Catálogo de publicaciones - libros

In collaborative self-organizing computing systems a complex task is performed by relatively simple autonomous agents that act without centralized control. Disruption of a task can be caused by agents that produce harmful outputs due to internal failures or due to maliciously introduced alterations of their functions. The probability of such harmful outputs is minimized by the application of a design principle called “the immune system paradigm” that provides individual agents with an all-hardware fault tolerance infrastructure. The paradigm and its application are described in this paper.

Greater understanding of biology in modern times has enabled significant breakthroughs in improving healthcare, quality of life, and eliminating many diseases and congenital illnesses. Simultaneously there is a move towards emulating nature and copying many of the wonders uncovered in biology, resulting in “biologically inspired” systems. Significant results have been reported in a wide range of areas, with systems inspired by nature enabling exploration, communication, and advances that were never dreamed possible just a few years ago. We warn, that as in many other fields of endeavor, we should be by nature and biology, not engage in mimicry. We describe some results of biological inspiration that augur promise in terms of improving the safety and security of systems, and in developing self-managing systems, that we hope will ultimately lead to self-governing systems.

Large, complex computing systems have many similarities to biological systems, at least at a high level. They consist of a very large number of components, the interactions between which are complex and dynamic, and the overall behavior of the system is not always predictable even if the components are well understood. These similarities have led the computing community to look to biology for design inspiration. But computing systems are not biological systems. Care must be taken when applying biological designs to computing systems, and we need to avoid applying them when they are not appropriate. We review three areas in which we have used biology as an inspiration to understand and construct computing systems. The first is the epidemiology of computer viruses, in which biological models are used to predict the speed and scope of global virus spread. The second is global defenses against computer viruses, in which the mammalian immune system is the starting point for design. The third is self-assembling autonomic systems, in which the components of a system connect locally, without global control, to provide a desired global function. In each area, we look at an approach that seems very biologically motivated, but that turns out to yield poor results. Then, we look at an approach that works well, and contrast it with the prior misstep. Perhaps unsurprisingly, attempting to reason by analogy is fraught with dangers. Rather, it is critical to have a detailed, rigorous understanding of the system being constructed and the technologies being used, and to understand the differences between the biological system and the computing system, as well as their similarities.

We present in this paper a clustering algorithm which is based on a cellular automaton and which aims at displaying a map of web pages. We describe the main principles of methods that build such maps, and the main principles of cellular automata. We show how these principles can be applied to the problem of web pages clustering: the cells, which are organized in a 2D grid, can be either empty or may contain a page. The local transition function of cells favors the creation of groups of similar states (web pages) in neighbouring cells. We then present the visual results obtained with our method on standard data as well as on sets of documents. These documents are thus organized into a visual map which eases the browsing of these pages.

We present a learning-based method for model completion and adaptation, which is based on the combination of two approaches: 1) R2D2C, a technique for mechanically transforming system requirements via provably equivalent models to running code, and 2) automata learning-based model extrapolation. The intended impact of this new combination is to make model completion and adaptation accessible to experts of the field, like biologists or engineers. The principle is briefly illustrated by generating models of biological procedures concerning gene activities in the production of proteins, although the main application is going to concern autonomic systems for space exploration.

From the biology’s point of view, pollination is an important step in the reproduction of seed plants. Prom our point of view, pollination is a promising and novel, biological paradigm for future dependable and self-managing computing systems. This estimation is based on the characteristics the pollination process between plants and insects implies inherently.

To utilize pollination as a paradigm for self-managing and thus autonomic computing systems, this paper identifies the useful properties that emerge by the collaborative behavior of insects and plants during the pollination process. Based on this process the paper presents an artificial pollination system that implements these properties by adapting the natural architecture and behavior. Furthermore, the paper illustrates the practical value of this system by an application in aviation. Finally open issues and an outlook on future work are presented.

We propose an architecture which supports the behavioral self-optimization of complex systems. In this architecture we bring together specification-based reasoning and the framework of ant colony optimization (ACO). By this we provide a foundation for distributed reasoning about different properties of the solution space represented by different viewpoint specifications. As a side-effect of reasoning we propagate the information about promising areas in the solution space to the current state. Consequently the system’s decisions can be improved by considering the long term values of certain behavioral trajectories (given a certain situational horizon). We consider this feature to be a contribution to .

This paper presents a basic and an extended heuristic to distribute operating system (0s) 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.

The rising complexity of distributed computer systems give reason to investigate self-organization mechanism to build systems that are self-managing in the sense of and . In this paper we propose the Artificial Hormone System (AHS) as a general approach to build self-organizing systems based on networked nodes. The Artificial Hormone System implements a similar information exchange between networked nodes like the human hormone system does between cells. The artificial hormone values are piggy-backed on messages to minimize communication overhead.

To show the efficiency of the mechanism even for large scale systems we implemented a simulation environment, in Java to evaluate different optimization strategies. The evaluations show that local information is enough to meet global optimization criterion.

In this article is presented a detection model of intruders by using an architecture based in agents that imitates the principal aspects of the Immunological System, such as detection and elimination of antigens in the human body. This model is based on the hypothesis of an intruder which is a strange element in the system, whereby can exist mechanisms able to detect their presence. We will use recognizer agents of intruders () for such goal and macrophage agents () for alerting and reacting actions.

The core of the system is based in by agents (), which will recognize anomalies in the behavior of the user, through a catalogue of Metrics that will allow us quantify the conduct of the user according to measures of behaviors and then we will apply and technics to classify the conducts of the user in intruder or normal behavior. Our experiments suggest that both methods are complementary for this purpose. This approach was very flexible and customized in the practice for the needs of any particular system.