Dynamic Core VNT Adaptability Based on Predictive Metro-Flow Traffic Models

Abstract

Abstract-MPLS-over-optical virtual network topologies (VNT) can be adapted to near future traffic matrices based on predictive models that are estimated by applying data analytics on monitored origin-destination (OD) traffic. However, the deployment of independent SDN controllers for core and metro segments can bring large inefficiencies to this core network reconfiguration based on traffic prediction when traffic flows from metro areas are rerouted to different ingress nodes in the core. In such case, OD traffic patterns in the core might severely change thus affecting the quality of the predictive OD models. New traffic models' re-estimation usually entails long time during which no predictive capabilities are available for the network operator. To alleviate this problem, we propose to extend data analytics to metro networks to obtain predictive models for the metro-flows; by knowing how these flows are aggregated into OD pairs in the core, we can also aggregate their predictive models thus accurately predicting OD traffic and therefore, enabling core VNT reconfiguration. To obtain quality metro-flow models, we propose an estimation algorithm that processes monitored data and returns a predictive model. In addition, a Flow Controller is proposed for the control architecture to allow metro and core controllers to exchange metro-flow model information. The proposed model aggregation is evaluated through exhaustive simulation, and eventually experimentally assessed together with the Flow Controller in a testbed connecting premises in CNIT (Pisa, Italy) and UPC (Barcelona, Spain).

CURSA-SQ: A Methodology for Service-Centric Traffic Flow Analysis

“…Monitoring data is then processed by statistical and/or machine learning algorithms (analyze) [6] aiming at detecting and identifying some evidence requiring some further actions to be taken (act). Some examples include the reconfiguration of virtual network topologies following traffic changes [7]- [8] and the dimensioning of next planning steps based on traffic prediction [9]; both require the analysis of monitoring data to model and characterize network traffic.…”

“…Nevertheless, the scalability remains a major issue; realistic simulations would consist in generating days or even weeks of traffic sourced by thousands of consumers in a network modelled as a system of queues that packets need to traverse. For instance, several weeks of traffic need to be analyzed to obtain accurate traffic models that would be required for traffic forecasting [8]. In consequence, it is required that execution time must be orders of magnitude smaller than simulation time, which is not typically achieved when huge amounts of packets need to be processed [17].…”

“…Assuming that a continuous queue system propagating flows instead of packets is adopted, a question still remains: how input flows entering the network need to be generated. Authors in [8] proposed a methodology to generate input traffic flows for distinct service types according to different daily patterns of expected traffic and distributions for the variance around that expectation. However, this approach makes difficult to generate profiles of new, incipient services, where there is no clear evidence on how the traffic generated by the service consumers behaves.…”

A control and management architecture supporting autonomic NFV services

“…Specifically, we assume in this example that the pool of HTTP servers need to be increased and the capacity of the VNT connecting the leaf cache to the users incremented; we assume that such VNT includes capacity for live-TV and for VoD contents. Hence, the CDN controller request the NFVO to scale the VNF by increasing the number of HTTP servers and incrementing the capacity of the VNT toward the users (5)(6)(7)(8)(9)(10). Finally, the availability of a new HTTP server is announced to the local cache manager, so that it can add that server to its local pool (11).…”

“…It is worth highlighting the importance of the control loops for autonomic networking, as it fundamentally changes the way networks are operated today -empowering truly dynamic and autonomous operation. Examples of control loops include soft-failure detection and localization [6], [7], and autonomic virtual network topology (VNT) management [8], [9].…”