1. TECHNION – Israel Institute of Technology Department of Electrical Engineering The Computer Network Laboratory Crankback Prediction in ATM According to Statistical Measures Raviv Brueller & Ilia Koifman Supervised by Dr. Ofer Hadar

2. Background and Common Terms: Why Is the ATM So Special? ATM is a cell-based transport service designed to carry a wide variety of applications. The user specifies the Quality of Service (QoS) he expects to receive. Connection Oriented – Before starting a session, a stage of connection (VC) establishment is required Efficient utilization of network resources.

3. The Ordinary Crankback Mechanism When an ATM node discovers it cannot continue the setup of a virtual channel under the requested QoS, it initiates a backtracking procedure: the crankback. After cranking back, the network looks for an alternate route.

4. A Simple Example for Crankback

5. The Main Principle/Idea In each stage we cross another node towards the destination, and calculate the probability to fail in establishing the connection. If this probability exceeds a threshold we decide that a predicted crankback should be activated.

6. The Advantages of the Predicted Crankback Mechanism Lowers setup time. Reduces network ’ s load. Prevents resources from being wasted.

7. Implementing the Mechanism The additive resource examined: Delay. An initial quota is allocated for the setup. Each node advertises the pdf of the delay needed to cross it. Assuming independent pdfs, the pdf of the delay for traveling from the current node to the destination is calculated by convolution. After crossing a node we reduce the delay needed to cross it from the remained quota. The probability of failure is derived by integrating over the general pdf.

8. Calculating the Probability of Failure from the pdf 60708090100110120130140 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 x 10 -3 P_fail(j) pdf(j…N) left quota Delay P

9. The Single Route Case One path consisting of 10 nodes. The nodes ’ pdfs are independent, of the same type (Gaussian) but with different characterizing parameters (mean, variance). A time quota is allocated for each setup. S D

10. The Single Route Case: Algorithm If P_fail exceeds a predefined threshold, a predicted crankback is activated. Variations: constant/dynamic threshold. A dynamic threshold is a decaying vector of values representing the thresholds in the nodes along the routes.

11. An Example for Activating the Predicted Crankbak Mechanism Normal crankback Threshold crankback gain P_fail(j) 0246810 0 0.2 0.4 0.6 0.8 1 Predicted crankback

12. The Single Route Case: Representative Results(1) Mean Saved Transitions Mean Unused Transitions Const=0.765.400.37 Const=0.785.460.35 Const=0.805.120.38 Const=0.824.030.43 Decaying vector5.200.63 Threshold Results The results obtained for different thresholds and a time quota of 1300 time units.

13. The Single Route Case: Representative Results(2) Mean Saved Transitions Mean Unused Transitions Quota=14001.921.45 Quota=13004.030.43 Quota=12006.710.28 Quota=11007.350.10 Results Time Quota The results obtained for different amounts of time quota and a threshold of 0.82.

14. The Multi-Route Case Several mutual exclusive routes from source to destination. As before, each node advertises the pdf of the time needed to cross it. The pdfs are independent, but of the same type.

15. The Multi-Route Case: Algorithm Choose as an initial route the path with the lowest a-priori P_fail, and cross its first node. After crossing a node, check if the current route is still the optimal. If it still is, cross its next node. If not, crank back to the origin and start traversing the new optimal route. Repeat crossing nodes in the optimal route until either the destination is reached or the time quota is fully consumed.

16. Representative Results: The Multi-Route Case Crankback Mechanism Success Ratio No-Crankback Mechanism Success Ratio Crankback: Mean Time Used for Setup No Crankback: Mean Time Used for Setup Time Quota=1400 (time units) 10.521240.1 (time units) 1261.7 (time units) Time Quota=1300 (time units) 10.451219.3 (time units) 1266.8 (time units)

17. Conclusions and Remarks The implemented mechanism reduces the setup time and lowers the network ’ s load. The mechanism improves the network ’ s utilization – an important feature in ATM. As the initial allocated time quota gets smaller, the benefits obtained by implementing are better felt. The mechanism can be extended to hierarchical networks.

18. Thanks for Listening!