Towards an optimized abstracted topology design in cloud environment

The rapid development and diversification of Cloud services occurs in a very competitive environment. 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. In this context, the ubiquity and the variety of Cloud services impose a form of collaboration between all these actors. For this reason, Cloud Service Providers (CSPs) rely on the availability of computing, storage, and network resources generally provided by various administrative entities. This multi-tenant environment raises multiple challenges such as confidentiality and scalability issues. To address these challenges, resource (network, computing, and storage) abstraction is introduced. 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. In this context, we propose two network resource abstraction techniques. First, we formulate the network topology abstraction problem as a Mixed-Integer Linear Program (MILP). Solving this formulation provides an optimal abstracted topology to the CSP in terms of availability of the underlying resources. Second, we propose an innovative scalable algorithm called SILK-ALT inspired from the Simple LinK (SILK) algorithm previously proposed by Abosi et al. 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. 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. Moreover, these algorithms represent a scalable and sufficiently accurate way of advertising the resources in a multi-tenant environment. (C) 2012 Elsevier B.V. All rights reserved.