Ant colony optimization (ACO) has proved its success as a meta-heuristic optimization in several network applications such as routing and load balancing. In this paper, a proposed ACO algorithm for load balancing in distributed systems will be presented. This algorithm is fully distributed in which information is dynamically updated at each ant movement. Multiple colonies paradigm will be adopted such that each node will send a colored colony throughout the network. Using colored ant colony helps in preventing ants of the same nest from following the same route, and hence enforcing them to be distributed all over the nodes in the network. Each ant acts like a mobile agent that carries newly updated load balancing information to the next visited node. Finally, the proposed algorithm will be compared with the standard work-stealing algorithm. ARTIFICIAL INTELLIGENCE NETWORK LOAD BALANCING USING ANT COLONY OPTIMIZATION
Ants first evolved around 120 million years ago, take form in over 11,400 different species and are considered one of the most successful insects due to their highly organised colonies, sometimes consisting of millions of ants. One particular notability of ants is their ability to create “ant streets”. Long, bi-directional lanes of single file pathways in which they navigate landscapes in order to reach a destination in optimal time. These ever-changing networks are made possible by the use of pheromones which guide them using a shortest path mechanism. This technique allows an adaptive routing system which is updated should a more optimal path be found or an obstruction placed across an existing pathway.
Computer scientists began researching the behaviour of ants in the early 1990’s to discover new routing algorithms. The result of these studies is Ant Colony Optimisation (ACO) and in the case of well implemented ACO techniques, optimal performance is comparative to existing top-performing routing algorithms.
This article details how ACO can be used to dynamically route traffic efficiently. An efficient routing algorithm will minimise the number of nodes that a call will need to connect to in order to be completed thus; minimising network load and increasing reliability. An implementation of ANTNet based on Marco Dorigo & Thomas stützle has been designed and through this a number of visually aided test were produced to compare the genetic algorithm to a non-generic algorithm. The report will final conclude with a summary of how the algorithm perform and how it could be further optimised.
Existing system that consists of a set of heterogeneous host computers connected in an arbitrary fashion by a communications network is considered. A general model is developed for such a distributed computer system, in which the host computers and the communications network are represented by product-form queuing networks. In this model, a job may be either processed at the host to which it arrives or transferred to another host. In the latter case, a transferred job incurs a communication delay in addition to the queuing delay at the host on which the job is processed. It is assumed that the decision of transferring a job does not depend on the system state, and hence is static in nature. Performance is optimized by determining the load on each host that minimizes the mean job response time. A nonlinear optimization problem is formulated, and the property of the optimal solution in the special case where the communication delay does not depend on the source-destination pair is shown.
We proposed ACO algorithm for load balancing in distributed systems will be presented. This algorithm is fully distributed in which information is dynamically updated at each ant movement. Multiple colonies paradigm will be adopted such that each node will send a colored colony throughout the network.
Ant Colony Optimization (ACO) is a general-purpose heuristic algorithm, which can be used to solve different combinatorial optimization problems. In ACO, the search activities are distributed over artificial ants, which mimic the behavior of real ants.
The advantages of that system are positive feed-back, distributed computation, and the use of a constructive greedy heuristic. Positive feed-back refers to the ability to rapid discovery of good solutions.
The proposed strategy was simulated and tested, the number of nodes in the distributed system was assumed to be 40, an ant is assumed to travel from one node to another in 1 time step, each task is assumed to take 40 steps. In order to emphasis the efficiency of the proposed algorithm, we consider the case when the distributed system is clear that the efficiency.
Ant –colony approach comes from the ability to distribute loading information through all the nodes, the tour of ants was randomly chosen, however the cleverness of the ants to carry the new loading-status to each nodes increases the chance of each node to quickly find a good food source or a busy node.