Validating Arbitrarily Large Network Protocol Compositions with Finite Computation

Formal tools like finite-state model checkers have proven useful in verifying the correctness of systems of bounded size and for hardening single system components against arbitrary inputs. However, conventional applications of these techniques are not well suited to characterizing emergent behaviors of large compositions of processes. In this paper, we present a methodology by which arbitrarily large compositions of components can, if sufficient conditions are proven concerning properties of small compositions, be modeled and completely verified by performing formal verifications upon only a finite set of compositions. The sufficient conditions take the form of reductions, which are claims that particular sequences of components will be causally indistinguishable from other shorter sequences of components. We show how this methodology can be applied to a variety of network protocol applications, including two features of the HTTP protocol, a simple active networking applet, and a proposed web cache consistency algorithm. We also doing discuss its applicability to framing protocol design goals and to representing systems which employ non-model-checking verification methodologies. Finally, we briefly discuss how we hope to broaden this methodology to more general topological compositions of network applications.

Modal Matching for Correspondence and Recognition

Modal matching is a new method for establishing correspondences and computing canonical descriptions. The method is based on the idea of describing objects in terms of generalized symmetries, as defined by each object's eigenmodes. The resulting modal description is used for object recognition and categorization, where shape similarities are expressed as the amounts of modal deformation energy needed to align the two objects. In general, modes provide a global-to-local ordering of shape deformation and thus allow for selecting which types of deformations are used in object alignment and comparison. In contrast to previous techniques, which required correspondence to be computed with an initial or prototype shape, modal matching utilizes a new type of finite element formulation that allows for an object's eigenmodes to be computed directly from available image information. This improved formulation provides greater generality and accuracy, and is applicable to data of any dimensionality. Correspondence results with 2-D contour and point feature data are shown, and recognition experiments with 2-D images of hand tools and airplanes are described.