Emerging services and applications demanding high bitrate and stringent quality of service requirements are pushing telecom operators to upgrade their core networks based on wavelength-division multiplexing (WDM) to a more flexible technology for the more dynamic and variable traffic that is expected to be conveyed. Finally, academy- and industry-driven research on elastic optical networks (EON) has turned out into a mature enough technology ready to gradually upgrade WDM-based networks. Among others, key EON features include flexible spectrum allocation, connections beyond 100 Gb/s, advanced modulation formats, and elasticity against time-varying traffic. As a consequence of the variety of features involved, network design and algorithms for EONs are remarkably more complex than those for WDM networks. However, new opportunities for network operators to reduce costs arise by exploiting those features; in fact, the classical network life cycle based on fixed periodical planning cycles needs to be adapted to greatly reduce overprovisioning by applying reoptimization techniques to reconfigure the network while it is in operation and to efficiently manage new services, such as datacenter interconnection that will require provisioning multicast connections and elastic spectrum allocation for time-varying traffic. This paper reviews and extends mathematical models and algorithms to solve optimization problems related to the design, operation, and reoptimization of EONs. In addition, two use cases are presented as illustrative examples on how the network life cycle needs to be extended with in-operation planning and data analytics thus adding cognition to the network.
Incremental planning is performed periodically to decide how a backbone optical network has to be updated to serve the forecast traffic during the next planning period. Based on reliable traffic prediction, new equipment is installed and its capacity is ready to be used. Nonetheless, due among others to the introduction of new services, exact prediction is not usually available, which leads to install more capacity than that required thus, increasing network
P. Vela, Alba; Via, A.; Morales, F.; Ruiz, M.; Velasco, L. International Conference on Transparent Optical Networks p. 1-4 DOI: 10.1109/ICTON.2016.7550544 Data de presentació: 2016-07 Presentació treball a congrés
With the incremental amount of applications running over the telecom cloud architecture it is becoming of paramount importance being able to run simulations aiming at evaluating the performance of such applications. To that end, one of the key elements in the simulation is how to generate network traffic. In this paper we propose realistic
traffic functions that can be used for such purposes and present how those functions have been integrated in our OMNET++-based simulator.
ABNO's OAM Handler is extended with big data analytics capabilities to anticipate traffic changes in volume and direction. Predicted traffic is used to trigger virtual network topology re-optimization. When the virtual topology needs to be reconfigured, predicted and current traffic matrices are used to find the optimal topology. A heuristic
algorithm to adapt current virtual topology to meet both actual demands and expected traffic matrix is proposed. Experimental assessment is carried out on UPC's SYNERGY testbed.
Big data analytics is applied for IP traffic prediction. When the virtual topology needs to be reconfigured, predicted and current traffic matrices are used to find the optimal topology. Exhaustive simulation results reveal large benefits.
Velasco, L.; Morales, F.; Gifre, L.; Castro, A.; González de Dios , O.; Ruiz, M. Journal of optical communications and networking Vol. 8, num. 1, p. 11-22 DOI: 10.1364/JOCN.8.000011 Data de publicació: 2016-01-01 Article en revista
Incremental planning is performed periodically to decide how a backbone optical network has to be updated to serve the forecast traffic during the next planning period. Based on reliable traffic prediction, new equipment is installed and its capacity is ready to be used. Nonetheless, due among others to the introduction of new services, exact prediction is not usually available, which leads to installing more capacity than that required thus, increasing network expenditures. To reduce expenses, in this paper we propose to increment the capacity of the network as soon as it is required to meet the target performance. Hence, performance metrics are monitored and the incremental capacity (INCA) planning problem is solved on-
demand when some drops under a threshold. The INCA problem
is mathematically modelled and a heuristic algorithm is proposed
to solve the problem in practical times. In view of the INCA problem needs to access both, operation and inventory databases, an architecture to support on-demand network planning as well as a model for the inventory is proposed. Exhaustive simulation results together with its experimental assessment validate the proposed on-demand incremental network capacity planning.