An edge-swap heuristic for generating spanning trees with minimum number of branch vertices

This paper presents a new edge-swap heuristic for generating spanning trees with a minimum number of branch vertices, i.e. vertices of degree greater than two. This problem was introduced in Gargano et al. (Lect Notes Comput Sci 2380:355-365, 2002) and has been called the minimum branch vertices problem by Cerulli et al. (Comput Optim Appl 42:353-370, 2009). The heuristic starts with a random spanning tree and iteratively reduces the number of branch vertices by swapping tree edges with edges not currently in the tree. It can be easily implemented as a multi-start heuristic. We report on extensive computational experiments comparing single-start and multi-start variants on our heuristic with other heuristics previously proposed in the literature

A superimposition algorithm for application-aware adaptation in mobile computing

In this paper we propose a superimposition algorithm than can be employed by a substrate to change its behavior according to current conditions or properties of the mobile environment. The algorithm has some interesting features such as it is completely distributed, works on-line, does not generate extra messages, and is based on unstable predicates. The algorithm proposed can be used by multiple local controllers that use information only from the subset of the participating mobile units. This is certainly an advantage over a centralized approach that is likely to be more expensive than distributed decisions since there are delays in the communication and the cost of sending extra messages. © 1999 IEEE

Efficient Clustering of Cabinets at FttCab

At this moment consumers want an internet connection with 20-50 Mb/s speed and around 100 Mb/s in the near future. Rolling out Fibre to the Curb networks quickly will be the only way for telecom operators in some countries to compete with cable tv operators. This requires a fibre connection to the cabinets. When the telecom operator wants toconnect the cabinets in a ring structure, he has to decide how to divide cabinets over a number of circuits, taking into account a maximum number of customers per circuit. This we call the cabinet clustering problem. In this paper we formulate this problem, present the heuristic approch we developed and show the results of our extensive testing that shows the method is accurate and fast. Finally we demonstrate the method on a real life case