Optimization possibilities of program

For TCN traffic optimization purposes algorithms of shortest path and maximal flow were developed. As it was mentioned earlier, developed application allows calculating minimal paths between two nodes. Shortest path algorithms are based on known classical Dijkstra and Floyd algorithms [2] but some modifications were made taking features of TCN reflected in formulas (1)-(7) into consideration. In classical Dijkstra algorithm minimal path is calculated by building shortest path tree (SPT), nodes to which are added in order of increasing distance to them from initial node. In Floyd algorithm value is received from the appropriate matrix. Classical Dijkstra and Floyd algorithms are widely described in [2]. Modified in developed computer system shortest path algorithms take delays on nodes defined by reflection (6) into account.

In case of telecommunication networks we could find the shortest connection between some nodes. From the other side, this possibility could be used in GPS navigation for building optimal paths between cities (cities in this case are represented by vertexes of graph) or inside some city (vertexes of graph in this case should represent parts of streets).

Flow calculations are based on classical augmental way algorithm, known in literature as Ford-Fulkerson algorithm. It allows finding maximal flow that could be passed between current two nodes using all available at the moment connections. Developed computer system calculates value of maximal flow and shows its structure by highlighting appropriate connections. Running application on pocket PC calculating maximal flow in city telecommunication network is shown on fig. 4.

Fig. 4. Program Graph .NET on pocket PC. Calculating maximal flow inside city telecommunication network.

Fig. 5. Structure of simulated city telecommunication network. Screenshot from Graph .NET for desktop PCs.

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Now let’s demonstrate possibilities of program on some example.

Telecommunication network shown on fig. 5 consists of 2 central and 16 peripheral nodes. All central and 11 peripheral nodes form two circuits; other 5 nodes lie beyond them. Carrying capacity of conections inside circuits is equal to 2,5 Gbit/s, beyond them – 1 Gbit/s. In every moment of time some connections could be disabled. Delays on all nodes for simplicity are equal to 1 ms. Speed of signal passing is equal to the speed of light so delays on conections inside city could be ignored. Table 1 shows calculated maximal possible flow, shortest path and passing time for signal in different situations.Central nodes (“Central”) are marked with letter C, peripheral (“ATS”) – P.

First row in table shows situation when both nodes are situated inside circuits so maximal flow and minimal signal passing values received. Second and third rows show situation when one of connections which belong to previous shortest path is getting disabled. Program recalculates shortest path looking for available nodes but this increases signal passing time. Maximal possible flow decreases because quantity of possible paths between nodes has been decreased. Next two rows show the similar situation but in this case peripheral connections are bottleneck of the entire path so maximal possible flow is equal to carrying capacity of peripheral connections. Last row in table shows situation when peripheral connection is getting disabled. In this case program is unable to find alternative path and appropriate message is shown.

Source node Type of nodes in accordance with formula (1) Desti- nation node Disabled connections Signal passing time, ms Shortest path of signal Maximal possible flow, Gbit/s C1 Central C2 - 2,0 C1-P6-P2-C2 7,5 C1 Central C2 P6 – P2 6,0 C1-P8-P3-P1-P15- P14-C2 5,0 C1 Central C2 P6 – P2 P3 – P1 6,0 C1-P7-P10-P11- P13-P14-C2 2,5 P4 Peripheral P9 P6 – P2 3,0 P4-P5-C1-P7-P9 1,0 P4 Peripheral P9 C1 - P7 P13 – P11 10,0 P4-P5-C1-P6-P2- C2-P14-P13-P11- P10-P7-P9 1,0 P4 Peripheral P9 P5 – C1 - - -

Tab. 1. Calculating maximal flow and shortest path of signal in city telecommunication network.

Calculation time wasn’t considered in this table because it is less than minimal detectable by program value (0,1 ms). Initialization time was equal to 15 ms.

5. Conclusions

1. Created computer system for telecommunication networks simulation. A cross- platform application developed using language C# and object-oriented point of view. 2. Theoretical base for computer system development is presented.

3. Program has user-friendly interface and allows solving optimization problems for different kinds of networks. In case of telecommunication network nodes are represented by vertexes in graph and connections by edges.

4. Maximal flow and shortest path calculation results for main lines in city telecommunication network were presented on example and analyzed.

5. In real networks from different reasons some connections or nodes could be disabled. In developed application this feature was also taken into account and transportation flows could be redistributed.

6. Project is in stage of further development. Versions for desktop and mobile systems are improving simultaneously. Perspective branch of development is adapting project for GPS navigators and other route planners.

References

[1] Base Mathematical Theory of Telecommunication systems (edit by V.V.Popovsky).- Kharkiv-“SMIT company”, 2006.-P.564. (in Ukrainian).

[2] Sedgewick R. Algorithms in C++.Part 5.Graph algorithms. Princeton University- Diasoft,-Kiev: 2002.- 496pp.(in Russian).

[3] J. Richter. Applied Microsoft .NET Framework Programming. Microsoft Press, 2002, 556 pp.

Polish Teletraffic Symposium 2007 ISBN 978-83-926054-0-9

pp. 151–164

Traffic engineering for industrial networks

Michał Morawskia

a

Division of Computer Networks Technical University of Łódź

[email protected]

Abstract:

In the paper we present the results of our further work on the new method of the traffic engineering based on an adaptive multipath unidirectional routing based on the Minimum Delay Routing principle [10, 11, 30–33]. The routing problem considered in our work is focused on the traffic specific for industrial applications and low performance links, esp. wireless. In such situation the regular on-off low volume traffic is interlaced with the intensive stream and/or datagram traffic. The traffic specific for control of technological processes requires quite low bandwidth, reconciles with even large single (not clustered) data loss, but does not tolerate delays. These cause significantly different requirements than in typical networks. The paper extends previous results considering TCP traffic by maintaining paths and overall network stability in hard traffic conditions.

In the presented approach, we assume that the values of link costs in all links and all metrics are not known exactly, but we consider them as values with uncertainty. Such an approach, together with associated forwarding method allows to assimilate well known routing algorithms (typically different for wired and wireless parts of networks) to the behaviour close to optimal, and therefore, to obtain significantly shorter latencies, jitter, nearly no loses, better throughput for data flows, than in the case of usage pure standard or uniform algorithms.

The paper strictly extends the work published in [24].

Keywords: : Traffic Engineering, Routing, QoS, Industrial networks

1. Statement of the problem of the traffic engineering in industrial networks