Optimization of ACO for Congested Networks by Adopting Mechanisms of Flock CC M. S. Sneha 1,J.P.Ashwini, 2, H. A. Sanjay 3 and K. Chandra Sekaran 4 1 Department of ISE, Student, NMIT, Bengaluru, 5560 024, India. 2,3 Department of ISE, Faculty, Bengaluru 556 0024, India. 4 Department of CSE, Faculty, NITK, Suratkal 575 014, India. e-mail: 2 ashwini.janagal@gmail.com Abstract. Network routing is one area in computational science that is being updated constantly by new algorithms and technologies. Most of the prevalent algorithms provide good performance but at a particular stage, lack in factors such as congestion control, noise reduction, fault tolerance, robustness etc. Many efforts to achieve these factors have been taken. As one of the outcomes, recently a category of algorithms called Bioinspired algorithms are added to the computer science field. They have been proved to provide robust, reactive and self-adaptive solutions for hard real world problems. ACO (Ant Colony Optimization) is one amongst them which provides reliable data transmission. But there is no consideration of congestion in this algorithm. This paper proposes an optimization technique for ACO by combining the ideas of Flock-CC (Flock Congestion Control). The proposed algorithm resulted in average improvement of 34.07% in transmission time in comparison to ACO in congested networks. Keywords: Bio-inspired algorithms, ACO, Flock-CC, Congestion, Network routing. 1. Introduction Natural Computing is an approach of observing and studying the natural systems and mimicking them in developing new computing technology [1]. Some approaches of natural computing can be simulating the natural systems on computers, to help in the experiments on them and studying the biological processes to help in generating solutions to hard computational problems. The latter approach is called as Bio-inspired computing. This came to be the origin of a category of algorithms called Bio-inspired algorithms. Bio-Inspired Algorithms (BIAs) do not try to perfectly imitate the complex functioning of biological processes. They use them as an inspiration to solve hard real world problems in computation technology. Some fields where BIAs can be used are neural network, sensor network, swarm intelligence, communication networks and protocols etc. Network routing is one such area in computational science that is being updated constantly by new algorithms and technologies. Most of the prevalent algorithms provide good performance but at a particular stage, lack in factors such as congestion control, noise reduction, fault tolerance and robustness etc. Many efforts to achieve these factors have been taken. One such effort is to employ Bio Inspired Algorithms (BIAs) that are non-linear, robust and noise tolerant. They have wide variety of applications. Since it is an upcoming field, they have not yet been effectively implemented in the areas they find applications in. The well known BIAs are Ant Colony Optimization (ACO) and FlockCC (Flock based Congestion Control). The area in which ACO finds application is routing. ACO finds out an optimum solution and causes that shortest path to be used always. In case there is congestion due to this behaviour, recovery takes lot of time. To reduce this, we implement FlockCC at congested node that clears congestion at a faster rate by distributing the load to neighboring nodes while routing. ACO on its own has been used to solve few of the problems like travelling salesman problem ([5,6]), self organization in sensor networks ([7]), communication among sensor nodes ([8]) etc. But here we propose a different approach of optimizing it for routing in congested network by adopting ideas from FlockCC algorithm, which can be used on its own to route packets ([3]). This being the first stage of such an experiment, wired networks have been considered and it can be easily extended to wireless ones with few modifications. Corresponding author Elsevier Publications 2013.
M. S. Sneha, et al. Organization of this paper is as follows: Section 2 briefs out few works that have been done in this direction. Section 3 explains the general ACO and Flock CC algorithms. Proposed algorithm is presented in section 4. Section 5 shows experiments and results. Section 6 concludes the paper. 2. Related Work The work by M. Dorigo et al. [2] describes the pheromone trail laying and foraging behavior of ants as an autocatalytic process. Positive feedback accounts for rapid discovery of very good solutions. A model is derived from the study of artificial ant colonies and Ant system, Ant quantity and ant density algorithms are designed. The artificial ants here will have some memory and will live in an environment where time is discrete. Pavlos Antoniou et al. in their work [3] deal with the problem of congestion in wireless sensor networks (WSNs) and proposes a robust and self adaptable nature-inspired congestion control approach. Flocking and obstacle avoidance behavior of birds forms a source of inspiration to guide packets bypass obstacles like congestion regions and dead node zones. Jose Alex Pontes Martins et al. [4] deals with the Mobile ad hoc networksand proposesa routingmechanismcalled Ant-DYMO. It uses some characteristics from the Dynamic MANET On-demand Routing protocol and other MANET protocols in order to propose the new routing algorithm. The packet transfer from one node to the other, usage of memory associated with the ants to store the traversed nodes and coming back to the sender following the same path are all effectively explained through algorithms. 3. ACO and FLOCK-CC The ant colony optimization algorithm (ACO) [3] is a probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs. This algorithm is a member of ant colony algorithms family, and it constitutes some metaheuristic optimizations. When individual ants move from their nest to a food source and back, they lay a chemical substance called pheromone. Other ants searching for food get attracted by these trails and follow such paths by reinforcing them even more. Eventually, an ant which reaches to the food faster comes back to the nest on the same trail and travels number of times in its path. This results an increase in the pheromone deposition on the shortest path and hence the trail-laying and trail-following behavior leads to the rapid emergence of optimal solution. Over time, the pheromone trail starts to evaporate, thus reducing its attractive strength. Longer an ant takes to travel between food and the nest, more time the pheromones have to evaporate. A short path, by comparison, gets marched over more frequently, and thus the pheromone density becomes higher on shorter paths than longer ones. The idea of the ant colony algorithm is to mimic this behavior with simulated ants walking around the graph representing the problem to solve. This system is based on positive feedback (the deposit of pheromone attracts other ants that will strengthen it themselves) and negative (dissipation of the route by evaporation prevents the system from thrashing). Routing by the means of ACO is implemented by researchers as: At definite time intervals, forward ants (data packets) are sent from source to predefined destinations. They are allowed to traverse the network, storing the identity of nodes they visited on a stack. Once the destination is reached, forward ant dies and creates a backward ant by giving all the information collected in its journey. Backward ant follows same path as the corresponding forward ant, updates pheromone values on links suitably (shortest path gets more pheromone) and reaches the source. At source, appropriate changes are made in the routing table based on the length of the path traversed by ants. An alternate path is reserved and can be used if the shortest link fails. The following advantages can be pointed out from the mechanism of ACO: Packets are always delivered in sequence and in the same route. So reliability is more. There is inherent parallelism in ACO Positive feedback accounts for rapid discovery of good solutions and negative feedback avoids premature convergence. Can be used in dynamic applications because of its adaptive nature. ACO has few disadvantages: Since packets are sent through the route one after the other, time taken for the transmission is more. Congestion model is not clearly defined. Waiting for new path to emerge in the case of congestion is time consuming. 188 Elsevier Publications 2013.
Optimization of ACO for Congested Networks by Adopting Mechanisms of Flock CC According to [1], at each iteration of the algorithm, some amount of pheromone is evaporated and the trail intensity on the link is incremented by a constant amount or by an amount that depends on the bandwidth of the link. This intensity is used to find out the transition probability of a packet from one node to the other. Flocking is the behaviour exhibited when a group of birds called a flock, are in flight. The flocking behaviour of birds is mimicked in [3] to design a BIA called Flock-CC to handle congestion in WSN (Wireless sensor networks). Here, packets are modelled as birds flying over a topological space (sensor network). The packets are generated by nodes and are guided to form flocks and fly towards a global attractor (destination), while trying to avoid obstacles (congested regions). The direction of motion is influenced by (a) Repulsion force by packets located on congested nodes. (b) Attractive forces by neighbouring packets located on low contention nodes. (c) Gravitational force in the direction of the destination. Due to the effect of these attractions and repulsions, packets tend to keep away from the congested nodes, thereby allowing them to recover. Nodes located closer to the sink are chosen with higher probability as next hop nodes. Since the packets are modeled as birds, in order to achieve congestion control, packet takes motivation from the limited visual field of birds. Here packet i can see only a fraction of nodes among all the nodes in the transmission range. So, FoV extends forward in the direction of decreasing hop distance towards the destination. This mechanism reveals the following advantages of FlockCC: Parallel transmission of packets through all the possible nodes in the network decreases transmission time. Since it is specially designed for the purpose, congestion control is easy and fast. We can also make out few disadvantages of FlockCC: Almost all possible paths in field of view are traversed. This includes the path with highest delay also. So, packets coming through that path might take long time to reach destination. If time out occurs before some packet coming through longer routes reach destination, retransmissions can be asked for. These retransmissions become useless once original packets are received after time out period. Packets are not always received in sequence. So, destination has to wait until all the packets are received and then additional process of rearranging them should be carried out. The normalized node loading indicator at a node is the good measure for repulsive force and normalized channel loading is a good measure of attractive force. The attraction and repulsion forces are captured according to the required level of influence through a desirability function. 4. Proposed Framework Even though routing and congestion seem to be two different problems, they are actually interrelated. An inefficient routing can lead to congestion and once congestion is detected, future packets must be routed away from congested nodes. Therefore, for congestion controlled routing, the methods employed for both efficient routing and congestion control must be collaborated. It has been proved that Bio-inspired approaches offer robust and adaptable algorithms with higher degree of performance. So, two BIAs mentioned above (ACO and Flock-CC) are used in this regard. The idea here is to use ACO for routing and it will always be active. Whenever congestion is reported, Flock-CC is initiated. Pictorial representation of this proposed algorithm s working is shown in figure 1. Figure 1. ACO and FlockCC combination. Elsevier Publications 2013. 189
M. S. Sneha, et al. Figure 2. Combination of ACO and Flock-CC. From the advantages and disadvantages of both ACO and Flock-CC discussed in the previous section, we can make out that, ACO is more reliable but weak in handling congestion. Efficient congestion control approach is to use FlockCC. But FlockCC lacks the control over paths used to go towards the destination. It can make packets go to such nodes that have no paths to destination and hence cause loss of packets. To overcome this, we can make a controlled use of FlockCC. That is, use FlockCC only at the point of congestion and restrict it to distribute the packets only to adjacent nodes of congested node. To employ this integration, each node must be capable of working with both ACO and Flock-CC algorithms. Integrated system works as shown in figure 2. Forward ant, backward ants are used as said by ACO and routes are found out. Using these routes, data packets are sent. Intermediate nodes, where the chance of congestion is high, constantly monitor the queue length factor which contributes for congestion. If any signs of congestion appear, congested nodes change over to Flock-CC. Here, overflow in queue is the symptom of congestion. We take the maximum number of data packets that can be handled by a node as some threshold, and exceed in this threshold as the indication of congestion. Once congestion is reported, congested nodes start to produce repulsive forces, due to which packets are diverted to some other best routes. This requires every node in the network to maintain a routing table that tells the best and next best paths to each destination in the network. Once the traffic comes to control, nodes that were congested in the past can return to use ACO algorithm. There are few challenges that are to be addressed when combining the algorithms. Pheromone is associated with the links. But links do not have memory associated with them to store these values. So, pheromone associated with a link was stored in a node which was the starting point to a link. Congestion model in ACO is not available. ICMP can be used to point out congestion. On detecting it, previous node tries for the emergence of some other path by decreasing the amount of pheromone on the link towards the congested node and increasing pheromone on the link towards the other neighbour nodes. This behaviour is drawn from the real ants which leaves out the congested path and traverses others leading to the evaporation of pheromone on congested paths. In the combined algorithm, initiation point of flock and the extent to which FlockCC will be employed had to be decided. So, the node previous to the congested node was made to initialise FlockCC when congestion was reported. FlockCC operates only in this location. All other nodes use ACO to route the packets. A. Algorithm for combining ACO and FlockCC Step 1: Route the packets using the ACO approach, selecting the paths with highest pheromone values Step 2: Whenever congestion in reported, just previous node to the congested one initiates flock CC by distributing the packets around congested area. Step 3: Nodes other than those in the area of congestion use ACO to route the packets. B. Problem formulation using the combination of ACO and FlockCC As said in the above algorithm for combination, ACO is employed first. After reading input values such as n, cost matrix, message size and congested node, routing is started using the pheromone table generated by ACO. During the course of routing, handling congestion by switching to FlockCC is done as follows: 190 Elsevier Publications 2013.
Optimization of ACO for Congested Networks by Adopting Mechanisms of Flock CC 1. while all packets p are not sent 2. for all nodes i in the network 3. send p to next node(i) 4. if next node(i) is congested 5. for all neighbours of i except next node(i) 6. send p in list of pkts[i] 7. end for 8. end if 9. end for 10. end while next node(i) is a function which returns node j, that has a high probability of receiving the data packets from node i according to the pheromone table. This is where ACO is implemented. On detecting congestion, node i sends the packets in its list to all of its neighbours except the congested node. This is the employment of FlockCC. FlockCC is initiated only in the node previous to congested one. If any node i, other than congested one receives the packet, FlockCC module is not at all entered and next node(i) function is called. So it is guaranteed that FlockCC is applied only in the proximity of congestion. Important formulae given by [2] and [3] which have been used in this regard with few transformations are: Updating pheromone τ on link (i, j) at time t + 1: τ ij (t + 1) = ρ τ ij (t) + τ ij (t, t + 1) where, ρ is a coefficient such that (1 ρ) represents the evaporation of trail and τ ij (t, t + 1) = 1/d is the quantity per unit of length of trail substance laid on edge (i, j) whose length is d. Transition probability p from node i to node j p ij (t) = [τ ij (t)] [τij If jε allowed (t)] 0 Otherwise Normalized node loading indicator p 1 if p in = p out = q = 0 P = otherwise p in +q p out p in +q where p in and p out are input and output packet rates respectively and q is the queue length of the node. Normalized channel loading factor r 0 if p out = 0 r = p out otherwise where p out is the total number of transmission attempts at a node. Desirability function D = α r + (1 α) (1 p) where α regulates the influence of parameters r and p. p out During the formulation, few considerations have been made to implement required conditions. From the source node, each packet(in ACO) or each flock (in Flock CC) are sent at intervals of T seconds, where T represents the approximate time taken by a packet or flock to move from a node to its neighbor node. It is calculated as the average of the transmission time taken by each link in the network. Congestion is thought of as the incapability of a node to hold the packets. So, a node is congested by initializing its queue size to 0. Each packet is stamped with a start time and end time is calculated at the destination depending on the path traversed by the packet. Transmission time is the time that has elapsed between the sending of the first packet at the source and reception of the last one at the destination. First node (with id 0) is the source and last node (with id n 1) is the destination. The nodes between which there is no link in the network are represented by a 0 entry in the network matrix. Elsevier Publications 2013. 191
M. S. Sneha, et al. 5. Experiments and Results ACO, FlockCC and combination were tested using a simulator built on our own using C language on fedora platform. Different network topologies were given as input and an attempt is made to note down the factors on which these algorithms depend. The following conventions were used in the experiments: A connected network is used, network topology is input in the form of a cost matrix, each packet is modeled to contain 20 kb, each node is given a queue capacity so that it can hold 20 such packets, evaporation factor ρ = 0.75, number of foraging ants sent to enforce pheromone trail π = 50. In FlockCC, parameter deciding the influence of r[k]and p[k] α = 0.5. To evaluate the performance of ACO, FlockCC and combined algorithm of these two, we have used the network topologies shown in figure 3. Topologies are selected so that they generate few of the cases that act as important factors in calculating transmission time, determining the aspect on which the performance of the algorithm depends, etc. Number on each link represents the bandwidth of that particular link. Giving each of these topologies as input to ACO, FlockCC and Combined algorithm, the time to transmit message of size 100 kb is noted down. Then, a node that comes in the shortest path determined by ACO is made as a congested node and time to transmit the same message of size 100 kb is recorded. These 3 values in each case are plot on a graph and compared Simulation Results. 5.1 Without congestion Here, only ACO and FlockCC are compared since the combined algorithm is ACO itself in the absence of congestion. Time measurements are plotted on a graph as shown in figure 4. We observe an average improvement of 16% in the results of FlockCC over ACO in congestion free network. We can make out that, when the number of nodes is less and bandwidth is uniform, FlockCC takes less time to transmit the packets than ACO. But as the number of nodes in the network increases, ACO outperforms FlockCC. The factors that contribute to this change are uneven distribution of bandwidth among the links, traversal of long paths by flock that cause high amount of increase in transmission time etc. Figure 3. Node topologies. Figure 4. Performance of ACO and FlockCC in congestion free network. 192 Elsevier Publications 2013.
Optimization of ACO for Congested Networks by Adopting Mechanisms of Flock CC Figure 5. Performance of ACO, FlockCC and combined algorithm in congested network. 5.2 With congestion As we can make out from the above graph, ACO results in optimal path. So, we introduce congestion in one of the nodes along the shortest path suggested by ACO. To compare the performance of the combined algorithm with that of the individual ones, we find out transmission time taken by ACO, FlockCC and their combination for the same message of size 100 kb and plot the values on a graph as shown in figure 5. Combined algorithm shows an average improvement of 34.07% in transmission time in comparison to ACO. It is clear that the performance of combined algorithm comes close to that of FlockCC or lies in between ACO and FlockCC. The reasons for this behavior are flockcc is especially designed for congestion control. It transmits a flock of packets together. Unavailability of one node because of congestion does not affect much. In the case of congestion, ACO needs to traverse next shorter path, which might be a bit longer, and send packets sequentially in that path. The bandwidth of that path plus the sequential nature leads to more time. Combined algorithm-borrowing the required features from both the algorithms-works well in the congested case. From the above results, it can be inferred that, FlockCC does not perform well in the case of congestion free large networks, but ACO does. ACO s performance is not that impressive in the case of congestion, but FlockCC is effective. The combination has the capacity to choose efficient algorithms depending on the situation and hence perform well in small/large, congestion free/congested networks. 6. Conclusion On the basis of the analysis we can conclude that when we consider the performance of ACO, FlockCC and their combination on congested and congestion free networks, the combined algorithm will give better performance when compared to performance of individual algorithms. When performance measurement is done for these routing algorithms, it was observed that Flock CC takes 16% less transmission time in congestion free networks but because of the disadvantages of Flock CC like the chances of packet loss keeps us away from using it. Proposed algorithm shows an average improvement of 34.07% in transmission time in comparison to ACO in congested networks. As a part of future work, networks with nodes that have no path to destination can be considered and the situation be handled if packet goes to such nodes. These algorithms can be implemented on wireless, adhoc networks. Any node in the network can be considered as the destination. Multiple nodes in the network can be congested. References [1] Natural Computing, an International Journal, Editors-in-Chief: G. Rosenberg and H. P. Spaink [2] Ant System: An Autocatalytic Optimizing Process, M. Dorigo, V. Maniezzo and A. Colorni [3] Mimicking the Bird Flocking Behavior for Controlling Congestion in Sensor Networks, Pavlos Antoniou and Andreas Pitsillides, Andries Engelbrecht, Tim Blackwell [4] Ant-DYMO: A Bio-Inspired Algorithm for MANETS, Jos e Alex Pontes Martins, Sergio Luis O. B. Correia, Joaquim Celestino J unior [5] Ant Colony System: A Cooperative Learning Approach to the Traveling Salesman Problem, Marco Dorigo, Luca Maria Gambardella Elsevier Publications 2013. 193
M. S. Sneha, et al. [6] A Bio-inspired Approach for a Dynamic Railway Problem, Petrica C. Pop, Camelia-M. Pintea, Corina Pop Sitar and D. Dumitrescu [7] Towards Bio-Inspired Self-Organisation in Sensor Networks: Applying the Ant Colony Algorithm, Jue Hong, Sanglu Lu, Daoxu Chen, Jiannong Cao [8] Observation-based Cooperation in Mobile Sensor Networks: A Bio-Inspired Approach for Fault Tolerant Coverage, Briana Lowe Wellman, Shameka Dawson, Aparna Veluchamy and Monica Anderson 194 Elsevier Publications 2013.