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A High Performance Channel Sorting Scheduling Algorithm Based On Largest Packet

A High Performance Channel Sorting Scheduling Algorithm Based On Largest Packet. P.G.Sarigiannidis, G.I.Papadimitriou, and A.S.Pomportsis Department of Informatics, Aristotle University Thessaloniki, Greece. Some keywords for the presentation are:. Optical networks WDM technology

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A High Performance Channel Sorting Scheduling Algorithm Based On Largest Packet

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  1. A High Performance Channel Sorting Scheduling Algorithm Based On Largest Packet P.G.Sarigiannidis, G.I.Papadimitriou, and A.S.Pomportsis Department of Informatics, Aristotle University Thessaloniki, Greece

  2. Some keywords for the presentation are: • Optical networks • WDM technology • Broadcast and select star topology • Reservation phase • Transmission phase • Scheduling algorithm • Channel service • Traffic prediction

  3. Optical technology offers: • Huge bandwidth • Protocol transparency • Enhanced reliability • In some cases easy and simple protocol functionality

  4. Wavelength Division Multiplexing (WDM) offers: • Excellent way of exploiting the huge bandwidth of optical fibers • Concurrency among multiple users transmitting at feasible rates • Gigabit-per-second data rates in independent channels, which transmit concurrently data flows to a single or multiple users

  5. This algorithm focuses on broadcast and select LAN: • Local area network • Broadcast and Select topology • N nodes • W channels (wavelengths) • A passive star connects all nodes • A TT-FR system (a tunable transmitter and a fixed receiver per node)

  6. Medium Access Control (MAC) Protocols are: • Common policies for • Which node will transmit • On which channel will transmit • During which time the transmission will be executed

  7. MAC protocols are classified into: • Pre-transmission coordination based • They designate at least one wavelength for coordination-control reasons • Pre-allocation based • The available channels are used only for data sending and receiving • In a special category of pre-transmission coordination based the protocols are using control packets instead of a control channel

  8. The coordination process entails two steps: • The algorithm accepts initially the demands of all nodes and organizes them in transmission frames • The demands are stored in a demand matrix D=[di,j] • Each transmission frame stores for every node the number of timeslots required for transmission to a specific channel • Time is divided in timeslots

  9. Two very important prior scheduling algorithms are: • Online Interval Scheduling Algorithm (OIS) • K. M. Sivalingam, J. Wang, J. Wu and M. Mishra, An interval-based scheduling algorithm for optical WDM star networks, Photonic Network Communications, vol. 4, no. 1, (January 2002), pp. 73-87. • Predictive online Scheduling Algorithm (POSA) • E. Johnson, M. Mishra, and K. M. Sivalingam, Scheduling in optical WDM networks using hidden Markov chain based traffic prediction, Photonic Network Communications, vol. 3, no. 3, (July 2001), pp. 271-286.

  10. OIS has the following characteristics: • Begins the construction of the schedule just after reading the requests of the first node (online algorithm) • Each node needs to maintain a list of time intervals that are available on every data channel • For each node whose request is being processed nodes maintain one additional list of intervals that have not yet been assigned to the specific node for transmission

  11. POSA functions as follows: • POSA attempts to reduce the duration of the schedule computation process by predicting the nodes’ requests for the next frame • The predictor uses two different algorithms, the learning algorithm and the prediction algorithm • The learning algorithm is responsible for informing and updating the data of the history queue • The prediction algorithm is responsible for predicting the demand matrix as accurately as possible

  12. The new proposed protocol is called First Max Predictive Online Scheduling Algorithm (FM-POSA) • It tries to reduce the idle timeslots of the final schedule by changing the service order of the nodes • Concurrently it adopts the prediction mechanism of POSA in order to reduce the computation time of the schedule

  13. The algorithm operates in three independent phases: • The learning phase • During the first phase the FM-POSA monitors the traffic of the network and fills the history queues with the actual traffic demands. • The shifting phase • FM-POSA stops to construct the schedule based on the actual requests and it enters the prediction phase. • The prediction phase • In the last and most important phase the algorithm predicts the nodes’ requests for the next frame based on its learning phase. In addition the algorithm performs the forwarding of packets to their destinations.

  14. The new element that is introduced by FM-POSA is the order in which the nodes’ requests are processed. • FM-POSA searches for the largest packet and sorts nodes according to this. • The first node that is served is the one with the largest packet following by the one with the second largest packet and ending with the one with the smallest packet. • If two nodes have packets of equal size the node that will be served first is randomly selected.

  15. It would be useful to see an example of the processing of a demand matrix in order to understand the operation of the proposed algorithm: Let us consider the demand matrix D. D =

  16. Firstly FM-POSA acts as follows: • Action 1: Search and locate the largest packet from the requests of the nodes and save it in a vector called MAX. D = = MAX

  17. Next FM-POSA performs: • Action 2: Reorder the elements in MAX in descending order. MAX = = S_MAX

  18. The reordering of vector MAX means that the processing of requests in order to produce the scheduling matrix is dictated by the final vector S_MAX. Firstly, Node 2 will be processed S_MAX = Secondly, Node 1 will be processed Thirdly, Node 0 will be processed

  19. For the specific demand matrix D OIS/POSA will construct the following final schedule:

  20. For the specific demand matrix D FM-POSA will construct the following final schedule:

  21. For the specific example: • OIS/POSA wastes: • FM-POSA wastes:

  22. In the simulation results we consider: • Two network models. The first consists of 4 channels and a row of nodes (10, 20, 30, 40, and 50). The second consists of 8 channels and a row of nodes (10, 20, 30, 40, and 50). • N symbols the number of nodes, W symbols the number of channels, K is the maximum value for incoming packets.

  23. Also we consider: • The speed of the line has been defined at 2.4 Gbps per channel. • The tuning latency is considered to be equal to zero for simplicity reasons. • A 10% of the simulated frames belongs to the learning phase. • The two algorithms (FM-POSA and POSA) are compared under uniform traffic.

  24. In the channel utilization for 4 channelsFM-POSA is better than POSA

  25. In the network throughput for 4 channelsFM-POSA is better than POSA

  26. In the relation throughput-delay FM-POSA reduces a little the mean waiting time of the packets for 4 channels

  27. FM-POSA remains better than POSA in channel utilization for 8 channels

  28. In the network throughput for 8 channelsFM-POSA is better than POSA

  29. Again in the relation throughput-delay FM-POSA remains better than POSA for 8 channels

  30. Conclusions • We introduced a new scheduling algorithm for collision free WDM star networks. • The new scheme offers a better utilization of the available channels of the network. • Also, it brings an improvement in channel utilization and network throughput by changing the order of the processing of each node based on the largest request.

  31. Thank you for your attention!!!

  32. A New Channel Priority Scheduling Technique for WDM Star Networks P.G.Sarigiannidis, G.I.Papadimitriou, and A.S.Pomportsis Department of Informatics, Aristotle University Thessaloniki, Greece

  33. This algorithm focuses on broadcast and select LAN: • Local area network • Broadcast and Select topology • N nodes • W channels (wavelengths) • A passive star connects all nodes • A TT-FR system (a tunable transmitter and a fixed receiver per node)

  34. Three prior scheduling algorithms are: • Online Interval Scheduling Algorithm (OIS) • K. M. Sivalingam, J. Wang, J. Wu and M. Mishra, An interval-based scheduling algorithm for optical WDM star networks, Photonic Network Communications, vol. 4, no. 1, (January 2002), pp. 73-87. • Predictive online Scheduling Algorithm (POSA) • E. Johnson, M. Mishra, and K. M. Sivalingam, Scheduling in optical WDM networks using hidden Markov chain based traffic prediction, Photonic Network Communications, vol. 3, no. 3, (July 2001), pp. 271-286. • Check and sort-predictive online scheduling algorithm (CS-POSA) • P. G. Sarigiannidis, G. I. Papadimitriou, A. S. Pomportsis, CS-POSA A high performance scheduling algorithm for WDM star networks”, Photonic Network Communication, vol 11, no 3 (2006), pp. 209-225.

  35. The new proposed protocol is called Load Eclectic Navigated Algorithm (LENA) • The algorithm begins the manufacture of scheduling matrix with the channel that contains the most requests. That means that the algorithm does not chooses the channels from the first one to the last one, but selects each time the channel with the most time requests for the specific node. • Concurrently it adopts the prediction mechanism of POSA in order to reduce the computation time of the schedule

  36. It would be useful to see an example of the processing of a demand matrix in order to understand the operation of the proposed algorithm: Let us consider the demand matrix D. D =

  37. Firstly LENA acts as follows: • Step 1: First of all LENA adds each row of the traffic matrix D in a new vector S that will register the total amount of requests by each node: D = = S

  38. Next LENA performs: • Step 2: Then LENA grades vector S in a declining order: S = = S’

  39. It is examined in which channel have been requested most demands. • In the particular example we observe that in channel W2 have been assembled most requests for node N2 and the transmission time for them are equal to four timeslots. • Then are examined the other two channels and we observe that their demands are equal with 2 timeslots, so the selection is random.

  40. LENA continues with the set of demands, which belongs to node N0. Hence, the channel W0 will be selected (three timeslots), channel W1 could be follow (two timeslots), and LENA could be finish with node N0, by selecting the demands of W0 (two timeslot).

  41. Finally, LENA finishes the construction of the scheduling matrix, by servicing the requests of Node N1. Again, channel W2 (three timeslots) will be selected first, channel W1 (two timeslots) will follow and the last selection will be the channel W0 (one timeslot).

  42. For the specific demand matrix D OIS/POSA will construct the following final schedule:

  43. For the specific demand matrix D LENA will construct the following final schedule:

  44. In the simulation results we consider: • Two network models. The first consists of 8 channels and a row of nodes (10, 20, 30, 40, and 50). The second consists of 16 channels and a row of nodes (10, 20, 30, 40, and 50). • N symbols the number of nodes, W symbols the number of channels, K is the maximum value for incoming packets.

  45. Also we consider: • The speed of the line has been defined at 2.4 Gbps per channel. • The tuning latency is considered to be equal to zero for simplicity reasons. • A 10% of the simulated frames belongs to the learning phase. • The three algorithms (LENA, CS-POSA, and POSA) are compared under uniform traffic

  46. In the channel utilization for 8 channelsLENA is better than POSA and CS-POSA

  47. In the network throughput for 8 channelsLENA is better than POSA and CS-POSA

  48. In the relation throughput-delay LENA improves the network throughput and meanwhile reduces a little the mean time delay for 8 channels.

  49. In the channel utilization for 16 channelsLENA is better than POSA and CS-POSA

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