International Journal Computer Engineering and Applications, INTELLIGENT ROUTING BASED ON ACO TECHNIQUE F FAULT RING IN 2D-MESHES Gaytri Kumari Gupta research sclar, Jharkhand Rai University, Ranchi-India gayatri.gupta27@gmail.com Sudhanshu Kumar Jha, Assistant Pressor,AssistantPressor, National Institute Technology, Jamshedpur-India sudhanshukumarjha@gmail.com, ABSTRACT We present an intelligent routing based on ACO technique for communication messages via fault ring in 2D meshes. A fault ring [1] is a ring consists the fault-free component around the faulty nodes or faulty links to bypass faults for uninterrupted communication. Ant Colony optimization technique finds and gives an optimized route in 2D-meshes from source to destination, saving time and efforts. When a flit passes through a fault ring, it needs to be routed in an optimal to reduce delays and to optimize cost. Our proposed routing algorithm with ACO technique is driven by the foraging behavior actual ants colonies. The simulation results sw the effectiveness the routing based on ACO technique by comparing it with the algorithm defined in [1]. Index Terms ACO technique, optimal, fault ring, intelligent routing etc. INTRODUCTION nt colony optimization is a meta -heuristic A approach toward solving many problems finding srtest like (the first ACO algorithm)travelling salesman problem[2], single destination srtest problems, all pairs srtest problems, problem protein folding, finding the functional shape or conformation a protein in 2D or 3Dspace etc..this technique has also been used in the field telecommunications such as routing and load balancing. ACO [3] technique is actually inspired by the real ant s foraging behavior or we can say that their indirect communication through pheromones (a kind scent that an ant deposits to tempt changes in the surroundings) secretions for marking the visited place helping others colony ants, following the to search food and carrying it to their nest via the selected optimal which hel to save time Gaytri Kumari Gupta, Sudhanshu Kumar Jha 1
INTELLIGENT ROUTING BASED ON ACO TECHNIQUE F FAULT RING IN 2D-MESHES and efforts. The intelligent decision is made by Ants (routing agents) on the basis pheromones concentration and some other parameters. ACO algorithms was first introduced by Marco Dorigo and colleagues in the early 1990 s[5][6][7].it is one the most important approximate optimization technique [4].this technique also plays role in Artificial Intelligent in terms Swarm intelligence which works on designing multiintelligent agent system based on the behavior social insects, birds, and animals etc. In literature, we have also found that this technique also works well in NOC s architecture which provides scalable structure and balancing the communication between the multiple cores. Here, ACO technique hel to find and optimize routes in mesh-based NOC s [8]. The TSP and the protein folding problem under lattice models belong to an important class optimization problems known as combinatorial optimization (CO) like TSP and the protein folding problem under lattice models is a kind optimization problem, graph coloring problem[9-10], vehicle routing problem[11] etc. can also be solved by ACO technique. Our paper is aimed to discuss an intelligent routing technique based on ACO being performed in a 2D-mesh network. In an MPP environment fault (node fault or link faulty) may be fatal for any system that must be treated carefully. There are so many faults tolerant routing algorithm proposed for mesh and other topology [12, 13]. Boppana and chalasini proposed a fault-tolerant routing algorithm for 2Dmeshes were able to handle special convex shape faults like L, T or + with four virtual channels and some non-convex faults too. In this paper, we first propose an intelligent routing algorithm based on multi-aco for finding the optimal. We next present a comparison among earlier used routing algorithm and the proposed routing algorithm in terms optimization. The rest the paper is organized as follows. Section I describes our proposed routing algorithm. Section II presents differences among our proposed routing algorithm and few earlier used routing algorithms. Section III concludes our paper. from different community work to find optimal for routing. Step1: A colony red and black Ants starts their journey from source to different directions (s row and column wise) in order to get food i.e. to reach the destination point. As they move forward they secrete few amounts pheromones to create a trail so that they can come back to their nest or source point. Step 2: Every Ants (routing agent) maintains their own trail to reach their nest (source point). Ants (red or black) communicate with only their own community. Step 3: when Ants get food, they carry food and follow their optimal trail to reach nest and secretes high level pheromones to make trail even with more scent, while some other ants may be wandering and don t get food but as Ants have capability indirect communication, they will get the idea getting food through the most scented trail others ants. Ants start running into the trail pheromones and leave their own search. Step 4: Ants may find multiple routes from the nest to food but the most promising route will be the route followed by the largest number ants having a high level pheromones. For example, as swn in Fig 1. Ant s nest is connected to a food source by multiple bridges having different lengths. Initially, all the ants are at the nest, when they start their journey, some the ants from the red ant community and some from black ant community moves to different s and secrets few amounts pheromone. When some the ants reach to the food source, they take food grains and moves back to the nest, while returning ants secrete a high level pheromones to make route selected, while some others are still in search. Through indirect communication, if they get the knowledge highly secreted pheromone trial and may get success to find the food source so they follow their own trail while some them are wandering and there is no communication so they stop searching and follows the high pheromone trail swn in Fig 1(c). Therefore multi-ant colony optimizations are a useful technique to find optimal as well as alternate routes if available. I. INTELLIGENT ROUTING ALGITHM Our proposed algorithm is based on multi-ant colony optimization [14] technique where ants Gaytri Kumari Gupta, Sudhanshu Kumar Jha 2
International Journal Computer Engineering and Applications, Fig 2: Application routing algorithm on different situations in a 2D mesh network n n size. Multi-Ant colony optimizations routing algorithm: ( a) food ( b) food Fig 1. The concentration Pheromones produces one or multiple optimal s for communication. Implementation the algorithm is as follows by a comparative analysis e_cube, f_cube 2, and our proposed (ACO based) algorithm: Basic terminology: A r 1 A r n is ants from red community ant and A b 1 A b n are the ants from black community. Pheromone = number ants visited (counter variable) the location community wise. For simplicity, we are discussing e_cube and f_cube 2 routing algorithm. S is the source point (nest) from where ants have to starts their journey acquiring food. D is the destination point (food). ( c) food ACO meta-heuristic approach 1. Initialize parameters and pheromones trails. Red_counter: =0; black_counter: =0; S (a1, b1)//source point or nest location. 2. While total_cycle do Loop for each direction until food is found Equal number ants starts moving from (a1, b1) to diverge in different directions. Increment the counter by 1; //inializes pheromone table. If found faulty_ link then f_ring exists; Move on f_ring until food is found or reach nearest to food. Increment the counter by 1 for each p; End if End loop Ants check for high pheromones back to nest and sto. // largest counter for each community. Update pheromones table. Increment the counter by 1. End while 3. Return the best solution; Nest /Source point Gaytri Kumari Gupta, Sudhanshu Kumar Jha 3
INTELLIGENT ROUTING BASED ON ACO TECHNIQUE F FAULT RING IN 2D-MESHES II. COMPARATIVE ANALYSIS OF FEW ROUTING ALGITHMS USED IN 2D-MESHES. e_cube routing algorithm: - In a fault-free network, e_cube [1], a message travels in a row until it is in the similar column as the destination and then takes column. A row message may take column, but before doing that it changes itself into a column message. A column message never changes its type in e_cube routing. Therefore for the e_cube between S 1 and D 1 cannot be defined as node (1, 1) has a faulty link {(1, 1)-(1, 2)} incident on it, and e_cube routing algorithm does not tolerate faults due to its non-adaptive nature so as with route between S2 and D2 also. 0,3-1,3-2,32,5(al ter nate ) 3,34, 3-4,4 1,0-2,0-3,3-4,3,4,4 3,0-3,1- Table 1: swing a comparative analysis optimal s from source to destination by different routing algorithms. Sourc e - Desti na tion e_cub e f- cube 2 ACO S1-D1 does not get destinati on due to fault 2,4 2,4 0,1-0,2- S2- D2 S3- D3 - - - 4,2-6 1,0-1,1-2,4-3,4-3,5-4,5-4,4 6/8 1,0-1,1-10 8 4,2-4,23,2 faulttol era nce 4 NO 4 YES 4 YES f_cube2 routing algorithm: - In f_cube 2[1] routing algorithm, it is assumed that each node knows the status the link incident on it, and its position in the f_ring if any the links is faulty. let us consider the routing packets from S 2(a 1,b 1) to D 2(a 2,b 2).packet is routed as per e_cube from(1,0)(1,1),gets a faulty link {(1,1)-(1,2)} therefore, packet is misrouted so it will move clockwise as a 1<b 1 (NS message) and moves towards (2,1) s e-cube (2,1)-(2,2)-(2,3)-(2,4) now, it becomes a NS message and move toward (3,4) but it again found a faulty link {(3,4)-(4,4)} so it is misrouted and take a clockwise turn and moves to (3,5)-(4,5)-(4,4). Finally, it reaches the destination (4, 4) visiting 10. Our proposed routing algorithm based on multi- Ant colony optimization technique: - The proposed routing algorithm is based on the real ant's foraging behavior to find the optimal from the source (nest) to the destination (food).in our algorithm ants (red and black) works as a searching agent which finds the srtest route between source and destination in less time. Ants from different community deposits pheromones different type and color in different situations. Ants share information from the same community. Ants have a strong sense smells which hel them to be with their group and smells the food near to them. Due to multiple ants, our algorithm may produce the srtest route as well as an alternate srtest route which hel to route messages even if congestion occurs. Let us consider the route between S2 and D2 depicted in fig 2. Colony ants (red and black) are at node (1, 0), they diverge in different directions to their next p i.e. (1, 1), (0, 0), (2, 0), during foraging process they secrete few amounts pheromones (initialize the counter) to the visited. At node (1,0) Gaytri Kumari Gupta, Sudhanshu Kumar Jha 4
International Journal Computer Engineering and Applications, they find a faulty link{(1,1)(1,2)} on their way, so ants would change the,again ants at node(1,1) diverse to node(0,1) or (2,1). Since they have found faulty link on their, they are on fault ring, therefore, they would keep running on the ring till they get the food or they are at the nearest point the food as ants have strong sense smells.ants would meet at node(2,3), instead moving to node(2,4) as in fcube2 algorithm (based on e_cube algorithm), they will move to (3,3)-(4,3) then finally they reach at (4,4). Ant following the (1,0)- (1,1)-(2,1)(2,2)-(2,3)-(3,3)-(4,3)-(4,4) will reach the destination first and they visited only 8 from the source. In Parallel, other ants are also moving towards destination.ants moving via the (1, 0)-(2, 0)-(2,1)- (2,2)-(2,3)-(3,3)-(4,3)-(4,4) also reaches destination at the same time by visiting 8 only. While returning to source point (nest) ants update pheromones (increment the counter) to a high level and make the more scented. By the time, other ants (red and black) may be wandering and will follow the with high pheromones (comparing own counter to the other s counter value their own community, if their counter is less then stop their own search and follow the more scented ) as ants are intelligent to cooperate by their indirect communication capability. III. CONCLUSION We have seen that ACO based routing algorithms have been used for solving a variety problems. Our proposed multi ant colony optimization routing algorithm solves the routing problem as well as congestion by providing alternate at some extents. In fig. 2 routes for S2-D2, our algorithm resulted in the optimal by visiting only 8 while, f_cube2 routing algorithm required 10 for routing a packet. Our algorithm also produces an alternate optimal that may be used on the occurrence congestion in a network. Overall our proposed algorithm yields better result as swn in table1. REFERENCES [1]. V.Boppana, S. Chalasani, Fault-tolerant wormle routing algorithms for mesh networks, IEEE Trans. Comput. 52.44(7) (1995)848-864. [2]. Dorigo, M., Gambardella, L.M., Ant colony system: A cooperative learning approach to the traveling salesman problem, IEEE Transactions on Evolutionary Computation 1(1) (1997) 53 66 [3]. Christian Blum, Ant colony optimization: Introduction and recent trends, Physics Life Reviews 2 (2005) 353 373. [4]. Dorigo M, Stützle T. Ant Colony Optimization. Cambridge, MA: MIT Press; 2004 [5]. Dorigo M, Optimization, learning and natural algorithms. Ph.D. thesis, Dipartimento di Elettronica, Politecnico di Milano, Italy, 1992 [in Italian]. [6]. Dorigo M, Maniezzo V, Colorni A, Positive feedback as a search strategy. Technical Report 91-016, Dipartimento di Elettronica, Politecnicodi Milano, Italy, 1991. [7]. Dorigo M, Maniezzo V, Colorni A. Ant System: Optimization by a colony cooperating agents. IEEE Trans Syst Man Cybernet Part B1996;26(1):29 41. [8]. Luneque Silva Junior,, Nadia Nedjahb, Luiza de Macedo Mourelle, Routing for applications in NoC using ACO-based algorithms. Applied St Computing, Elsevier 13 (2013) 2224 2231. [9]. I. A. Wagner, M. Linden baum, A. M. Bruckstein, Efficient Graph Search by a Smell-Oriented Vertex Process, Annuals Mathematics and Artificial Intelligence, 24, p. 211-223,1998. [10]. I. A. Wagner, M. Linderbaum, A. M. Bruckstein, ANTS: Agents, Networks, Trees, and Subgraphs, IBM Haifa Research Lab, Future Generation Computer Systems Journal, North Holland (Editors: Dorigo, Di Caro, and Stutzel), vol.16, no 8, p. 915-926, June 2000. [11]. M. Dorigo, G. Di Caro, The Ant Colony Optimization MetaHeuristic, in Corne D., Dorigo M. and Glover F., New Ideas in Optimization, McGrawHill, May 1999. ISBN: 0077095065. [12]. T. Lee and J. Hayes, A fault-tolerant communication scheme for hypercube computers, IEEE Trans. Computers, vol. 41, pp. 1242-1256, Oct. 1992. [13]. M. Peercy and P. Banerjee, Distributed algorithms for srtest- deadlock-free routing and broadcasting in arbitrarily faulty hypercubes, 20th Ann. Int l Symp. Fault-tolerant Computing, pp. 218225, 1990. [14]. Kwang Mong Sim and Weng Hong Sun, Multi Ant- Colony Optimization for Network Routing, Proceedings the first International Symposium on Cyber Worlds(CW 02)0-7695-1862-1/02$17.00 2002 IEEE. Gaytri Kumari Gupta, Sudhanshu Kumar Jha 5