Current data networks are highly homogeneous because of management, economic, and interoperability reasons. This technological homogeneity introduces shared risks, where correlated failures may entirely disrupt the network operation and impair multiple nodes. In this paper, we tackle the problem of improving the resilience of homogeneous networks, which are affected by correlated node failures, through optimal multiculture network design. Correlated failures regarded here are modeled by SRNG events. We propose three sequential optimization problems for maximizing the network resilience by selecting as different node technologies, which do not share risks, and placing such nodes in a given topology. Results show that in the 75% of real-world network topologies analyzed here, our optimal multiculture design yields networks whose probability that a pair of nodes, chosen at random, are connected is 1, i.e., its ATTR metric is 1. To do so, our method efficiently trades off the network heterogeneity, the number of nodes per technology, and their clustered location in the network. In the remaining 25% of the topologies, whose average node degree was less than 2, such probability was at least 0.7867. This means that both multiculture design and topology connectivity are necessary to achieve network resilience.

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In this paper, we present an algorithm for minimizing the processing time of a video sequence on a handheldbased distributed computing system. The algorithm considers the energy use and the remaining energy of the nodes, and steers a Monte-Carlo-based allocation according to the following rationale: images should be mapped more likely onto those nodes that communicate and process faster, consume less energy, and possess more energy reservoirs. Simulation results show that these simple ideas allow the allocation algorithm to deal efficiently with the fundamental tradeoff between turnaround time and energy.