRR guarantees a higher bandwidth for the favorite sources, still without starving the other sources.

24. WRR – Cons. The problem of “misses his turn” remains unsolved.
And what will we do if the average packet size is not known in advance (or has a large STDEV)?

25. WFQ – Weighted Fair Queuing
GPS – The ultimate choice!
We would have preferred to handle a bit-by-bit (rather then packetized) weighted RR fashion.
This is called Generalized Processor Sharing.
Unfortunately, this is impractical.
But we can approximate it.

26. From GPS to PGPS (WFQ)

27. Weighted Fair Queuing Overview
WFQ schedules the packets according to their finish time had they been handled by a GPS algorithm.
Parekh and Gallager had proved that WFQ’s performances are lower then that of GPS by only a small constant [P & G] .

28. WFQ in different scenarios
WFQ succeeds to give the same good results as WRR gave in the different packets’ sizes scenario, without requiring the user to promote the packets’ average sizes in advance.
WFQ succeeds to solve the problem of “Misses his turn”, in which both RR and WRR failed.

29. WFQ – not “Misses his turn” anymore !

30. WFQ – Cons.
The sophisticated scheduling requires higher computational complexity.
Jon C.R. Bennett and Hui Zhang showed that under one specific scenario WFQ’s service is far AHEAD of GPS. This will result in unstable and less efficient network control algorithm [B & Z] .
To solve these problems, newer algorithm, such as W2FQ, were implemented…

31. SUMMARY
As the demands from computer networks become more and more specific and complicated, more sophisticated algorithms are invented
Sophisticated algorithms are likely to give better performances, but they also require a higher computational complexity.
There are plenty of newer interesting algorithms.

32. REFERENCES
[P & G] Abhay K. Parekh and Robert G. Gallager. A Generalized Processor Sharing Approach to Flow Control in Integrated Services Networks: The Single-Node Case IEEWACM Transactions on nej’worjong, vol. 1, NO. 3, June Chuck Semeria. Supporting Differentiated Service Classes: Queue Scheduling Disciplines. Sridhar Iyer. Lectures slides for Autumn Semester, KR School of Information Technology, IIT Bombay. Queuing and Scheduling: Krishna Paul. Lectures slides for Autumn Semester, KR School of Information Technology, IIT Bombay. Scheduling: Kevin Fall. Lectures slides for spring 1999, UC Berkley, EECS COMMUNICATION NETWORKS, Supplementary notes on WFQ: [B & Z] Jon C.R. Bennett, Hui Zhang. WF2Q: Worst-case Fair Weighted Fair Queuing, Presented by Pin Zhou.