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Development of green routers aimed at saving energy when the input traffic is low has the positive side effect of reducing the working temperature of their internal components. This idea can encourage the realization of smaller scale routers, but this process has to be supported by the confidence that temperature remains in a given range. For this reason, the target of this paper is to propose an analytical discrete-time Markov model that helps green router designers to calculate in advance the temperature statistics of a router, for given input traffic statistics and energy saving law applied by the router Governor, which is an entity which decides the router policy to be adopted. At the same time the model evaluates the obtained energy saving gain. The proposed model is applied to a case study to show how it can be used to support the design of the router Governor parameters to achieve the target of maintaining the mean temperature below a given threshold.

Any application that represents data as sets of graphs may benefit from the discovery of relationships among those graphs. To do this in an unsupervised fashion requires the ability to find graphs that are similar to one another. That is the purpose of GraphClust. The GraphClust algorithm proceeds in three phases, often building on other tools:(1) it finds highly connected substructures in each graph;(2) it uses those substructures to represent each graph as a feature vector; and (3) it clusters these feature vectors using a standard distance measure. We validate the cluster quality by using the Silhouette method. In addition to clustering graphs, GraphClust uses SVD decomposition to find frequently co-occurring connected substructures. The main novelty of GraphClust compared to previous methods is that it is application-independent and scalable to many large graphs.

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