Elsevier

Computer Networks

Volume 52, Issue 10, 16 July 2008, Pages 2033-2044
Computer Networks

Route optimization in optical burst switched networks considering the streamline effect

https://doi.org/10.1016/j.comnet.2008.02.017Get rights and content

Abstract

Route optimization in optical burst switching (OBS) networks is investigated in this paper. Two route optimization problems are studied. The first problem considers the network in the normal working state where all the links are working properly. The route for each flow is decided so as to minimize the overall network burst loss. The second problem considers the failure states apart from the normal working state. The primary and backup paths for each flow are determined in such a way to minimize the expected burst loss over the normal and failure states. We argue that route selection based on load balancing or the traditional Erlang B formula is not efficient because of an important feature called the streamline effect. We analyze the streamline effect and propose a more accurate loss estimation formula which considers the streamline effect. Based on this formula, we develop mixed integer linear programming (MILP) formulations for the two problems. Since the MILP-based solutions are computationally intensive, we develop heuristic algorithms. We verify the effectiveness of our algorithms through numerical results obtained by solving the MILP formulations with CPLEX and also through simulation results.

Introduction

Optical burst switching (OBS) is a promising technology to transmit bursty traffic over wavelength division multiplexing (WDM) networks [1]. To reduce the burst loss in OBS networks, many scheduling algorithms [2] and loss reduction techniques, such as burst segmentation [3], traffic distribution [4], route deflection [5] and burst rescheduling [6], have been proposed. In this paper, we consider using offline route optimization to reduce burst loss in OBS networks. We assume that the network has multiple protocol label switching (MPLS) control. We use the term ‘flow’ to refer to the stream of bursts sent on a label switching path (LSP) from an ingress node to an egress node. We also assume that each node has full wavelength conversion, which is widely adopted in the OBS research community. Offline route optimization determines the routes of the flows in such a way to minimize the overall network burst loss, assuming the estimated traffic demand is known. We assume the traffic demand is quasi-stationary, as the measurements in Internet traffic indicate that the aggregated load on links changes relatively slowly [8]. Due to the large amount of data carried by an OBS network, a failure would cause huge data loss. Therefore, we also consider the selection of backup routes.

We study two route optimization problems in this paper. The first problem considers the usual case of normal state where all the links are working properly, and one route is determined for each flow to minimize the overall burst loss. The second problem considers the failures, and the primary and backup paths for each flow are determined in such a way to minimize the expected burst loss over the normal and the failure states. We refer the first problem as the normal state route (NSR) optimization problem and the second problem as the failure recovery route (FRR) optimization problem.

For the FRR problem, we consider a failure recovery mechanism as below. For each flow, two link-disjoint LSPs, the primary LSP and backup LSP, are set up. When the network is in the normal working state, the bursts are transmitted on the primary LSP. When a link failure occurs, the end nodes of the failed link detect the failure and notify the end nodes of the failed LSPs. After receiving the notification, the source node transfers the affected flows to the pre-configured backup LSP. We assume single link failure, which has been commonly used in the literature. So when a failure occurs the affected traffic could be transferred to the backup path without searching for a new route. Such a recovery scheme is fast since it is exempted from the searching and setup of a new route after a failure occurs, and it is also efficient as the routes have been optimized. There has been some work done on OBS fault managements [9], [10], [11], but none deals with primary/backup route selection to minimize the expected burst loss.

There are works [12] for offline route optimization in OBS networks where the Erlang B formula is used to estimate the loss. Our work differs from those in that we consider the special feature of OBS networks, called the streamline effect. This effect is that, in OBS networks, due to the bufferless core nodes, if some flows share a link, there will be no contention among these flows in the outgoing link. We present a loss estimation formula considering the streamline effect. Based on our formula, we present mixed integer linear programming (MILP) formulations for the NSR and the FRR problems. Because of the intensive computation needed to solve MILP formulations, heuristic algorithms are developed.

The rest of the paper is organized as follows. Section 2 analyses the streamline effect, presents a new loss estimation formula, and illustrates how it can help find a better route layout. Section 3 gives the MILP formulations for the NSR and the FRR problems based on the new formula. The heuristic algorithms are described in Section 4. Section 5 presents the performance study. Section 6 makes concluding remarks.

Section snippets

Streamline effect and loss estimation

We assume that the burst arrivals follow a Poisson distribution. Such an assumption is reasonable since a flow in OBS networks is the aggregation of many independent IP streams. The Erlang B formula is usually used to estimate burst loss in OBS networks. The Erlang B formula assumes that all the flows are independent and contend with each other. However, since there is no buffering at the core nodes in OBS networks, if two or more flows share more than one consecutive link along their paths,

The MILP formulation

In this section, we develop MILP formulations for the NSR and the FRR problems based on the new loss estimation formula.

The NSR problem can be stated as follows:

Given an OBS network topology and a traffic demand, it is required to determine a route for each flow so as to minimize the overall burst loss.

The FRR problem can be stated as follows:

Given an OBS network topology and a traffic demand, it is required to determine a pair of link-disjoint primary and backup paths for each flow to minimize

Heuristic algorithms

Since solving an MILP problem is computationally intensive, heuristic algorithms are developed. SLNS-Heur (streamline effect based normal state route optimization heuristic) is developed to solve the NSR problem, while SLFR-Heur (streamline effect based failure recovery route optimization heuristic) is developed to solve the FRR problem.

Performance study

In this section, we present numerical results. We first study the accuracy of the streamline loss estimation formula. Then the performance of routing algorithms proposed in this paper are compared with other known algorithms.

Conclusions

Two problems of route optimization in OBS networks have been studied in this paper. The first problem is to determine a route for each flow to minimize the overall burst loss. The second problem considers the failure states and determines the primary and backup paths for each flow to minimize the expected burst loss over the normal and the failure states. We have discussed the streamline effect, a special feature of OBS networks. We have also shown that the route selection based on Erlang B

Qian Chen received her B.E. degree from Beijing University of Posts and Telecommunications, China in 1996. She received her M.E. degree in Electrical and Computer Engineering from National University of Singapore in 2003. Now she is working towards her Ph.D. degree. Her research interests focus on optical burst switching.

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Qian Chen received her B.E. degree from Beijing University of Posts and Telecommunications, China in 1996. She received her M.E. degree in Electrical and Computer Engineering from National University of Singapore in 2003. Now she is working towards her Ph.D. degree. Her research interests focus on optical burst switching.

Gurusamy Mohan received the Ph.D. degree in Computer Science and Engineering from the Indian Institute of Technology (IIT), Madras in 2000. He joined the National University of Singapore in June 2000, where he is currently an associate professor in the Department of Electrical and Computer Engineering. He has held a visiting position at Iowa State University, USA, during January–June 1999. His current research interests are in WDM OCS networks, WDM OBS networks, MPLS switching networks, wireless sensor networks and grid networks.

Kee Chaing Chua received his Ph.D. degree in Electrical Engineering from the University of Auckland, New Zealand, in 1990. Following this, he joined the Department of Electrical Engineering at the National University of Singapore (NUS) as a lecturer, became a senior lecturer in 1995, an associate professor in 1999 and professor in 2006. From 1995 to 2000, he was seconded to be the Deputy Director of the Center for Wireless Communications (now Institute for Infocomm Research), a National Telecommunication R&D Institute funded by the Singapore Agency for Science, Technology and Research. From 2001 to 2003, he was on leave of absence from NUS to work at Siemens Singapore where he was the founding head of the ICM Mobile Core R&D Department. He is a director for the Singapore National Research Foundation now. He has carried out research in various areas of communication networks. His current interests are in ensuring end-to-end quality of service in wireless and optical networks. He is a recipient of an IEEE Third Millennium Medal.

Earlier version of part of this work appeared in ICC 2006 and HPSR 2006.

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