Data intensive distributed computing in data aware self-organizing networks
Transactions on Computational Science XV
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This book presents a comprehensive reference of state-of-the-art efforts and early results in the area of autonomic networking and communication. The essence of autonomic networking, and thus autonomic communications, is to enable the self-governing of services and resources within the constraints of business rules. In order to support self-governance, appropriate self-* functionality will be deployed in the network on an application-specific basis. The continuing increase in complexity of upcoming networking convergence scenarios mandates a new approach to network management. This special issue explores different ways that autonomic principles can be applied to existing and future networks. In particular, the book has 3 main parts, each of them represented by three papers discussing them from industrial and academic perspectives. The first part focuses on architectures and modeling strategies. It starts with a discussion on current standardization efforts for defining a technological neutral, architectural framework for autonomic systems and networks, followed by an insight report on how a telecommunication company utilizes autonomic principles to manage its infrastructure and finalized by a European effort to model distribution and behaviour of and for (autonomic) network management. Part two of this book is dedicated to middleware and service infrastructure as facilitators of autonomic communications. This part starts introducing a connectivity management system based on a resilient and adaptive communication middleware. The second paper of this part combines the concept of a knowledge plane with real-time demands of the military sector to regulate resources. This is followed by a profound discussion on how to the management of service access can benefit from autonomic principles, with special focus on next generation networks. Part three focuses on autonomic networks, specifically how current networks can be equipped with autonomic functionality and thus migrate to autonomic networks. We start this part by analyzing the difference between traditional network management and autonomic network management and learn how the later one enables cross-layer optimization. Next, we see how a multi-agent system helps to manage a combined MPLS DiffServ-TE Domain. This part is concluded by a very interesting approach that applies game theory to (autonomically) manage the available spectrums in wireless networks.