TRAFFIC GROOMING WITH BLOCKING PROBABILITY REDUCTION IN DYNAMIC OPTICAL WDM NETWORKS

Transcription

1 TRAFFIC GROOMING WITH BLOCKING PROBABILITY REDUCTION IN DYNAMIC OPTICAL WDM NETWORKS K.Pushpanathan 1, Dr.A.Sivasubramanian 2 1 Asst Prof, Anand Institute of Higher Technology, Chennai Prof & Head, St Joseph s College of Engineering, Chennai Abstract: An optical network is a data network built on fibre-optics technology which sends data digitally as light. It offers an enormous increase in both transmission capacity and speed from traditional networks. The migration from electronic to optical transmission technology was achieved by only replacing copper cables with optical fibers. An optical fiber is capable of providing bandwidth in the order of terahertz with high reliability. Wavelength-division multiplexing divides the available fiber bandwidth into multiple smaller bandwidth units called wavelengths. Each wavelength channel has a bandwidth of few gigabits per second. But the requirement of each network traffic demand is normally lower than the capacity provided by a wavelength channel. Therefore multiple low rate traffic demands are usually multiplexed together to share a high speed wavelength channel during transmission. This process is known as Traffic grooming. The maximum number of traffic demands that can be multiplexed is known as grooming factor. Traffic grooming is two types. These are static and dynamic grooming. A connection request is not served within a time period by using RWA, it is rejected. This is treated as a blocked call. Priority based RWA has been focused to reduce the number of blocked connections. But this scheme gives best result if the connection requests are having the same source destination pair. In this paper we propose adaptive routing algorithm for routing, distributed relative capacity loss (DRCL) algorithm for wavelength assignment. In adaptive routing, the route from a source node to a destination node is chosen dynamically, depending on the network state. In Distributed Relative Capacity Loss (DRCL), on which wavelength total capacity loss is more, that is assigned to the lightpath. The objective is to reduce the blocking probability, and for maximum utilization of available wavelength capacity compare to priority based RWA scheme. Index Terms: Optical Networks, Traffic Grooming, Adaptive Routing, Wavelength Division Multiplexing. 1. INTRODUCTION Wavelength routing together with wavelength division multiplexing (WDM) technology is a strong candidate for next generation high performance networks because it provides high bandwidth, low bit error rate, low power requirements and low cost. WDM technology has provided tremendous bandwidth for optical fibers by allowing simultaneous transmission of traffic on many non-overlapping channels (wavelengths) in an optical fiber. A message is sent from one node to another using wavelength continuous route called lightpath, without requiring any optical-electronic-optical conversion and buffering at the intermediate nodes. Given a set of connection requests, the problem of establishment of a lightpath for each connection request by selecting the optimal route and assigning a suitable wavelength is known as routing and wavelength assignment (RWA) [1,2]. Typically, connection requests may be of three types: static, incremental, and dynamic [3]. With static traffic, the entire set of connections is known in advance, and the problem is then to set up lightpaths for these connections in a global fashion while minimizing network resources such as the number of wavelengths or the number of fibers in the network. The RWA problem for static traffic is known as the Static Lightpath Establishment (SLE) problem. In the incremental traffic case, connection requests arrive sequentially, a lightpath is established for each connection, and the lightpath remains in the network indefinitely. For the case of dynamic traffic, a lightpath is set up for each connection request as it arrives, and the lightpath is released after some finite amount of time. The objective in the incremental and dynamic traffic cases is to set up lightpaths and assign wavelengths in a manner that minimizes the amount of connection blocking, or that maximizes the number of connections that are established in the network at any time. This problem is referred to as the Dynamic Lightpath Establishment (DLE) problem. Majority of connection requests are in the Mbps range and a single wavelength channel in a WDM of 100Gbps in a commercially available system. A new opportunity has opened up in the form of traffic grooming. It is only possible to incorporate a traffic grooming [4-8] mechanism with an RWA approach where a number of low speed connection requests are multiplexed onto a high capacity wavelength channel to enhance overall channel utilization. During RWA if a connection request is not served within a time period (called the holding time) by using RWA, it is rejected. This is treated as a blocked call. Our objective is mainly for the reduction of this call blocking. Routing and wavelength assignment is Volume 2, Issue 4 July August 2013 Page 355

2 a hard problem; it can be simplified by decoupling the problem into two separate sub problems: the routing sub problem and the wavelength assignment sub problem. The main techniques for routing are fixed, fixed alternative, adaptive routing. The techniques for wavelength assignment are Random, First-Fit, Least- Used/SPREAD, Most Used, Min- Product, Least Loaded, MAX-SUM, Relative Capacity Loss, Wavelength Reservation, and Protecting Threshold. An advantage of adaptive routing is that it results in lower connection blocking than fixed and fixed-alternate routing. Among all wavelength assignment algorithms Relative Capacity Loss (RCL) [9] performance is good; however, RCL is relatively expensive to implement in a distributed controlled network, and it may introduce some significant control overhead. To overcome this a new algorithm called the Distributed Relative Capacity Loss (DRCL) is introduced. It is based on RCL and which is more efficient in a distributed environment. In this paper, we have attempted to reduce the blocking probability by using a priority based adaptive routing and distributed relative capacity loss wavelength assignment scheme with incorporation of a traffic grooming mechanism (PARDWATG) and compared its performance with a similar priority based routing and wavelength assignment (PRWATG) scheme. The rest of the paper is organized as follows. Section II formally defines the problem. The previous work is presented in Section III. The proposed PARDWATG is presented in Section IV. Results are present in Section V. and, finally, Section VI concludes the paper. 2. Network Description: We model the physical topology of an optical network as a directed connected graph G (V, E, W), where V and E are the sets of nodes and bi-directional optical fiber links or edges of the network, respectively. Here each link has a finite number of wavelengths, W. In the network, a nonnegative cost (distance between adjacent nodes) C (e) is assigned for every e. The cost between nodes a and b is considered to be 1 if there exists no link between a and b. The following assumptions are considered in the model: Each fiber link can carry an equal number of wavelengths and the network is without wavelength conversion capabilities. All the lightpaths using the same fiber link must be allocated distinct wavelengths. Each node can work as both an access node and a routing node. Each node is equipped with a fixed number of tunable transceivers. Each node is capable of multiplexing/demulti-plexing as many connection requests (having the same source destination (s d) pair) within the channel capacity. All the channels have the same bandwidth. The connection requests arrive in the system randomly based on a Poisson process. The holding time of all connection requests having the same s d pair is equal. The following notations are used in the paper: N and E are the total numbers of nodes and links, respectively in the network. s and d are the source and destination of a connection request. A is the total number of different s d pairs for all connection requests (A N (N-1)). W is the total number of wavelengths per fiber link. L is the number of links between an s d pair.² Z is the total number of connection requests in the network. Y is the total number of groomed connection requests in the network (Y Z). α s,d is the total volume of traffic for a connection request between an s d pair. α i,j is the total amount of traffic offered onto a lightpath from node i to node j. CB is the maximum bandwidth or capacity of a channel. th is the holding time of a connection request C(s,d). K is the number of alternate paths. Pi, j is the lightpath indicator. Pi, j = 1 if there exists a lightpath from node i to node j; otherwise 0. f max is the maximum offered traffic flow on any lightpath in the network. 3. Proposed work The proposed PRWATG is intended to assign the wavelengths for the connection requests according to priority order to reduce the blocking probability in the network. In the proposed scheme, there are two steps constructing the complete framework. These steps are connection request grooming with priority order estimation and finally routing and wavelength assignment (RWA). Connection request grooming with priority order estimation is the first step used to groom the connection requests and estimate their priority order. Finally, groomed connection requests are served for wavelength assignment according to their priority order. Capacity utilization is less for without priority. Priority assignment increases the capacity utilization. Capacity of λ is filled with the priority of 0.5λ+0.25λ+0.25λ. So the traffic is groomed efficiently with the priority manner. The overall concept of the proposed scheme is depicted in Fig.1. Fig. 1 Concept of proposed scheme Volume 2, Issue 4 July August 2013 Page 356

3 This Fig. 1 shows that the randomly arriving connection requests are groomed first and then enqueued in the priority queue to estimate their priority order according to the CRGPE algorithm. Finally, the groomed connection requests are served according to their priority order. Algorithm: Connection request grooming and priority estimation (CRGPE). Input: Network configuration and set of connection requests. Output: Groomed connection requests with their priority order. Step 1: The connection requests with the same s d pair are groomed within channel capacity. Where R is the set of connection requests and indicates the bandwidth of a connection request from source s to destination d. represents the groomed connection request from source s to destination d. Step 2: Enqueue all the groomed connection requests in the priority queue. Step 3: Cluster all the groomed connection requests into two types Such as direct physical link groomed connection requests and indirect physical link groomed connection requests. GR1= GR2= Such that network has a cost of, and each wavelength-converter link has a cost of c units. If wavelength conversion is not available, then c =. When a connection arrives, the shortest-cost path between the source node and the destination node is determined. If there are multiple paths with the same distance, one of them is chosen randomly. By choosing the wavelength-conversion cost c appropriately, we can ensure that wavelength-converted routes are chosen only when wavelength-continuous paths are not available. In shortest- cost adaptive routing, a connection is blocked only when there is no route (either wavelength-continuous or wavelength- converted) from the source node to the destination node in the network. An advantage of adaptive routing is that it results in lower connection blocking than fixed and fixed-alternate routing. For the network in Fig. 3, if the links (1,2) and (4,2) in the network are busy, then the adaptive-routing algorithm can still establish a connection between Nodes 0 and 2, while both the fixed-routing protocol and the fixed-alternate routing protocols with fixed and alternate paths as shown in Fig. 2 would block the connection. Another form of adaptive routing is least-congested-path (LCP) routing [10]. Similar to alternate routing, for each source-destination pair, a sequence of routes is preselected. Upon the arrival of a connection request, the least-congested path among the pre-determined routes is chosen. The congestion on a link is measured by the number of wavelengths available on the link. Links that have fewer available wavelengths are considered to be more congested. Where GR1 and GR2 are the two groomed connection request ordered sets of having direct and indirect physical links respectively. The priority order of each groomed connection request is assigned according to its position either in GR1 or in GR2. Groomed connection requests in GR1 have higher priorities compared to groomed connection requests in GR2. 4. Routing and Wavelength Assignment In previous work they are used fixed alternate algorithm for routing, first-fit method for wavelength assignment. In this paper we are using adaptive technique for routing and distributed relative capacity loss (DRCL) for wavelength assignment. 4.1Adaptive routing: In adaptive routing, the route from a source node to a destination node is chosen dynamically, depending on the network state. The network state is determined by the set of all connections that are currently in progress. One form of adaptive routing is adaptive shortest-cost-path routing, which is well-suited for use in wavelength-converted networks. Under this approach, each unused link in the network has a cost of 1 unit, each used link in the Fig. 2 Primary (solid) and alternate (dashed) routes from Node 0 to Node 2. Fig. 3 Adaptive route from Node 0 to Node Wavelength Assignment using DRCL: In order to implement an effective wavelength-selection policy in a distributed adaptive routing environment, two problems have to be solved: How is information of network state exchanged? and How can we reduce the amount of calculation upon receiving a connection request? To speed up the wavelength-assignment procedure, each node in the network stores information on the capacity loss on each wavelength so that only a table lookup and a small amount of calculations are required upon the arrival of a connection request. To maintain a valid table, the related values should be up-dated as soon as the network state has changed. To simplify the computation, we Volume 2, Issue 4 July August 2013 Page 357

4 propose an algorithm called Distributed Relative Capacity Loss (DRCL). We introduce an RCL table at each node and allow the nodes to exchange their RCL tables as well. The RCL tables are updated in a similar manner as the routing table. Each entry in the RCL table is a triple of (wavelength w, destination d, rcl (w, d)). When a connection request arrives and more than one wavelength is available on the selected path, computation is carried out among these wavelengths. Similar to the manner in which MΣ and RCL consider a set of potential paths for future connections, DRCL considers all of the paths from the source node of the arriving connection request to every other node in the network, excluding the destination node of the arriving connection request. DRCL then chooses the wavelength that minimizes the sum of rcl (w, d) over all possible destinations. The rcl (w, d) at node s is calculated as follows: If there is no path from node s to node d on wavelength w, then rcl(w,d) = 0; otherwise, If there is a direct link from node s to node d, and the path from s to d on wavelength w is routed through this link (note that it is possible for a direct link to exist between two nodes, but for the path to be routed around this link), then rcl(w,d) = 1/k, where k is the number of available wavelengths on this link through which s can reach d; otherwise, If the path from node s to node d on wavelength w starts with node n (n is s s next node for destination d on wavelength w), and there are k wavelengths available on link s n through which s can reach d, then rcl(w,d) at node s is set to (1/k, rcl(w,d) at node n). Table1 shows the computation carried out by DRCL for the same example given in Fig.4. 5 Results We consider the Indian network with 14 nodes, 24 bidirectional physical paths for analysing the blocking performance. It is as shown in fig.5, fig.6, fig.7. Connection request is blocked or rejected if it is not served within the holding time. So, the performance is measured in terms of blocking probability (BP), where the lower the blocking probability is the better the performance is.fig.5, 6, 7 shows the blocking probability (BP) versus the number of wavelengths (W), obtained by using the proposed PRWATG scheme for the Indian network with 1000 connection requests. In these figures, K = 1 corresponds to a primary path and other values represent alternate paths (including the primary path). From the figures it is observed that the BP decreases with increase of W. and also the BP decreases with increase of K. Fig. 5 BP versus W, obtained by using PRWATG for the Indian backbone network when k=1. Fig. 6 BP versus W, obtained by using PRWATG for the Indian backbone network when k=2 Fig. 4 Wavelength-usage pattern for a network segment consisting of six fiber links in tandem. (Unshaded regions indicate that wavelength is available on these links.) TABLE I The calculation in DRCL. Fig. 7 BP versus W, obtained by using PRWATG for the Indian backbone network when k=3. 6. Conclusion The effectiveness of the proposed scheme is examined through its performance evaluation in the Indian backbone network. It is a better scheme to reduce the call blocking in practical networks; only in the case of incoming connection requests are having the same source-destination pairs. For further reduction of blocking probability, for effective utilization of wavelength Volume 2, Issue 4 July August 2013 Page 358

5 capacity we are adding adaptive routing, distributed relative capacity loss techniques to PRWA instead of fixed alternate routing and first-sit wavelength assignment. Our simulation result shows that the blocking probability of priority based adaptive distributed routing and wavelength assignment (PARDWATG) schemes is less compared with priority based routing and wavelength assignment (PRWATG). References: [1] B. Mukherjee, Optical WDM Networks. Springer, [2] R. M. C. Siva and G. Mohan, WDM Optical Networks: Concepts, Design and Algorithms. PHI, [3] O. Gerstel and S. Kutten, Dynamic Wavelength Allocation in All-Optical Ring Networks, Proc., IEEE ICC 97, Montreal, Quebec, Canada, vol. 1, pp , June [4] K. Zhu and B. Mukherjee, Traffic grooming in an optical WDM mesh network, IEEE J. Sel. Areas Commun., vol. 20, no. 1, pp , [5] T. De, P. Jain, and A. Pal, Distributed dynamic grooming routing and wavelength assignment in WDM optical mesh networks, Photonic Network Commun., vol. 21, pp , [6] C. Colbourn, G. Quattrocchi, and V. Syrotiuk, Grooming traffic to maximize throughput in SONET rings, J. Opt. Commun. Netw.,vol. 3, no. 1, pp , [7] A. Balma, N. Hadj-Alouane, and A. Hadj-Alouane, A nearoptimal solution approach for the multi-hop traffic grooming problem, J. Opt. Commun. Netw., vol. 3, no. 11, pp ,2011. [8] S. Huang, M. Xia, C. Martel, and B. Mukherjee, Survivable multipath traffic grooming in telecom mesh networks with inverse multiplexing, J. Opt. Commun. Netw., vol. 2, no. 8, pp , [9] X. Zhang and C. Qiao, Wavelength Assignment for Dynamic Traffic in Multi-fiber WDM Networks, Proc.,7th International Conference on Computer Communications and Networks, Lafayette, LA, pp , Oct [10] K. Chan and T. P. Yum, Analysis of Least Congested Path Routing in WDM Lightwave Networks, Proc., IEEE INFOCOM 94,Toronto, Canada, vol. 2, pp , April [11] Bijoy Chand Chatterjee, Nityananda Sarma, and Partha Pritim Sahu, Priority Based Routing and Wavelength Assignment With Traffic grooming for Optical Networks, J. Opt. Commun. Netw./vol. 4, no. 6, pp , June Madras University, Chennai, Tamilnadu, India. He has completed M.E in Digital Communication and Network Engineering (2006) from Anna University, Chennai, Tamilnadu. He has 10 years of experience in teaching and guiding projects for undergraduate and postgraduate students. His research areas are Optical networks and Optical Communication. Dr.A.Sivasubramanian has received B.E. degree in ECE from University of Madras in 1990, and M.E. in Applied Electronics from Bharathiar University in 1995 and Ph.D. degree in Optical Comm. from AnnaUniversity Chennai in Currently he is working as a Prof & Head, in the department of Electronics and communication engineering at St.Joseph s College of Engineering, Chennai, India. He has 20 years of experience in teaching and guiding projects for undergraduate, postgraduate and research scholars. He has added more than ten international and national publications to his credit. He is a recognized supervisor for the doctoral degree programme at Anna University Chennai and Sathyabama University, Chennai. His areas of interests include optical communication, optical networks, Bio-optical Engineering, Wireless sensor and computer networks. He is a member of ISTE, IETE, IEEE, and OSA. AUTHOR: K.Pushpanathan is a Research Scholar and pursuing a Doctoral Degree in Information & Communication Engineering at the Department of Electronics and Communication Engineering at Anna University, Chennai , India. He received his B.E in Electronics and Communication Engineering (2001) from Volume 2, Issue 4 July August 2013 Page 359