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.

An Information Theoretic Framework for Field Monitoring Using Autonomously Mobile Sensors

We consider a mobile sensor network monitoring a spatio-temporal field. Given limited cache sizes at the sensor nodes, the goal is to develop a distributed cache management algorithm to efficiently answer queries with a known probability distribution over the spatial dimension. First, we propose a novel distributed information theoretic approach in which the nodes locally update their caches based on full knowledge of the space-time distribution of the monitored phenomenon. At each time instant, local decisions are made at the mobile nodes concerning which samples to keep and whether or not a new sample should be acquired at the current location. These decisions account for minimizing an entropic utility function that captures the average amount of uncertainty in queries given the probability distribution of query locations. Second, we propose a different correlation-based technique, which only requires knowledge of the second-order statistics, thus relaxing the stringent constraint of having a priori knowledge of the query distribution, while significantly reducing the computational overhead. It is shown that the proposed approaches considerably improve the average field estimation error by maintaining efficient cache content. It is further shown that the correlation-based technique is robust to model mismatch in case of imperfect knowledge of the underlying generative correlation structure.