# ROUTING ALGORITHMS FOR RING NETWORKS

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1 ROUTING ALGORITHMS FOR RING NETWORKS by Yong Wang B.Sc., Peking University, 1999 a thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in the School of Computing Science c Yong Wang 200 SIMON FRASER UNIVERSITY July 200 All rights reserved. This work may not be reproduced in whole or in part, by photocopy or other means, without the permission of the author.

2 APPROVAL Name: Degree: Title of thesis: Yong Wang Master of Science Routing Algorithms for Ring Networks Examining Committee: Dr. Petra Berenbrink Chair Dr. Qian-ping Gu Senior Supervisor Dr. Joseph G. Peters Supervisor Dr. Arthur L. Liestman SFU Examiner Date Approved: ii

3 Abstract In this thesis, we study routing problems on ring networks. The ring is a popular topology for communication networks and has attracted much research attention. A communication application on a ring network can be regarded as a set of connection requests, each of which is represented by a set of nodes to be connected in the ring network. To implement a communication application, we need to develop a routing algorithm to find a path connecting all the nodes involved in each connection request. One of the most important optimization problems for the communication on ring networks is to develop a routing algorithm such that the maximum congestion (i.e., the maximum number of paths that use any single link in the ring) is minimized. This problem can be formulated as the Minimum Congestion Hypergraph Embedding in a Cycle (MCHEC) problem with a set of connection requests represented by a hypergraph. A special case of the MCHEC problem, in which each connection request involves exactly two nodes, is known as the Minimum Congestion Graph Embedding in a Cycle problem. A more general case, in which connection requests may have non-uniform bandwidth requirements, is known as the Minimum Congestion Weighted Hypergraph Embedding in a Cycle problem. The Minimum Congestion Graph Embedding in a Cycle problem is solvable in polynomial time, and the other problems are NP-hard. In this thesis, we focus on the MCHEC problem and propose efficient algorithms in three categories. In the first category is a 1.8-approximation algorithm that improves the previous 2-approximation algorithms. In the second category is an algorithm that computes optimal solutions for the MCHEC problem. This algorithm runs in polynomial time for subproblems with constant maximum congestions, and is more efficient in terms of the time complexity than the previous algorithm that solves the same problem. The third category contains two heuristic approaches. According to our simulation results, both heuristics have lower time complexities and better practical performance than a well known heuristic. iii

4 To my wife, my parents and my little sister iv

5 v

6 Acknowledgments I would like to express my deepest thanks to my senior supervisor, Dr. Qian-ping Gu, for his valuable guidance, continuous encouragement, and instructions on academic writing throughout this research. I am very thankful to my supervisor, Dr. Joseph G. Peters, who is the organizer of Network Modeling Group. I learned much through our group meetings. I would also like to thank Dr. Arthur L. Liestman for serving as the examiner of this thesis and being very responsible. He gave me many valuable advices on academic writing. I would like to take this opportunity to say thanks to all the friends whom I have met at Simon Fraser University, and all the staff and faculty in the School of Computing Science, for making a nice study and working environment. Special thanks go to Dr. Pavol Hell, I gained a lot in his class. Last but not least, I am deeply in debt to my parents for their continuous love. I would like to express my great gratitude to my wife for her love and encouragement all the time. vi

7 Contents Approval Abstract Dedication Quotation Acknowledgments Contents List of Tables List of Figures ii iii iv v vi vii ix x 1 Introduction 1 2 Preliminaries Approximation algorithm and approximation ratio Notation and terminology Related work on the MCHEC problem 9.1 A special case: Minimum Congestion Graph Embedding in a Cycle Minimum Congestion Hypergraph Embedding in a Cycle (MCHEC) NP-completeness Approximation algorithms An algorithm for optimal solutions vii

8 .2.4 A heuristic Efficient algorithms for the MCHEC problem A 1.8-approximation algorithm Algorithm Analysis An algorithm for optimal solutions Heuristics Heuristic 1: Heaviest Zone Removing Heuristic 2: Spin Routing Simulation results and conclusions Simulation results Conclusions Discussion and future work Further improvement on the algorithms for the MCHEC problem Minimum Congestion Weighted Hypergraph Embedding in a Cycle Related work Future research Path Coloring problem Related work Future research Bibliography 51 viii

9 List of Tables 4.1 Practical performance of our 1.8-approximation algorithm on the cycle with n = 100 nodes Practical performance of our 1.8-approximation algorithm on the cycle with n = 0 nodes Performance comparison on the cycle with n = 100 nodes Performance comparison on the cycle with n = 0 nodes ix

10 List of Figures 1.1 Hypergraph embedding in a cycle Clockwise Embedding Re-embedding candidate C-paths for the candidate w.r.t. k = 1 in the Clockwise Embedding and after the re-embedding Embedding algorithm for the MCHEC problem Lower bound involving three links x, y, and z Heaviest Zone Removing heuristic An example of Heaviest Zone Removing Spin Routing heuristic An example of Spin Routing x

11 Chapter 1 Introduction We study routing problems on ring networks in this thesis. In a ring network, a set of network nodes are connected together by a set of links as a cycle, and every node plays the same role. This node-symmetry simplifies the design of network algorithms such as routing and path coloring. Moreover, a ring is a 2-connected topology. There exist two distinct paths between any pair of nodes in a ring network, so it remains connected even in the presence of any single node or link failure. Taking account of these advantages over other network configurations, the ring topology is widely used in communication networks and has attracted much attention as a research subject [1, 2,, 4, 5, 6, 7, 8]. A communication application on a ring network can be regarded as a set of connection requests, each of which is represented by a set of nodes to be connected in the ring network. To implement a communication application, we need to develop a routing algorithm to find a path connecting all the nodes involved in each connection request. Researchers studied routing problems on ring networks either as directed connection requests on directed ring networks [5] or as undirected connection requests on undirected ring networks [4]. In the former case, a ring network is modeled as a symmetric digraph with two directed links in two opposite directions between every pair of adjacent nodes. In the latter case, a ring network is modeled as an undirected graph with one undirected link between every pair of adjacent nodes. In some practical ring networks such as optical rings, current technologies do not support the bi-directional use of a link, so two opposite-directed optical fibers are usually used to connect each pair of adjacent nodes. Therefore, the directed model is more appropriate to describe such ring networks. However, the undirected model is commonly used in studying communication problems because it is simple and appropriate for modeling 1

12 CHAPTER 1. INTRODUCTION 2 two-way communications. Results derived from the undirected model can often be easily extended to practical ring networks with directed communication links. In this thesis, the discussion is based on the undirected model. As a part of the future work, we will extend the results of this thesis to the directed model. We assume that all the links in a ring network have the same capacity, which is also referred to as the capacity of the ring. The cost of a ring network depends on its capacity. Therefore, an important optimization problem arises in designing routing algorithms for ring networks: Given a set of connection requests, develop a routing algorithm to find a path in the ring for each connection request such that the required ring capacity to satisfy all the connection requests is minimized. If we define the congestion of a link to be the number of paths containing the link and the maximum congestion of a ring to be the maximum number of paths containing any single link of the ring, then the required ring capacity is equal to the maximum congestion on the ring network. A communication application consisting of m connection requests on a ring network with n nodes can be described by a hypergraph with m hyperedges and n nodes. Each hyperedge in the hypergraph represents a connection request among its nodes. Figure 1.1(a) shows a hypergraph that corresponds to four connection requests {0, 1, 2}, {0, 4, 5}, {, 4, 5}, and {,5} on node set {0,1,2,,4,5}. Since routing a set of connection requests on a ring network can be regarded as embedding corresponding hyperedges as paths in a cycle, Ganley and Cohoon [9] formulated the above routing problem on ring networks as the Minimum Congestion Hypergraph Embedding in a Cycle (MCHEC) problem: Embed hyperedges of a hypergraph as paths in a cycle such that the maximum congestion is minimized. Figure 1.1(b) and (c) give embeddings with maximum congestion and maximum congestion 2 respectively for the hypergraph in Figure 1.1(a). A special case of the MCHEC problem, in which each connection request involves exactly two nodes, is in fact the Minimum Congestion Graph Embedding in a Cycle problem. The Minimum Congestion Graph Embedding in a Cycle problem has been proved to be solvable in polynomial time by Frank et al. []. However, the MCHEC problem is known to be NP-complete [9]. Ganley and Cohoon [9] gave a -approximation algorithm for the MCHEC problem. The approximation ratio was improved to 2 based on different approaches [10, 11, 12]. Gonzalez [10] proposed two 2-approximation algorithms for the MCHEC problem. One algorithm formulated the MCHEC problem as an Integer Program, and the other transformed the MCHEC problem to the Minimum Congestion Graph Embedding in a Cycle problem. Carpenter et al. [11] gave

13 CHAPTER 1. INTRODUCTION (a)connection request set (b)embedding with Max congestion = (c)embedding with Max congestion = 2 (optimal) Figure 1.1: Hypergraph embedding in a cycle a simple 2-approximation algorithm called Clockwise Embedding. Lee and Ho [12] developed a greedy algorithm named Longest Adjacent Path Removing that has an approximation ratio of 2 as well. Ganley and Cohoon [9] also proposed an O((mn) L +1 ) time algorithm to compute optimal solutions for the MCHEC problem, where L is the maximum congestion, m is the number of hyperedges, and n is the number of nodes in the hypergraph. This algorithm runs in polynomial time for the subproblem with constant maximum congestion L. In addition, some heuristic algorithms have been proposed to achieve good performance in practice. Among them, the Iterated Maximum Independent Set (IMIS) heuristic presented by Ganley and Cohoon [1] is very well known. For the MCHEC problem discussed above, we assume that the connection requests have a uniform bandwidth requirement, so the congestion of each link in a cycle can be simplified to be the number of paths using the link. A more general case, in which the connection requests may have non-uniform bandwidth requirements, was formulated by Lee and Ho [12] as the Minimum Congestion Weighted Hypergraph Embedding in a Cycle problem: Embed the weighted hyperedges of a hypergraph as weighted paths in a cycle such that the maximum congestion (i.e., the maximum total weight of paths using any single link in the cycle) is minimized. Lee and Ho proved the Minimum Congestion Weighted Hypergraph Embedding in a Cycle problem is NP-complete and gave two 2-approximation algorithms [12]. Again for a special case in which each connection request involves exactly two nodes, the Minimum Congestion Weighted Hypergraph Embedding in a Cycle problem is in fact the Minimum Congestion Weighted Graph Embedding in a Cycle problem. The Minimum Congestion

14 CHAPTER 1. INTRODUCTION 4 Weighted Graph Embedding in a Cycle problem is proved to be NP-complete as well by Cosares and Iraj [14]. The approximation algorithms has been proposed in [14, 7, 15]. In this thesis, we concentrate on the MCHEC problem. The MCHEC problem has applications not only in network communications, but also in parallel computing and electronic design automation [, 1, 16, 17, 18, 19]. In parallel computing, the processors of a parallel computer can be represented by the nodes of a hypergraph; a group of processors, which are required to communicate with one another, can be represented by a hyperedge in the hypergraph. A solution to the MCHEC problem gives a routing path for each communication group. In electronic design, among applications is the moat routing. An instance of the moat routing consists of two concentric rectangles, which represent a core circuit area and input/output pads respectively, and a set of nets whose pins lie on either or both of the perimeters of the two rectangles. The routing of each net will be placed between the two rectangles. The goal is to implement the routing for the set of nets such that the width of the circular channel between the two rectangles is minimized. We propose algorithms for the MCHEC problem in three categories: the approximation algorithm, the algorithm for optimal solutions, and the heuristic approach. In the first category is a 1.8-approximation algorithm that improves the best known 2-approximation algorithms. Our algorithm starts from the Clockwise Embedding, and then the algorithm tries to re-embed some hyperedges to reduce the maximum congestion. Let L be the maximum congestion of the Clockwise Embedding and L be the maximum congestion of an optimal embedding. If our algorithm can re-embed k hyperedges to get an embedding with maximum congestion L k, the approximation ratio of the algorithm is (L k)/l. Since L/2 L [11], the approximation ratio of our algorithm is at most 2(L k)/l. This gives a good approximation ratio if k is large. If k is small, we shall prove a new lower bound on L. As shown later, the approximation ratio of the algorithm increases from 1 to 1.8 as k decreases from L/2 to L/10, the ratio decreases from 1.8 to 1.5 as k decreases from L/10 to 0, and the ratio is 1.8 in the worst case. Our algorithm has the optimal O(mn) time for the hypergraph with m hyperedges and n nodes. In the second category is an O(( mn) L ) time algorithm to compute optimal solutions for the MCHEC problem, where L is the maximum congestion, m is the number of hyperedges, and n is the number of nodes in the hypergraph. This algorithm improves the O((mn) L +1 ) time algorithm given by Ganley and Cohoon [9], and runs in polynomial time

15 CHAPTER 1. INTRODUCTION 5 for the subproblem with constant maximum congestion L. The O((mn) L +1 ) time algorithm uses the fact that every link in a cycle has a congestion at most L in any optimal embedding. An arbitrary link i in the cycle is chosen, and an optimal embedding is sought by enumerating all the good embeddings that incur a congestion of at most L on link i. An important observation is that the fewer good embeddings, the more efficient the algorithm. So in our algorithm, instead of considering only one link i, we think of two links in the cycle and merely check those good embeddings that incur congestions of at most L on both of the two links. Therefore, our algorithm reduces the number of checked good embeddings further, and thus improves the efficiency of the algorithm. The third category contains two heuristics. One heuristic uses a greedy strategy, and the other tries to evenly distribute congestions on all the links in a cycle. Both heuristics run in O(mn) time, which improves the O(m(mn) 2 ) time Iterated Maximum Independent Set heuristic proposed by Ganley and Cohoon [1] for the hypergraph with m hyperedges and n nodes. In addition, our heuristics achieve much better performance than the Iterated Maximum Independent Set heuristic in practice according to our simulation results. This thesis is organized as follows. In chapter 2, we introduce basic concepts and notation. In chapter, the previous work about the MCHEC problem is surveyed. Chapter 4 gives our improved algorithms for the MCHEC problem in three categories. In the last chapter, we present possible research directions for the future work.

16 Chapter 2 Preliminaries 2.1 Approximation algorithm and approximation ratio For any NP-hard optimization problem, there is no polynomial time algorithm to compute an optimal solution unless P = N P. So much effort has been put into developing polynomial time algorithms to find near-optimal solutions whose values are close to the value of an optimal solution. We call such a near-optimal solution an approximate solution, and an algorithm that produces approximate solutions an approximation algorithm. The following formal definitions of the approximate solution, approximation algorithm, and approximation ratio are from the book by Cormen et al. [20]. Definition [20] (Approximate Solution) For an optimization problem, a feasible solution with the value close to the value of an optimal solution is an approximate solution of. Definition [20] (Approximation Algorithm) For an optimization problem, a polynomial time algorithm that generates approximate solutions is an approximation algorithm of. Any algorithm that produces approximate solutions for problem can be called an approximation algorithm. How to evaluate approximation algorithms is important. Generally speaking, good approximation algorithms should be efficient (polynomial time) and produce solutions with values as close to the optimum as possible. Concept approximation ratio [20] is widely used to evaluate approximation algorithms. In what follows, we assume that a solution always has a positive value. 6

17 CHAPTER 2. PRELIMINARIES 7 Definition 2.1. [20] (Approximation Ratio) An approximation algorithm A for an optimization problem has an approximation ratio of ρ(n) if for any instance of with size n, the value C of any approximate solution produced by the approximation algorithm satisfies where C is the value of an optimal solution. max{ C C C, C } ρ(n), This definition applies for both maximization and minimization problems. For a maximization problem, 0 < C C, and C /C gives the approximation ratio. For a minimization problem, 0 < C C, and C/C gives the approximation ratio. Therefore, the approximation ratio of an approximation algorithm is at least 1, and the approximation ratio of an optimal algorithm is exactly 1. An approximation algorithm with a large approximation ratio may return a solution that is much worse than an optimal solution. Reducing the approximation ratio as close to 1 as possible is a major goal of developing approximation algorithms. 2.2 Notation and terminology A cycle C of n nodes is an undirected graph with node set {i 0 i n 1}. There is a link between nodes i and j if i = j ±1, where (and in what follows) the arithmetic involving nodes is performed implicitly using the modulo n operation. The link from node i to node i + 1 is labeled as link i (see Figure 1.1). In what follows, without loss of generality, we assume that n nodes and n links of C are labeled from 0 to n 1 in the clockwise direction as shown in Figure 1.1(b) and (c). A hypergraph H(V,E H ) of n nodes and m hyperedges is a hypergraph with node set V = {i 0 i n 1} and hyperedge set E H = {e 1,e 2,...,e m }, where each hyperedge e i is a subset of V with n i (n i 2) nodes. As a special case, a graph G(V,E) is a hypergraph with each hyperedge consisting of exactly two nodes (i.e., n i = 2 for every e i in graph G(V,E)). For a hypergraph H(V,E H ), let {v i 1,vi 2,...,vi n i } be the sorted set of nodes contained in hyperedge e i (i.e., v i 1 < vi 2 <... < vi n i ). For 1 i m, a connecting path (or c-path) P i in C for hyperedge e i is a path in C such that all the nodes in e i are connected by P i. An adjacent path (or a-path) in C for hyperedge e i is a path in C from node vj i to node vi j+1 (1 j n i ) in the clockwise direction, where vn i i +1 = vi 1. An embedding of hypergraph

18 CHAPTER 2. PRELIMINARIES 8 H(V,E H ) in cycle C is a set of c-paths in C such that there is exactly one c-path in the set for each hyperedge. A hyperedge e i with n i nodes has exactly n i a-paths in C, and any n i 1 a-paths together form a path in C to connect all the n i nodes in e i. Thus any n i 1 a-paths constitute a c-path P i for hyperedge e i. Given an embedding of a hypergraph, the congestion of each link in C is the number of c-paths that contain the link. More precisely, we define the MCHEC problem as follows: Given a hypergraph and a cycle on the same node set, embed the hyperedges as c-paths in the cycle such that the maximum congestion (i.e., the maximum number of c-paths using any single link in the cycle) over all the links in the cycle is minimized. Figure 1.1(b, c) gives two feasible embeddings for the hypergraph in Figure 1.1(a) on the cycle with node set {0,1,2,,4,5}. The optimal embedding may not be unique, and Figure 2.1(c) is one of such embeddings. A segment of cycle C is a connected subgraph of C. In what follows, we use segment p,q to denote the segment of C that includes nodes p,p + 1,...,q. Cycle C is cut into two segments by removing any two links i and j, and we call the two links i and j a cut (i,j) of C. A hyperedge is separated by a cut if there is at least one node of the hyperedge in each of the two segments. For example, if we choose links 0 and as a cut of the cycle in Figure 1.1, hyperedges e 1 = {0,1,2},e = {,4,5}, and e 4 = {,5} are separated by cut (0,); hyperedge e 2 = {0,4,5} is not separated by cut (0,). It is observed that the c-path for a separated hyperedge in any embedding must contain at least one of the two links in the cut. Therefore, if we use N(i,j) to denote the number of hyperedges separated by cut (i,j), then max i,j {N(i,j) 2 } is a lower bound on the maximum congestion of an optimal embedding.

19 Chapter Related work on the MCHEC problem.1 A special case: Minimum Congestion Graph Embedding in a Cycle Frank et al. [] proved the following theorem for the Minimum Congestion Graph Embedding in a Cycle problem: Theorem.1.1 [] The Minimum Congestion Graph Embedding in a Cycle problem can be solved optimally in polynomial time. Schrijver et al. [7] developed an O(mn 2 ) time optimal algorithm for the graph with m edges and n nodes..2 Minimum Congestion Hypergraph Embedding in a Cycle (MCHEC).2.1 NP-completeness As a general case of the Minimum Congestion Graph Embedding in a Cycle problem, the MCHEC problem deals with the situation in which each connection request may involve more than two nodes. The MCHEC problem is more complicated than the Minimum Congestion Graph Embedding in a Cycle problem, and has been proved to be NP-complete by Ganley 9

20 CHAPTER. RELATED WORK ON THE MCHEC PROBLEM 10 and Cohoon [9]. They first transformed the MCHEC problem to the following Cycle Cover by Multiple Choice Paths (CCMCP) problem: Given the same problem instance as the MCHEC problem, find a path in the cycle for each hyperedge to connect two nodes in the hyperedge without spanning any other node in the same hyperedge, such that the minimum congestion over all the links in the cycle is maximized. For any solution of the CCMCP problem, we can easily transform it to a solution of the MCHEC problem by setting each path in the solution of the CCMCP problem to be the complement of the path. Therefore, if the CCMCP problem is NP-complete, so is the MCHEC problem. To prove the NPcompleteness of the CCMCP problem, Ganley and Cohoon constructed a polynomial time transformation from a known NP-complete problem Numerical Matching with Target Sums (NMTS) [21] to the CCMCP problem. We refer to Ganley and Cohoon [9] for a detailed proof of the following theorem. Theorem.2.1 [9] The MCHEC problem is NP-complete..2.2 Approximation algorithms 1. A simple -approximation algorithm Recall that any two links in a cycle C form a cut of C. Ganley and Cohoon [9] presented a simple algorithm for the MCHEC problem as follows: Choose an arbitrary cut (i, j) consisting of links i and j. Let S denote the set of hyperedges that are separated by cut (i,j), and S denote the set of hyperedges that are not separated by cut (i,j). Embed every hyperedge in S within the segment containing its nodes, and embed the hyperedges in S arbitrarily. This algorithm has an approximation ratio of and runs in O(mn) time for the hypergraph with m hyperedges and n nodes. 2. A 2-approximation algorithm based on Linear Programming Gonzalez formulated the MCHEC problem as an Integer Program [10]. As is well known, solving an Integer Program is NP-hard, however, a Linear Program is solvable in polynomial time. Gonzales transformed the Integer Program to a corresponding Linear Program by performing the LP Relaxation, and then obtained an approximate solution of the Integer Program by rounding off the solution of the Linear Program. This algorithm has an approximation ratio of 2. However, the running time of the algorithm is dominated by the part computing Linear Program, which has a high time complexity.

21 CHAPTER. RELATED WORK ON THE MCHEC PROBLEM 11. A 2-approximation algorithm based on the Minimum Congestion Graph Embedding in a Cycle problem As an alternative to the above Linear Programming (LP) based approximation algorithm, Gonzales proposed a LP-Free algorithm with a lower time complexity [10]. Recall that the Minimum Congestion Graph Embedding in a Cycle problem can be solved optimally in polynomial time []. The LP-Free algorithm transformed any instance of the MCHEC problem to a corresponding instance of the Minimum Congestion Graph Embedding in a Cycle problem. Once an optimal embedding of the Minimum Congestion Graph Embedding in a Cycle problem is obtained, it is then transformed back to an approximate solution of the original MCHEC problem. The LP-Free algorithm has an approximation ratio of 2 as well. For the hypergraph with m hyperedges and n nodes, the time complexity is O(mn ), which is dominated by the time complexity of the optimal algorithm for the Minimum Congestion Graph Embedding in a Cycle problem. 4. A 2-approximation algorithm based on the Clockwise Embedding Carpenter et al. proposed a very simple approximation algorithm named Clockwise Embedding [11]. The idea is to embed each hyperedge as a path from its lowest numbered node to its highest numbered node in the clockwise direction. Figure.1 shows an example of the Clockwise Embedding. For hyperedge e 1 = {0,1,2}, the lowest numbered node is 0 and the highest numbered node is 2. So e 1 is embedded as the path from node 0 to node 2 in C in the clockwise direction. The other three hyperedges are embedded similarly. In the Clockwise Embedding, no c-paths contain the highest numbered link, which is link 5 in Figure.1. The Clockwise Embedding is also a 2-approximation algorithm. Since our improved approximation algorithm will be based on the Clockwise Embedding, we state the complete proof by Carpenter et al. [11] for the following theorem. Theorem.2.2 [11] The approximation ratio of the Clockwise Embedding is 2. Proof: Recall that N(i,j) denotes the number of hyperedges separated by cut (i,j) of a cycle C, and max i,j {N(i,j) 2 } is a lower bound on the maximum congestion of an optimal embedding. Let l(i) be the congestion of any link i in C in the Clockwise Embedding.

22 CHAPTER. RELATED WORK ON THE MCHEC PROBLEM 12 0 e 1 = {0, 1, 2} e 2 = {0, 4, 5} e = {, 4, 5} e = {, 5} (a)hyperedges (b)clockwise Embedding Figure.1: Clockwise Embedding Let L = max{l(i)}, and s be a link with maximum congestion L in the Clockwise i Embedding. Since no c-paths contain link n 1 (i.e., the highest numbered link in C), all the hyperedges whose c-paths contain link s in the Clockwise Embedding are separated by cut (s,n 1). Therefore, L = N(s,n 1) 2 max i,j {N(i,j) 2 } 2L, that is, the maximum congestion of the Clockwise Embedding is bounded above by twice that of an optimal embedding. The Clockwise Embedding runs in O(mn) time for the hypergraph with m hyperedges and n nodes. 5. A 2-approximation algorithm based on the Longest Adjacent Path Removing Lee and Ho proposed a simple approximation algorithm called Longest Adjacent Path Removing [12]. Every hyperedge e i with n i nodes has n i a-paths in cycle C. Any n i 1 a-paths together constitute a c-path of hyperedge e i. Therefore, if any one of the n i a-paths is removed, the rest n i 1 a-paths form a feasible embedding for hyperedge e i. The Longest Adjacent Path Removing algorithm simply deletes the longest a-path for each hyperedge. The Longest Adjacent Path Removing has an approximation ratio of 2. It runs in O(mn) time for the hypergraph with m hyperedges and n nodes.

23 CHAPTER. RELATED WORK ON THE MCHEC PROBLEM 1.2. An algorithm for optimal solutions For any fixed integer k and hypergraph with m hyperedges and n nodes, Ganley and Cohoon proposed an algorithm to compute solutions for the MCHEC problem with maximum congestion at most k, or to determine that such solutions do not exist in O((mn) k+1 ) time. Their algorithm was stated as follows: Choose an arbitrary link i in cycle C. For every subset S of hyperedges with S k, check all the possible embeddings such that link i is used by all the hyperedges in S, and link i is not used by any hyperedge that is not in S. In the worst case, each hyperedge in S has n 1 possible embeddings that use link i, and each hyperedge that is not in S has only one unique embedding that does not use link i. Therefore in the worst case, the total number of checked embeddings is k i=1 ( m i ) (n 1) i = O((mn) k ). Embedding the hyperedges requires another O(mn) time, so the algorithm runs in O((mn) k+1 ) time. This algorithm can be used to construct an optimal algorithm running in O((mn) L +1 ) time for the MCHEC problem, where L is the maximum congestion. For the subproblem with constant maximum congestion L, the optimal algorithm runs in polynomial time..2.4 A heuristic Ganley and Cohoon proposed a heuristic approach called Iterated Maximum Independent Set (IMIS) [1]. They claimed that the heuristic performed very well in empirical studies. In a hypergraph with m hyperedges and n nodes, each hyperedge e i consisting of n i sorted nodes {v1 i,vi 2,...,vi n i } has n i possible c-paths (only those consisting of exactly n i 1 a-paths are considered). Each of these n i c-paths can be considered as an arc in cycle C. Notice that if n i > 2, all the arcs from the same hyperedge e i are pairwise intersecting. For all the arcs from all the hyperedges, construct a circular arc graph G ca, in which each node represents an arc and there is an edge between two nodes if the two corresponding arcs intersect in the cycle. Compute a maximum independent set (MIS) for G ca in O((mn) 2 ) time using the algorithm proposed by Gupta et al. [22]. Since all the arcs from a hyperedge with n i > 2 are pairwise intersecting, the MIS can contain at most one arc from each hyperedge. If the MIS contains both arcs from a hyperedge with n i = 2, the size of the MIS must be 2, so an MIS that does not contain two arcs from the same hyperedge can be found exhaustively in

24 CHAPTER. RELATED WORK ON THE MCHEC PROBLEM 14 O(m 2 ) time. Thus, computing an MIS that contains at most one arc from each hyperedge can be done in O((mn) 2 ) time. Once an MIS is obtained, each hyperedge, for which there is an arc in the MIS, is embedded according to that arc. Remove all the arcs in G ca that are from the embedded hyperedge, and repeat the same process until no arcs remain in G ca. An MIS is computed in O((mn) 2 ) time for each round, and at most m rounds are required in the worst case. So the heuristic runs in O(m(mn) 2 ) time.

25 Chapter 4 Efficient algorithms for the MCHEC problem In this chapter, we develop new algorithms for the MCHEC problem in three categories. In the first category is a 1.8-approximation algorithm that improves the previous 2-approximation algorithms [10, 11, 12]. In the second category is an O(( mn) L ) time algorithm to compute optimal solutions for the MCHEC problem, where L is the maximum congestion, m is the number of hyperedges, and n is the number of nodes in the hypergraph. This algorithm improves the previous O((mn) L +1 ) time algorithm [9]. The third one contains two heuristics. Both of our heuristics not only have lower time complexities, but also beat the performance of the Iterated Maximum Independent Set heuristic [1] according to our simulation results. 4.1 A 1.8-approximation algorithm Algorithm In this section, we give a 1.8-approximation algorithm for the MCHEC problem. The basic idea of our algorithm is as follows: The algorithm starts from the Clockwise Embedding, and then re-embeds some hyperedges to reduce the maximum congestion of the Clockwise Embedding. Recall that L is the maximum congestion of the Clockwise Embedding and L is the maximum congestion of an optimal embedding. If our algorithm can re-embed k hyperedges to get an embedding with maximum congestion L k, the approximation ratio of the algorithm is (L k)/l. Since L/2 L (i.e., L/2 is a lower bound on the maximum 15

26 CHAPTER 4. EFFICIENT ALGORITHMS FOR THE MCHEC PROBLEM 16 congestion of an optimal embedding) [11], the approximation ratio of our algorithm is at most 2(L k)/l. If k is large, 2(L k)/l gives a good approximation ratio. If k is small, we shall prove a new lower bound on L in order to achieve a good approximation as well. As shown later, the approximation ratio of our algorithm increases from 1 to 1.8 as k decreases from L/2 to L/10, and then decreases from 1.8 to 1.5 as k decreases from L/10 to 0. That is, the approximation ratio of our algorithm is always bounded above by 1.8. Furthermore, our algorithm has the optimal O(mn) time for the hypergraph with m hyperedges and n nodes. Re-embedding hyperedges decreases congestions on some links in cycle C, however, congestions on some other links could be increased as well. That is, re-embedding k hyperedges decreases the maximum congestion of the Clockwise Embedding by at most k. In our algorithm, we re-embed k hyperedges only when the re-embedding decreases the maximum congestion by exactly k. Thus our algorithm can re-embed at most L/2 hyperedges (i.e., 0 k L/2 ), since L/2 is a lower bound on the maximum congestion of an optimal embedding. To guarantee that re-embedding k hyperedges decreases the maximum congestion of the Clockwise Embedding by exactly k, we introduce the following terms. Recall that l(i) is the congestion of any link i in the Clockwise Embedding. For an integer k in the range 1 k L/2, let g k be the lowest numbered link with l(g k ) L 2k + 1, and h k be the highest numbered link with l(h k ) L 2k + 1. Thus 0 g k h k < n 1. For the example shown in Figure 4.1, n = 10, L = 4, g 2 = 0, h 2 = 7, g 1 = 2, and h 1 = 6. We call a hyperedge a re-embedding candidate with respect to k (or candidate w.r.t. k) if the hyperedge has a node in segment 0,g k, has a node in segment h k + 1,n 1, and has no node in segment g k + 1,h k. Let x k be the number of candidates w.r.t. k. In Figure 4.1, x 2 = 0, x 1 = 1, and hyperedge {0,1,8} is a candidate w.r.t. k = 1. In our algorithm, we re-embed k hyperedges only when x k, the number of candidates w.r.t. k, is greater than or equal to k. We re-embed any k out of the x k candidates such that the c-path for each of the k candidates does not contain any link i in the range g k i h k (see Figure 4.2). Therefore, re-embedding k candidates w.r.t. k decreases the maximum congestion by exactly k in our algorithm. The definitions of g k, h k, and the candidate w.r.t. k imply that g k+1 g k, h k+1 h k, and any candidate w.r.t. k+1 is also a candidate w.r.t. k (i.e., x k+1 x k ), where k is in the range 1 k < L/2. To re-embed as many hyperedges as possible, our algorithm checks whether x k k is true starting from k = L/2. If it is true, our algorithm re-embeds

27 CHAPTER 4. EFFICIENT ALGORITHMS FOR THE MCHEC PROBLEM 17 c-path for {0,1,8} c-path for {1,4,7} g c-path for {2.,4} 7 8 h g c-path for {,5,7} h Figure 4.1: Re-embedding candidate any k out of the x k candidates to decrease the maximum congestion by k. Otherwise, our algorithm decreases k by one and repeats. Eventually, our algorithm terminates with k hyperedges re-embedded, where k is one of the values in {0,1,..., L/2 }. The outline of our algorithm is as follows: We start with the Clockwise Embedding, and then we try to re-embed k candidates w.r.t. k to decrease the maximum congestion of the Clockwise Embedding by k. The re-embedding process starts from k = L/2. If x k k then we re-embed k or k + 1 candidates and the algorithm terminates. Otherwise, k is decreased by one and the re-embedding process is repeated. As shown later, the algorithm has an approximation ratio of 1.8 except for a few special cases of fixed L. Although the O((mn) L +1 ) time algorithm in [9] or the O(( mn) L ) time algorithm given in section 4.2 can be used to find optimal embeddings for the special cases, the time complexity is high in practice. We give a subroutine to handle those special cases. The subroutine is efficient and guarantees an approximation ratio of 1.8 for the special cases. Our algorithm and subroutine are given in Figure 4..

28 CHAPTER 4. EFFICIENT ALGORITHMS FOR THE MCHEC PROBLEM 18 c-path for {0,1,8} after the re-embedding c-path for {0,1,8} in clockwise embedding g h g h Figure 4.2: C-paths for the candidate w.r.t. k = 1 in the Clockwise Embedding and after the re-embedding Analysis Algorithm R Embedding terminates with k or k + 1 hyperedges re-embedded, where k is one of the values in {0,1,..., L/2 }. Let L k denote the maximum congestion achieved by our algorithm. The following lemma holds for L k : Lemma L k = L k or L k = L k 1. Proof: When algorithm R Embedding terminates without calling Subroutine Special Cases, either k candidates w.r.t. k are re-embedded, or k + 1 candidates w.r.t. k, including at least one candidate w.r.t. k + 1, are re-embedded. After a candidate w.r.t. k is re-embedded, the congestion of each link i with g k i h k is decreased by one and the congestion of each link i with i < g k or i > h k is increased by at most one. Assume that k candidates w.r.t. k are re-embedded. For each link i with g k i h k, the congestion of link i after the re-embedding is l(i) k L k. For each link i with i < g k or i > h k, the congestion of link i after the re-embedding is at most l(i)+k L 2k+k = L k. So after the re-embedding, maximum congestion L k is L k.

29 CHAPTER 4. EFFICIENT ALGORITHMS FOR THE MCHEC PROBLEM 19 Procedure R Embedding Input: A hypergraph on the same node set of the cycle. Output: An embedding of the hypergraph in the cycle. begin 1. Perform the Clockwise Embedding for the hypergraph. Let L denote the maximum congestion of the Clockwise Embedding. 2. Find links g k and h k, and compute x k for k = 1,2,..., L/2. x k is defined to be 0 for k = L/2 + 1 and k = 0.. k := L/2. while k 1 do if (x k k) then goto step 4 else k := k If x k k + 1 and x k+1 1 then re-embed k + 1 arbitrary candidates w.r.t. k including at least one candidate w.r.t. k + 1 else if ((L = 2 or L = 4) and k = 0) or (L = 12 and k = 1) then call Subroutine Special Cases else re-embed k arbitrary candidates w.r.t. k. end. Subroutine Special Cases Input The Clockwise Embedding of the hypergraph. Output An embedding of the hypergraph in the cycle. begin /* Let s be a link with maximum congestion L in the Clockwise Embedding, E be the set of hyperedges whose c-paths contain link s in the Clockwise Embedding, and P i be the c-path for e i E that does not contain link s. */ If (L = 2 or L = 4) and k = 0 then for every e i E do if re-embedding e i as P i reduces L by one then re-embed e i as P i and return else /* L = 12 and k = 1 */ for every pair e i,e j E do if re-embedding e i as P i and e j as P j reduces L by two then re-embed e i as P i and e j as P j and return re-embed an arbitrary candidate w.r.t. k = 1. Return end. Figure 4.: Embedding algorithm for the MCHEC problem.

30 CHAPTER 4. EFFICIENT ALGORITHMS FOR THE MCHEC PROBLEM 20 Assume that k+1 candidates w.r.t. k are re-embedded. For each link i with g k i h k, the congestion of link i after the re-embedding is l(i) (k + 1) L k 1. For each link i with i < g k+1 or i > h k+1, the congestion of link i after the re-embedding is at most l(i) + (k + 1) L 2(k + 1) + (k + 1) = L k 1. For each link i with g k+1 i < g k or h k+1 i > h k, since the k + 1 re-embedded candidates include at least one candidate w.r.t. k+1, the congestion of link i after the re-embedding is at most l(i) 1+k L 2k 1+k = L k 1. So after the re-embedding, maximum congestion L k is L k 1. Assume that Subroutine Special Cases is executed. For k = 0, L k = L 1 or L k = L. For k = 1, L k = L 2 or L 1. According to the above lemma, the approximation ratio of our algorithm is (L k)/l or (L k 1)/L. If the algorithm terminates with a large k, we use L/2 as a lower bound on L and get an approximation ratio of (L k)/ L/2 or (L k 1)/ L/2. This suggests that when terminating with a large k, our algorithm has a good approximation ratio. However, if the algorithm terminates with a small k, for example, k = 0, then the approximation ratio given by L k / L/2 is 2, which is no better than the Clockwise Embedding. In the following, we shall prove that if the algorithm terminates with a small k, then a new lower bound that is better than L/2 can be found. Using this better lower bound, our algorithm has a good approximation ratio as well when k is small. Lower bound L/2 is obtained by using a cut consisting of two links in the cycle. The new lower bound involves three links. To derive the new lower bound, we need some new notation. Let x, y and z be any three distinct links in the cycle. Without loss of generality, we assume that 0 x < y < z n 1. We define four disjoint subsets of hyperedges as follows: W : the set of hyperedges such that each hyperedge has a node in segment z+1,x, a node in segment x + 1,y, and a node in segment y + 1,z ; X : the set of hyperedges such that each hyperedge has a node in segment z + 1,x, has NO node in segment x + 1,y, and has a node in segment y + 1,z ; Y : the set of hyperedges such that each hyperedge has a node in segment z +1,x, a node in segment x + 1,y, and has NO node in segment y + 1,z ; Z : the set of hyperedges such that each hyperedge has NO node in segment z + 1,x, has a node in segment x + 1,y, and a node in segment y + 1,z.

31 CHAPTER 4. EFFICIENT ALGORITHMS FOR THE MCHEC PROBLEM 21 c-path for {0,1,8} 9 z 0 1 c-path for {1,4,7} c-path for {2.,4} 8 2 x 7 c-path for {,5,7} y Figure 4.4: Lower bound involving three links x, y, and z For the example shown in Figure 4.4, if we assume that x = 2, y = 6, and z = 9, then hyperedge {1,4,7} belongs to set W, hyperedge {0,1,8} belongs to set X, hyperedge {2,,4} belongs to set Y, and hyperedge {,5,7} belongs to set Z. The intuition for proving the new lower bound involving three links is as follows: When algorithm R Embedding terminates with some small k, for the two corresponding links g k, h k and the highest numbered link n 1, we get four disjoint subsets of hyperedges W, X, Y, and Z as defined above. The congestion of link g k (resp. h k ) in the Clockwise Embedding is l(g k ) L 2k+1 (resp. l(h k ) L 2k+1). So when k is small, l(g k ) (resp. l(h k )) is large. A hyperedge whose c-path contains link g k (resp. h k ) in the Clockwise Embedding belongs to one of the sets W, X, and Y (resp. W, X, and Z), that is, l(g k ) = W + X + Y (resp. l(h k ) = W + X + Z ). Therefore, when k is small, W + X + Y (resp. W + X + Z ) is large. Based on the above observations, a new lower bound can be obtained when k is small. The following lemma gives the new lower bound: Lemma For any three links x, y and z with 0 x < y < z n 1 in the cycle, L 2 W + 1 ( X + Y + Z ).

32 CHAPTER 4. EFFICIENT ALGORITHMS FOR THE MCHEC PROBLEM 22 Proof: Let I denote an arbitrary embedding, and l I (i) denote the congestion on link i in embedding I. We have L min I {max{l I (x),l I (y),l I (z)}}. Let T = l I (x)+l I (y)+l I (z). Notice that in any embedding I, the c-path for any hyperedge in W must contain at least two of links x, y, and z. Therefore, each hyperedge in W contributes at least 2 to T in any embedding I. Similarly, the c-path for any hyperedge in X, Y, or Z must contain at least one of links x, y, and z in any embedding I, so each hyperedge in X, Y, or Z contributes at least 1 to T. Based on the above analysis, it is concluded that in any embedding I, So in any embedding I, T 2 W + X + Y + Z. max{l I (x),l I (y),l I (z)} 2 W + 1 ( X + Y + Z ). So L 2 W + 1 ( X + Y + Z ). It is worth pointing out that there exist hypergraphs for which the lower bound derived above is equal to the maximum congestion of an optimal embedding (i.e., the lower bound derived above is tight). An example of such hypergraphs is as follows: Let x, y, and z be three distinct links with x < y < z in the cycle. We construct a hypergraph H(V,E h ) with E h = W X Y Z. Each hyperedge in set X (resp. Y, Z) has nodes only in segment y + 1,z + 1 (resp. z + 1,x + 1, x + 1,y + 1 ). W consists of hyperedges in three disjoint subsets W x, W y and W z. Every hyperedge in W x (resp. W y, W z ) does not contain any node in segment z + 2,x (resp. x + 2,y, y + 2,z ). The sizes of sets W x, W y and W z are defined as W x = W y = W z = W X + 2 Y Z, W X Y + 2 Z, and W + 2 X Y Z.

34 CHAPTER 4. EFFICIENT ALGORITHMS FOR THE MCHEC PROBLEM 24 Case 1: L k = L k 1. Taking g = g k+1 and h = h k+1 in inequality (4.1), we have X = x k+1. Since the algorithm terminates with maximum congestion L k, we have x k+1 k L/2, which implies that g k+1 < h k+1. Since l(g k+1 ) L 2(k + 1) + 1 and l(h k+1 ) L 2(k + 1) + 1, L 1 {l(g k+1) + l(h k+1 ) x k+1 } 1 {2(L 2(k + 1) + 1) k} = 1 (2L 5k 2). Therefore, an upper bound on the approximation ratio of the algorithm is Since L/2 is also a lower bound on L, L k (L k 1) L 2L 5k 2. L k L L k 1. L/2 So the approximation ratio of the algorithm is bounded above by min{ L k 1 (L k 1), L/2 2L 5k 2 }. Function (L k 1)/ L/2 is decreasing in k and function (L k 1)/(2L 5k 2) is increasing in k. For k L/10, (L k 1)/ L/2 1.8; for k L/10 1, (L k 1)/(2L 5k 2) 1.8. Case 2: L k = L k. In this case, either x k = k or x k+1 = 0. Assume that x k = k. Taking g = g k and h = h k in inequality (4.1), then X = x k = k and g k < h k. Since l(g k ) L 2k + 1 and l(h k ) L 2k + 1, L 1 {l(g k) + l(h k ) x k } 1 {2(L 2k + 1) k} = 1 (2L 5k + 2). The approximation ratio of the algorithm is bounded above by min{ L k L/2, (L k) 2L 5k + 2 }.

35 CHAPTER 4. EFFICIENT ALGORITHMS FOR THE MCHEC PROBLEM 25 For k L/10, (L k)/ L/2 1.8; for k L/10 1, (L k)/(2l 5k + 2) 1.8. Assume that x k+1 = 0. So L 1 {l(g k+1) + l(h k+1 ) x k+1 } 1 {2(L 2(k + 1) + 1)} = 1 (2L 4k 2). The approximation ratio of the algorithm is bounded above by min{ L k L/2, (L k) 2L 4k 2 }. For k L/10, (L k)/ L/2 1.8; for k L/10 1, (L k)/(2l 4k 2) 1.8, except for the following three special cases: 1 L = 2 and k = 0, 2 L = 4 and k = 0, and L = 12 and k = 1. we shall prove that L k /L 1.8 holds for 1, 2, and in the following. Recall that s is a link with maximum congestion L in the Clockwise Embedding, E is the set of hyperedges whose c-paths contain link s in the Clockwise Embedding, and P i is the c-path that does not contain link s for each e i E. Let I opt be an optimal embedding. Case 1 can be divided into two sub-cases according to subroutine Special Cases. Subcase 1.1 is that one hyperedge e i E is re-embedded as P i. So L k = L 1 = 1, which implies that L k /L = 1. Sub-case 1.2 is that re-embedding any hyperedge in E does not decrease the maximum congestion. So no hyperedge is re-embedded and L k = L = 2. We shall prove that 2 is a lower bound on L for sub-case 1.2, which implies that L k /L = 1. If any one of the following three conditions holds, then L 2. Condition 1: There exists one hyperedge in E whose c-path in I opt contains both links s and n 1. Notice that every hyperedge in E is separated by cut (s,n 1). So the c-path for any e i E in any embedding must contain at least one of links s and n 1. If the condition 1 holds, then the sum of congestions on links s and n 1 in I opt is at least, which implies that L 2.

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