Experimental Evaluation of Dynamic Resource Orchestration in Multi-Layer (Packet over Flexi-Grid Optical) Networks?

This paper has been presented at : ONDM 2019 23rd Conference on Optical Network Design and ModellingIn future 5G infrastructures, network services will be de- ployed through sets of Virtual Network Functions (VNFs) leveraging the advantages of both Software Defined Networking (SDN) and Net- work Function Virtualization (NFV). A network service is composed of an ordered sequence of VNFs, i.e., VNF Forwarding Graph (VNFFG), deployed across distributed data centers (DCs). Herein, we present a Cloud/Network Orchestrator which dynamically processes and accom- modates VNFFG requests over a pool of DCs interconnected by a multi- layer (packet/flexi-grid optical) transport network infrastructure. We propose two different cloud and network resource allocation algorithms aiming at: i) minimizing the distance between the selected DCs, and ii) minimizing the load (i.e., consumed cloud resources) of the chosen DCs. Both algorithms run on a Cloud/Network Orchestrator and are ex- perimentally validated and benchmarked on the CTTC ADRENALINE testbed.This work is partially funded by the EU H2020 5G TRANSFORMER project (761536) and the Spanish AURORAS project (RTI2018-099178

Experimental evaluation of dynamic resource orchestration in multi-layer (packet over flexi-grid optical) networks

Network services in 5G will be rolled out as pools of virtual network functions (VNFs) exploiting the advantages of both software-defined networking and network function virtualization. In this context, 5G network services are envisaged as ordered sequences of VNFs resulting in the so-called VNF Forwarding Graphs (VNFFGs). Such VNFs can be allocated over a number of distributed but interconnected data centers (DCs). In this work, a cloud/network orchestrator is discussed to dynamically process and accommodate VNFFG requests over a pool of DCs interconnected by a multi-layer (packet/flexi-grid optical) transport network infrastructure. Two different cloud and network resource allocation algorithms are proposed aiming at: (1) minimizing the distance between the selected DCs and (2) minimizing the load (i.e., consumed cloud resources) of the chosen DCs. In the performance evaluation, the proposed algorithms are experimentally validated and compared on the CTTC ADRENALINE testbed