Saqib, Muhammad; Elbiaze, Halima et Glitho, Roch
(2024).
« Adaptive In-Network Traffic Classifier: Bridging the Gap for Improved QoS by Minimizing Misclassification ».
IEEE Open Journal of the Communications Society, 5, pp. 677-689.
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Résumé
Network Function Virtualization (NFV) empowers Internet Service Providers (ISPs) to place Virtual Network Functions (VNFs) efficiently in order to enhance the network performance without incurring a high cost. In this environment, Service Function Chains (SFCs) always need to steer the traffic through a sequence of VNF instances. Therefore, ISPs must adopt a suitable SFC embedding strategy to bolster their revenue. However, existing VNF placement and chaining methodologies harbour unrealistic assumptions, as they tackle the mapping problem from a generic standpoint, overlooking the distinctive characteristics of the constituent VNFs within a chain. Hence, they are not efficient when the strict requirements of life-critical applications, such as telesurgery, need to be satisfied. In this paper, taking into account the strict requirements of telesurgery-like systems, we present a cost-efficient characteristic-aware SFC mapping method for telesurgery Systems in the Cloud-Edge continuum. We formulate this problem as a Binary Linear Programming (BLP) model to embed SFC requests at minimal cost. Also, we propose an innovative heuristic algorithm that allocates each VNF based on its distinctive characteristics. Simulation results demonstrate that taking the characteristics of the VNFs into account when addressing the placement problem improves the system performance notably.
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