In emerging network technologies designed to support a variety of services, it is common to find that the packet switching service is implemented on top of a facility network. For example, in typical narrowband ISDN architectures, the packet switches installed a t some of the central offices (CO's) a r e interconnected with trunks derived from an underlying channelized facility network. Likewise, in future broadband ISDN's, the ATM switches will he connected with trunks obtained from a n underlying "pool" of fiber facilities interconnected by digital cross connect systems (DCS). In this paper, we address the design of a P/S network embedded into a backbone facility network. We formulate the problem as a network optimization problem where a congestion measure based on the average packet delay is minimized, subject to capacity constraints posed by the underlying facility trunks. The variables in this problem a r e the routing on the "express" pipes (i.e., the channels that interconnect the PIS modes), and the allocation of bandwidth to such pipes. We present a n efficient algorithm for the solution of the above problem and apply it to some representative examples. We show that for some test cases, the congestion measure is substantially reduced with respect to the values obtained when the embedded topology is kept identical to the hackbone topology. We also discuss dynamic reconfiguration schemes where the embedded topology is periodically adjusted to track the fluctuations in traffic requirements.

No abstract

The K-means clustering algorithm is widely used in several domains, because of its simplicity of implementation and interpretation. However, one of its limitations is its high computational complexity. In this work the problem of reducing the complexity of the K means algorithm is approached, in order to make possible the solution of large scale data sets like those from Big Data, without significantly degrading solution quality. To this end, a new metaheuristics is proposed, which by an early assignment of objects to clusters, significantly reduces the number of calculations of distances from objects to centroids. The approach was experimentally evaluated by solving real and synthetic datasets yielding encouraging results. Time reductions of up to 91% were obtained with respect to the standard K-means, at the expense of reducing quality by 3.2%.

No abstract

No abstract