Half Cycling Dynamic Bandwidth Allocation with Prediction on EPON

Transcription

1 Half Cycling Dynamic Bandwidth Allocation with Prediction on EPON Özgür Can TURNA 1, M.Ali AYDIN 1, A.Halim ZAĐM 2, Tülin ATMACA 3 1 Department of Computer Engineering, Istanbul University, Istanbul, Turkey {ozcantur, aydinali}@istabul.edu.tr 2 Institute of Science and Engineering, Istanbul Commerce University, Istanbul, Turkey azaim@iticu.edu.tr 3 Department RST, Institut Telecom / Telecom SudParis, Evry, France tulin.atmaca@it-sudparis.eu Abstract This study is about a prediction approach for our previous dynamic bandwidth allocation algorithm for Ethernet Passive Optical Networks (EPON). Our previous work (hcdba) is based on half cycle timing for bandwidth allocation. That can be handled as a middle way between online and offline bandwidth allocation schemes. In PONs, prediction is used for bandwidth allocation to grant loaded nodes with early responses. However, due to the versatile nature of data traffic, prediction algorithms have a handicap to provide a better solution in classical approaches. In this study, a novel prediction approach integrated with hcdba algorithm described. Performance comparison of hcdba with & without prediction and IPACT algorithm is given. According to the simulation results, prediction on hcdba seems to give some performance improvements in terms of access-delay. Keywords-component; Prediction, Dynamic Bandwidth Allocation, EPON, hcdba I. INTRODUCTION Each day, the growth of internet usage and computer network is rising. It is a challenging race for service providers to represent more bandwidth to customers. However it can be costly to switch to a new technology in access networks. Besides, the DSL systems that are used over the twisted pair telephone lines have limitations. For future access networks, fiber solutions are the promising ones. Passive Optical Networks (PONs) coming to front because of its costeffectiveness. A PON system consists of an Optical Line Terminal (OLT) at central office, Optical Network Units (ONU) at subscribers and fiber lines connected by passive optical splitters. In PON, the transmission is done with broadcasting in downstream and a single line is used by multiplexing in upstream. Time division multiplexing (TDM) approaches for upstream bandwidth allocation is standardized. For TDM, there are two main standardization branches. The first one is Gigabit PON (GPON), which is a successor of Asynchronous Transfer Mode PON (APON), standardized by ITU-T. GPON uses GPON Encapsulation Method (GEM) framing in upstream and downstream direction, which is based G.7041 Generic framing procedure (specification for sending IP packets over SDH networks) [1]. Contrary to APON, which is originally created to carry ATM based transmission packet, GPON can carry ATM, Ethernet or any other TDM based packet type inside GEM frames. The second one is EPON, which is standardized by IEEE 802.3ah Task Force [2]. They have developed a PON architecture, which is based on standard Ethernet packing. Their approach to the solution for PON is that, in the near future all the carried traffic to subscribers will become IP traffic. Thus, usage of Ethernet in the first mile makes network design simple and easily understandable. They have published EPON standard in 2000 at 1Gbps up/down transmission capacity. By 2009, 10Gbps up/down transmission capacity has been standardized for EPON architecture [3]. For bandwidth allocation, EPON, which has only passive optical units in its infrastructure, comes front in cost and deliverability of service with high bandwidth and long haul access. In upstream direction, EPON needs a multiple access control mechanism to control the bandwidth allocation among Optical Network Units (ONUs) where Multi-Point Control Protocol (MPCP) is responsible for. EPON standard supports the controlling mechanism for bandwidth allocation but it leaves the bandwidth allocation mechanism for implementers. The scheme of bandwidth allocation in EPON can be either static or dynamic. In static allocation, a fixed-size transmission window is allocated by OLT to each ONU, regardless of the traffic requirements at each ONU. On other hand, in the dynamic allocation, a variable-size transmission window is dynamically allocated to different ONUs, taking into account their traffic demands that they reported. It is well known that by using dynamic allocation the bandwidth utilization can be increased. Dynamic bandwidth allocation on EPON is an open subject for implementers and researchers to work on. In our previous study [4], we have introduced a new dynamic bandwidth allocation algorithm for EPON. We have named it Half Cycling Dynamic Bandwidth Allocation (hcdba) due to its cycling mechanism. Under low traffic load, hcdba works like online DBA algorithms, on the contrary under high load it works like offline DBA algorithm. This rotation is done by a control mechanism in the algorithm at each report message arrival. In offline mode, the decision for bandwidth allocation is done only for half of the clients. In this paper, we present a prediction extension for bandwidth allocation scheme of hcdba and compare the performance of hcdba with prediction extension, hcdba without prediction extension and IPACT as a reference model /12/$ IEEE

2 This paper is organized as follows. In section II; Interleaved Polling with Adaptive Cycle Time (IPACT) algorithm and hcdba have been summarized. In section III; our new proposed prediction extension for hcdba is introduced. In section IV, our simulation environment is described and the simulation results are presented. Finally, section V concludes the paper. II. OVERVIEW OF IPACT AND HCDBA A. IPACT G. Kramer et al [5] propose the early solution for dynamic bandwidth allocation Interleaved Polling with Adaptive Cycle Time (IPACT). In [5], there are five different conditions that can be used over IPACT and according to the authors; the best variation of IPACT algorithm is to use a maximum window limit approach. In IPACT, OLT sends gate messages to the ONUs one by one in an interleaved fashion without waiting the next report message to arrive from other ONUs. This helps OLT to make the maximum usage of the upstream. However, if one of the ONUs have a huge packet tail in its buffer, then it can make a request which dominates the upstream channel for a long period of time. In this case, other ONUs start starvation for bandwidth and they are threatened in an unfair way. So, in IPACT, a maximum window size has been decided for maximum bandwidth allocation for an ONU in one cycle. The problem with IPACT algorithm is that, it is not able to distribute the free capacity of the upstream channel to the highly loaded ONUs in a cycle. If we increase the maximum window size, this can cause longer waiting time for packets in ONUs local buffers to be sent in next cycle. On the contrary, if we set the window size shorter, this will cause more GATE and REPORT transmission which bring extra overhead to the system. B. hcdba The hcdba algorithm, which we presented in our previous study [4], works in two modes according to the load of the upstream channel. In low loads the algorithm switch into online DBA mode that is similar to IPACT algorithm and in high loads it switches into offline DBA mode. The working mode changes respectively according to the incoming upstream bandwidth demands to the OLT. In offline DBA (Interleaved Polling with Stop) algorithm, if OLT is able to know the bandwidth demands from entire ONUs before idle period start time (T idle_start ) as earlier as the length of idle period T idle (T computation + RTT ), GATE messages can be sent without any idle period in upstream channel. In hcdba, to send GATE messages, OLT calculates the bandwidth amount to be given for half of ONUs instead of entire list, if more than half of ONUs have reported their demands after the last gated ONU. Otherwise, OLT directly sends a GATE message to the following ONU in polling list. In this case, OLT is in online DBA mode which will continue until number of reported ONUs is more than half of the total. When reported ONU count reaches this amount, OLT again starts working in offline DBA mode and jump in half cycle algorithm to distribute bandwidth among ONUs. The algorithm switches into online DBA mode when the OLT cannot collect enough REPORT messages. If time slots for ONUs are so short, the upstream channel is lowly loaded. Thereby, in online mode we do not have to take into consideration about fair distribution because we know that the system have a cycle time below the desired cycle time limit. The maximum window size can be held much bigger than IPACT (limited) approach. Even the cycle time becomes longer; the algorithm changes its form to offline DBA that is supposed to give a fair bandwidth distribution among ONUs in a cycle. III. PREDICTION EXTENSION FOR HCDBA In this approach, the prediction mechanism is processed by OLT, because any change in the ONU part is undesirable. In hcdba algorithm, the prediction takes place in online mode, since the traffic from other ONUs is not too much and the channel is not overloaded in online mode. Thereby we can give some extra grant to the next ONU according to its request while we are not behaving unfair to other ONUs [6]. Parameter definitions used in the algorithm is given in Table I. The prediction amount for an ONU is added to its requested window size and the grant for next cycle is send to the ONU in gate messages as usual. In this case, if the prediction amount plus the requested size exceeds the B max value the grant size is limited at the B max value. Parameter G R P B max H α k TABLE I. In the n th cycle; PARAMETER DESCRIPTIONS Description Grant Size - The granted window that OLT gives to an ONU in a GATE message. Request Size - ONU s demanded window size in the report message. Prediction Size - The predicted extra window size which will be added to the Request. Maximum Bandwidth - Maximum given bandwidth for each ONU in a cycle ( 15KB for 1Gbps ) Mean value of last 10 (j) grants given to an ONU. prediction weight value for history (0.5 taken) Weight value for prediction rate, initially taken as k=3 (means prediction will be %33 of the request) Gn = n+ n + < n + The prediction amount is affected by the amount of ONU s previous grants. In our algorithm, we consider the mean value of the last 10 grants that the OLT grants for the related ONU. = The predicted amount is affected by α of the mean of previous grants and (1-α) of the last request. The predicted amount is taken as proportion of k of the calculated value. The k value is a weight variable to regulate the predicted amount according to the miss of the previous requests and grants. (1) (2) /12/$ IEEE

3 The predicted amount is calculated as; = Each time of the prediction for the next queue size in the ONU, there is a big possibility to miss the exact value of the queue size. Therefore, the prediction mechanism has the possibility to give wrong decisions about the queue size under arbitrary traffic behavior. In such condition if the prediction is made much more or equal to the queue size of the ONU, the following request will be zero. If the prediction is made less than the queue size the OLT may take a request from the concerning ONU which will be more than zero. Therefore, according to the new request and last grant ratio, the k value can be regulated to change the OLT s prediction amount for the new cycle. >0 > 0 > = 1 >0 > 0 < +1 =0 If k n+1 is below one then it is rounded to one or if it is more than 100 the algorithm rounds it to 100. This limitation is to keep the k value between reasonable values. If the prediction value is smaller than a predetermined value (such as 64Bytes) then the algorithm eliminates the prediction amount in the grant calculation since the Ethernet packets cannot be smaller. This arrangement decreases the unusable grants. If no data packet arrives to the OLT in the last cycle from an ONU and the requested window size is zero then prediction is not made for this ONU in that cycle because this means that the ONU pass to an idle position and with prediction, it is possible to give an unnecessary grant. IV. PERFORMANCE EVALUATION In this section, we present simulation results to verify our analysis and demonstrate the performance of proposed prediction extension to hcdba algorithm. We compare the results obtained from the hcdba algorithm with and without prediction versions and IPACT algorithm. We use the same scenario for each algorithm on the simulation. We consider an EPON access network consisting of 16 ONUs connected to an OLT through a passive coupler. All ONUs are assigned a downstream and an upstream propagation delay (from ONU to OLT). The distance between the coupler and OLT and distances between ONUs and the coupler are fixed to 10 km (about 0.05 ms). We compare the algorithms in two cases; 1Gbps upstream channel for EPON and 10Gbps upstream channel for 10G-EPON. The algorithms are compared under four different priority classes described in Table II. For Premium and Silver traffic, we use CBR (Constant Bit Rates) sources. To generate self-similar traffic of Ethernet LAN (Bronze and Best Effort BE classes), we use an (3) (4) aggregation of multiple sources of Poisson Pareto Burst Process (PPBP), so called Pareto-distributed ON-OFF [7]. TABLE II. TRAFFIC HYPOTHESIS [8] CoS1 Premium CoS2 Silver CoS3 Bronze CoS4 BE Traffic Ratio 10 % 10 % 30 % 50 % Packet size (in Bytes) ,500, ,500,1500 Source and Burstiness CBR CBR PPBR/ µ=1.4 PPBP/µ=1.4 Burst Length (# of Packets) CBR CBR TABLE III. SIMULATION PARAMETERS Parameter Value(1Gbps) Value(10Gbps) No. of ONUs Upstream Bandwidth, R 1 Gbit/s 10 Gbit/s Maximum cycle time for 2ms 2ms hcdba and odba Maximum transfer window 15 KB (IPACT) size for IPACT and hcdba 30 KB (hcdba) Guard Time 5µs 5µs 150 KB (IPACT) 300 KB (hcdba) In simulation, we used a two-leveled queue approach for QoS traffic classes. Each QoS class packets buffered in separate queues in first level. Packets from all QoS classes that are waiting for a window in the following cycle are stored in a second level queue. When a new GATE message arrives to the ONU according to the granted window, the packets in second level queue are firstly scheduled and the packets in first level queues are scheduled from high priority to lower ones. After that, the packets buffered in first level queues are transferred to second level queue and a new window is demanded from OLT according to the second level queue length. By the use of twoleveled queue, we eliminate high priority packets to dominate the upstream bandwidth. The results are given under mono-service and multi-service conditions. The mono-service results are given to analyze the overall performance o the proposed approach easily. Simulations were done using discrete event network simulation tool (ns2.34). Table III shows the simulation parameters for each algorithm. p-hcdba refers to hcdba algorithm with prediction extension. Figure 1 shows mean access delay values for mono-service traffic of both cases (1Gbps, 10Gbps). hcdba algorithm with or without prediction extension give lower access delay values in all offered loads. Prediction extension makes an improvement in delay performance compared to pure hcdba algorithm. When the load increases, the prediction can significantly decrease the queue lengths in ONUs. If we compare the 1Gbps case with 10Gbps case, by the use of huge bandwidth capacities, prediction of the upstream traffic gets much more importance for decreasing the queue delays. In 10Gbps case, the decrease of access delay of p-hcdba seen obviously /12/$ IEEE

4 Figure 1. Mean Access Delay with Mono-Service Traffic 1Gbps 10Gbps Figure 2. Mean Access Delay with Multi-Service Traffic /12/$ IEEE

5 Figure 2 shows mean access delay values in 1Gbps Case and 10Gbps Case for multi-service traffic. In these graphs, results are given according to different service classes (CoS1 refers to the highest priority). In each case, initial three graphs show the result of an algorithm for different service classes and comparison of algorithms for CoS1 (highest priority) and CoS4 (best effort) are given in the following graphics. As it can be seen in the graphics, p-hcdba has a definite improvement in access delay values for each service class. Especially in high priority class, prediction has the greatest impact in access delay values. In two-leveled queue approach, after sending the packets in second level queue, first level queues are scanned from highest to lowest. By using prediction, extra window size is firstly used by the highest priority packets. V. CONCLUSION In this article, we proposed a prediction extension to our previous dynamic bandwidth allocation algorithm (hcdba) for EPON. Our aim is to improve our algorithms performance by decreasing the queuing delay of packets in ONUs local buffers. Prediction is used for early window reservation. It is known that under the arbitrary traffic, prediction of the traffic is a difficult task. False drops in window sizing are possible handicaps in prediction schemes. In our approach, by the advantange of load aware behavior of hcdba algorithm, it is to balance prediction decision. The performance evaluation results also show the advantage of new prediction scheme. p- hcdba improved the performance in terms of access delay. In this study, we saw that when the bandwidth increases the prediction approached has more impact of the overall performance. For further studies, early bandwidth allocation mechanisms should take more attention. ACKNOWLEDGMENT This work was supported by Scientific Research Projects Coordination Unit of Istanbul University (Project number 11793) and partially by the European EURO-NF project. This work is also a part of ongoing PhD thesis titled Next Generation Optical Access Networks at Istanbul University, Institute of Physical Sciences. REFERENCES [1] I. Cale, A. Salihovic, M. Ivekovic, Gigabit Passive Optical Network- GPON, Proceedings of the ITI th Int. Conf. on Information Technology Interfaces, June 25-28, 2007, Cavtat, Croatia [2] IEEE 802.3ah task force home page [Online]. Available: [3] G. Krammer, 10G-EPON: Drivers, Challenges, and Solutions, ECOC 2009, September, 2009 Vienna Austria. [4] O.C. Turna, M.A. Aydin, T. Atmaca, H. Zaim, A Novel Dynamic Bandwidth Allocation Algorithm Based on Half Cycling for EPON, EMERGING 2010, Florence, Italy, October [5] G. Kramer, B. Mukherjee, G. Pesavento, IPACT: a dynamic protocol for an Ethernet PON (EPON), IEEE Commun. Mag., 2002, 40, (2), pp [6] Özgür Can TURNA, M.Ali AYDIN, Tülin ATMACA, A.Halim ZAĐM, A Prediction Extension for Half Cycling Dynamic Bandwidth Allocation on EPON, Euro-NF International Workshop on Traffic and Congestion Control for the Future Internet (Euro-NF TCCFI 2011), March 31 April , Volos, Greece. [7] W. Willinger et al., Self-Similarity through High-Variability: Statistical Analysis of Ethernet LAN Traffic at the Source Level, Proc. ACM SIGCOMM 95, pp [8] T.D. Nguyen, T. Eido, T. Atmaca, An Enhanced QoS-enabled Dynamic Bandwidth Allocation Mechanism for Ethernet PON, Fift European Conference on Universal Multiservice Networks, ECUMN 2009, October 11-16, 2009, Sliema, Malta /12/$ IEEE