No abstract

This paper presents an unified overview of a new family of distributed algortithms for routing and load balancing in dynamic communication networks. These new algorithms are described as an extension to the classical routing algorithms: they combine the ideas of online asynchronous distance vector routing with adaptive link state routing. Estimates of the current traffic condition and link costs are measured by sending routing agents in the network that mix with the regular information packets and keep track of the costs (e.g. delay) encountered during their journey. The routing tables are then regularly updated based on that information without any central control nor complete knowledge of the network topology. Two new algorithms are proposed here. The first one is based on round trip routing agents that update the routing tables by backtracking their way after having reached the destination. The second one relies on forward agents that update the routing tables directly as they move toward their destination. An efficient co-operative scheme is proposed to deal with asymmetric connections. All these methods are compared on a simulated network with various traffic loads; the robustness of the new algorithms to network changes is proved on various dynamic scenarii.

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Model-Driven Engineering (MDE) proposes to modularize complex software-intensive systems using multiple models where each module serves a specific concern. These concerns of a system might be diverse and the use of multiple heterogeneous models often becomes inevitable. These models adhere to different paradigms and use distinct formalisms, which makes it hard to ensure consistency among them. Moreover, these models might contain certain concepts (at times overlapping) that are reused for building cross-concern views/models. Maintaining models using separation of concerns in a heterogeneous modeling space becomes difficult. Traditional MDE suggests the use of model transformations to maintain the mappings between heterogeneous models. In this paper, we introduce a different approach based on model federation to map heterogeneous models. In contrast to traditional approaches where heterogeneous models are gathered in a single technological space, model federation keeps them in their own technological spaces. We provide a mechanism so that elements of these models are accessible for the development of cross-concern views/models from their respective technological spaces.