Back-pressure-based adaptive routing algorithms where each packet is routed along a possibly different path have been extensively studied in the literature. However, such algorithms typically result in poor delay performance and involve high implementation complexity. In this paper, we develop a new adaptive routing algorithm built upon the widely studied back-pressure algorithm. We decouple the routing and scheduling components of the algorithm by designing a probabilistic routing table that is used to route packets to per-destination queues. The scheduling decisions in the case of wireless networks are made using counters called shadow queues. The results are also extended to the case of networks that employ simple forms of network coding. In that case, our algorithm provides a low-complexity solution to optimally exploit the routing–coding tradeoff.

It has been conjectured that MWS-α scheduling policies with α going to zero are heavy-traffic optimal for scheduling in a generalized switch when the objective is to minimize the number of backlogged packets in the system. We examine this conjecture by first deriving optimal or heavy-traffic optimal policies for small switches and then comparing them to MWS- α policies by simulation. Our conclusion is that the conjecture is not true in general, i.e., there are simple topologies for which there exist policies that outperform MWS-α in heavy traffic.

We consider small generalized switches with less than or equal to four links, and study scheduling policies designed to minimize the total number of packets in the system. By focusing on very small switches, we are able to derive optimal or heavy-traffic optimal policies whose performance can then be compared to previously conjectured optimal policies. In particular, it has been conjectured that the max-weight policy with weight qalphais optimal in heavy-traffic when alpha rarr 0. Our results show that this conjecture is not true.

In this paper, we consider distributed systems that can be modeled as finite state machines with known behavior under fault-free conditions, and we study the detection of a general class of faults that manifest themselves as permanent changes in the next-state transition functionality of the system. This scenario could arise in a variety of situations encountered in communication networks, including faults occurred due to design or implementation errors during the execution of communication protocols. In our approach, fault diagnosis is performed by an external observer/diagnoser that functions as a finite state machine and which has access to the input sequence applied to the system but has only limited access to the system state or output. In particular, we assume that the observer/diagnoser is only able to obtain partial information regarding the state of the given system at intermittent time intervals that are determined by certain synchronizing conditions between the system and the observer/diagnoser. By adopting a probabilistic framework, we analyze ways to optimally choose these synchronizing conditions and develop adaptive strategies that achieve a low probability of aliasing, i.e., a low probability that the external observer/diagnoser incorrectly declares the system as fault-free. An application of these ideas in the context of protocol testing/classification is provided as an example.