iOn-Profiler: intelligent Online multi-objective VNF Profiling with Reinforcement Learning

Leveraging the potential of Virtualised Network Functions (VNFs) requires a clear understanding of the link between resource consumption and performance. The current state of the art tries to do that by utilising Machine Learning (ML) and specifically Supervised Learning (SL) models for given network environments and VNF types assuming single-objective optimisation targets. Taking a different approach poses a novel VNF profiler optimising multi-resource type allocation and performance objectives using adapted Reinforcement Learning (RL). Our approach can meet Key Performance Indicator (KPI) targets while minimising multi-resource type consumption and optimising the VNF output rate compared to existing single-objective solutions. Our experimental evaluation with three real-world VNF types over a total of 39 study scenarios (13 per VNF), for three resource types (virtual CPU, memory, and network link capacity), verifies the accuracy of resource allocation predictions and corresponding successful profiling decisions via a benchmark comparison between our RL model and SL models. We also conduct a complementary exhaustive search-space study revealing that different resources impact performance in varying ways per VNF type, implying the necessity of multi-objective optimisation, individualised examination per VNF type, and adaptable online profile learning, such as with the autonomous online learning approach of iOn-Profiler.Comment: 22 pages, 12 figures, 8 tables, journal article pre-print versio

End-to-end Quantum Secured Inter-Domain 5G Service Orchestration Over Dynamically Switched Flex-Grid Optical Networks Enabled by a q-ROADM

Dynamic and flexible optical networking enabled by NFV and SDN are the key technology enablers for supporting the dynamicity and bandwidth requirements of emerging 5G network services. To achieve the objective of 5G, Network Services (NSes) must be often deployed transparently over multiple administrative and technological domains. Such case often presents security risks since a typical NS may comprise a chain of network functions, each executed in different remote locations, and tampering within the network infrastructure may compromise their communication. To avoid such threats, QKD has been identified and proposed as a future-proof method immune to any algorithmic cryptanalysis based on quantum-physics mechanisms. The maturity of QKD has enabled the R&D of quantum networks coexisting with optical networks using telecom equipment. This makes the QKD a suitable candidate for the security of distributed and virtualised network services. In this paper, for the first time, we propose a dynamic quantum-secured optical network for supporting network services that are dynamically created by chaining VNF over multiple network domains. This work includes a new quantum-ROADM, extensions to SDN-enabled optical control plane, and extensions to NFV orchestration to achieve quantum-aware, on-demand chaining of VNFs. The experimental results verify the capability of routing quantum and classical data channels both individually and dynamically over shared fibre links. Moreover, quantum secured chaining of VNFs in 5G networks is experimentally demonstrated via interconnecting four autonomous 5G islands simultaneously through the q-ROADM with eight optical channels using the 5GUK Exchange orchestration platform. The experimental scenarios and results confirm the benefit of the proposed data plane architecture and control/management plane framework

On reliability improvement of Software-Defined Networks

In Software-Defined Networks (SDNs) the role of the centralized controller is crucial, and thus it becomes a single point of failure. In this work, a distributed controller architecture is explored as a possible solution to improve fault tolerance. A network partitioning strategy, with small subnetworks, each with its own Master controller, is combined with the use of Slave controllers for recovery aims. A novel formula is proposed to calculate the reliability rate of each subnetwork, based on the load and considering the number and degree of the nodes as well as the loss rate of the links. The reliability rates are shared among the controllers through a newly-designed East/West bound interface, to select the coordinator for the whole network. This proposed method is called \u201cReliable Distributed SDN (RDSDN).\u201d In RDSDN, the failure of controllers is detected by the coordinator that may undertake a fast recovery scheme to replace them. The numerical results prove performance improvement achievable with the adoption of the RDSDN and show that this approach performs better regarding failure recovery compared to methods used in related research