Ant based heuristic for OS service distribution on ad hoc networks

This paper presents a basic and an extended heuristic to distribute operating system (OS) services over mobile ad hoc networks. The heuristics are inspired by the foraging behavior of ants and are used within our NanoOS, an OS for distributed applications. The NanoOS offers an uniform environment of execution and the code of the OS is distributed among nodes. We propose a basic and an extended swarm optimization based heuristic to control the service migration in order to reduce the communication overhead. In the basic one, each service request leaves pheromone in the nodes on its path to the service provider (like ants leave pheromone when foraging). An optimization step occurs when the service provider migrates to the neighbor node with the higher pheromone concentration. The proposed extension takes into account the position of the node in the network and its energy. Realized simulations have shown that the basic heuristic performs well. The total communication cost in average is just 40% higher than the global optimum. In addition, both heuristics have a low computational requirement.1st IFIP International Conference on Biologically Inspired Cooperative Computing - Biological Inspiration 2Red de Universidades con Carreras en Informática (RedUNCI

Ant based heuristic for OS service distribution on ad hoc networks

This paper presents a basic and an extended heuristic to distribute operating system (OS) services over mobile ad hoc networks. The heuristics are inspired by the foraging behavior of ants and are used within our NanoOS, an OS for distributed applications. The NanoOS offers an uniform environment of execution and the code of the OS is distributed among nodes. We propose a basic and an extended swarm optimization based heuristic to control the service migration in order to reduce the communication overhead. In the basic one, each service request leaves pheromone in the nodes on its path to the service provider (like ants leave pheromone when foraging). An optimization step occurs when the service provider migrates to the neighbor node with the higher pheromone concentration. The proposed extension takes into account the position of the node in the network and its energy. Realized simulations have shown that the basic heuristic performs well. The total communication cost in average is just 40% higher than the global optimum. In addition, both heuristics have a low computational requirement.1st IFIP International Conference on Biologically Inspired Cooperative Computing - Biological Inspiration 2Red de Universidades con Carreras en Informática (RedUNCI

An Efficient Algorithm for the Physical Mapping of Clustered Task Graphs onto Multiprocessor Architectures

The most important issue in sequential program parallelisation is the efficient assignment of computations into different processing elements. In the past, too many approaches were devoted in efficient program parallelization considering various models for the parallel programs and the target architectures. The most widely used parallelism description model is the task graph model with precedence constraints. Nevertheless, as far as physical mapping of tasks onto parallel architectures is concerned, little research has given practical results. It is well known that the physical mapping problem is NP-hard in the strong sense, thus allowing only for heuristic approaches. Most researchers or tool programmers use exhaustive algorithms, or the classical method of simulated annealing. This paper presents an alternative approach onto the mapping problem. Given the graph of clustered tasks, and the graph of the target distributed architecture, our heuristic finds a mapping by first placi..