He has been appointed as an expert by the Flemish Government of Belgium (1998) and the National Science Foundation of the USA (2001, 2004). He is in the steering committee or TPC of several IEEE-IFIP conferences. He has chaired the IEEE High Performance Switching and Routing conference (2009) and the IEEE Globecom Symposium on Advanced Technologies and Protocols for Transparent Optical Networks (2006). He has been co-guest editor of several special issues of the Computer Networks journal (Elsevier, 2000) Proceedings of the IEEE titled (IEEE Press, September 2004) and Annals of Telecoms (Springer, 2010). He has also authored several books in French. He is the author or co-author of several books in English on broadband access systems (Artech House, 2003), (Kluwer, 2000), IP over WDM (Addison–Wesley, 2002) and on optical traffic grooming (Springer, 2007). His research activities are carried out in the context of European projects (BONE network of excellence, DICONET project), national research projects. His field of expertise covers optical core networks design and traffic engineering, hybrid optical-wireless access systems, resource virtualization and pricing strategies in Cloud environment. Maurice Gagnaire is Professor at the Computer Science and Networks Department of Telecom ParisTech, Paris, France where he leads the Network, Mobility and Services research group. Moreover, these algorithms represent a scalable and sufficiently accurate way of advertising the resources in a multi-tenant environment. Using our proposed algorithms, the obtained numerical results show that resource abstraction in general and network topology abstraction in particular can effectively hide details of the underlying infrastructure. We compare the MILP formulation, the SILK-ALT algorithm, and the SILK algorithm in terms of rejection ratio of users’ requests at both the Cloud provider and the network provider levels. Second, we propose an innovative scalable algorithm called SILK-ALT inspired from the SImple LinK (SILK) algorithm previously proposed by Abosi et al. Solving this formulation provides an optimal abstracted topology to the CSP in terms of availability of the underlying resources. First, we formulate the network topology abstraction problem as a Mixed-Integer Linear Program (MILP). In this context, we propose two network resource abstraction techniques. In this paper, we focus on network resource abstraction algorithms used by a Network Service Provider (NSP) for sharing its network topology without exposing details of its physical resources.
To address these challenges, resource (network, computing, and storage) abstraction is introduced. This multi-tenant environment raises multiple challenges such as confidentiality and scalability issues. For this reason, Cloud Service Providers (CSPs) rely on the availability of computing, storage, and network resources generally provided by various administrative entities.
In this context, the ubiquity and the variety of Cloud services impose a form of collaboration between all these actors. The number of actors providing Infrastructure as a Service (IaaS) remains limited, while the number of PaaS (Platform as a Service) and SaaS (Software as a Service) providers is rapidly increasing.
The rapid development and diversification of Cloud services occurs in a very competitive environment.
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