The intuitive background for measures of structural centrality in social networks is reviewed aPzd existing measures are evaluated in terms of their consistency with intuitions and their interpretability. Three distinct intuitive conceptions of centrality are uncovered and existing measures are refined to embody these conceptions. Three measures are developed for each concept, one absolute and one relative measure of the ~entra~~t~ ~ of ~os~tio~ls in a network, and one relenting the degree of centralization of the entire network. The implications of these measures for the experimental study of small groups is examined. The problem of centrality The idea of centrality as applied to human communication was introduced by Bavelas in 1948. He was specifically concerned with communication in small groups and he hypothesized a relationship between structural centrality and influence in group processes.

This paper introduces a framework for evaluating replica placement algorithms (RPA) for content delivery networks (CDN) as well as RPAs from other fields that might be applicable to current or future CDNs. First, the framework classifies and qualitatively compares RPAs using a generic set of primitives that capture problem definitions and heuristics. Second, it provides estimates for the decision times of RPAs using an analytic model. To achieve accuracy, the model takes into account disk accesses and message sizes, in addition to computational complexity and message numbers that have been considered traditionally. Third, it uses the "goodness" of produced placements to compare RPAs even when they have different problem definitions. Based on these evaluations, we identify open issues and potential areas for future research.

Numerous replica placement algorithms have been proposed in the literature for use in content delivery networks. However, little has been done to compare the various placement algorithms against each other and against caching. This paper debates whether we need replica placement algorithms in content delivery networks or not.

Plants, distribution centers, and other facilities generally function for years or decades, during which time the environment in which they operate may change substantially. Costs, demands, travel times, and other inputs to classical facility location models may be highly uncertain. This has made the development of models for facility location under uncertainty a high priority for researchers in both the logistics and stochastic/robust optimization communities. Indeed, a large number of the approaches that have been proposed for optimization under uncertainty have been applied to facility location problems. This paper reviews the literature...

The design of the distribution system is a strategic issue for almost every company. The problem of locating facilities and allocating customers covers the core topics of distribution system design. Model formulations and solution algorithms which address the issue vary widely in terms of fundamental assumptions, mathematical complexity and computational performance. This paper reviews some of the contributions to the current state-of-the-art. In particular, continuous location models, network location models, mixed-integer programming models, and applications are summarized.

: The p-median problem is the problem of locating p facilities (medians) on a network so as to minimize the sum of all the distances from each demand point to its nearest facility. A successful approach to approximately solve this problem is the use of Lagrangean heuristics, based upon Lagrangean relaxation and subgradient optimization. The Lagrangean/surrogate is an alternative relaxation proposed recently to correct the erratic behavior of subgradient like methods employed to solve the Lagrangean dual. We propose in this paper Lagrangean/surrogate heuristics to p-median problems. Lagrangean and surrogate relaxations are combined relaxing in the surrogate way the assignment constraints in the p-median formulation. Then, the Lagrangean relaxation of the surrogate constraint is obtained and approximately optimized (one-dimensional dual). Lagrangean/surrogate relaxations are very stable (low oscillating) and reach the same good results of Lagrangean (alone) heuristics in less computation...

When dealing with the design of service networks, such as health and EMS services, banking or distributed ticket selling services, the location of service centers has a strong influence on the congestion at each of them, and consequently, on the quality of service. In this paper, several models are presented to consider service congestion. The first model addresses the issue of the location of the least number of single-server centers so that all the population is served within a standard distance, and nobody stands on line for a time longer than a given time-limit, or with more than a predetermined number of other clients. We then formulate several maximal coverage models, with one or more servers per service center. A new heuristic is developed to solve the models and tested in a 30-nodes network. 1 This research has been possible thanks to grants by NORTEL- External Research, FONDECYT

This paper envisions a distributed network storage service with Quality-ofService (QoS) guarantees, and describes its architecture and key mechanisms. When fully realized, this service architecture would be able to support, in one integrated framework, network storage services ranging from best-effort caching to replication with performance guarantees. Content owners could, through the use of standardized protocols, reserve network storage resources to satisfy their application-specific performance requirements. They would be able to specify either the number and/or placement of the replicas, or higher-level performance goals based on access latency, bandwidth usage or data availability. The network storage provider would then optimally allocate storage resources to meet the service commitments, using leftover capacity for best-effort caching. Content consumers would then retrieve the nearest copy of the data object, be it from a replica, cache, or the original source, in a completely ...

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Given a (combinatorial) optimization problem and a feasible solution to it, the corresponding inverse optimization problem is to find a minimal adjustment of the cost function such that the given solution becomes optimum. Several such problems have been studied in the last ten years. After formalizing the notion of an inverse problem and its variants, we present various methods for solving them. Then we discuss the problems considered in the literature and the results that have been obtained. Finally, we formulate some open problems.