Jitter-based analysis and discussion of burst assembly algorithms

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. J. Aracil, J. A. Hernández, K. Vlachos, and E. Varvarigos, "Jitter-based analysis and discussion of burst assembly algorithms", in 3rd International Conference on Broadband Communications, Networks and Systems, BROADNETS 2006, p. 1-3This work provides a jitter analysis of size-based burst assembly algorithms and also discusses other burst assembly algorithms that use the packet delay as the assembly threshold to provide a bound on jitter.This work has been funded by the European Union e-Photon/ONe+ projec

Flexibility Aggregation of Temporally Coupled Resources in Real Time Balancing Markets Using Machine Learning

In modern power systems with high penetration of renewable energy sources, the flexibility provided by distributed energy resources is becoming invaluable. Demand aggregators offer balancing energy in the real-time balancing market on behalf of flexible resources. A challenging task is the design of the offering strategy of an aggregator. In particular, it is difficult to capture the flexibility cost of a portfolio of flexibility assets within a price-quantity offer, since the costs and constraints of flexibility resources exhibit inter-temporal dependencies. In this article, we propose a generic method for constructing aggregated balancing energy offers that best represent the portfolio's actual flexibility costs, while accounting for uncertainty in future timeslots. For the case study presented, we use offline simulations to train and compare different machine learning (ML) algorithms that receive the information about the state of the flexible resources and calculate the aggregator's offer. Once trained, the ML algorithms can make fast decisions about the portfolio's balancing energy offer in the real-time balancing market. Our simulations show that the proposed method performs reliably towards capturing the flexibility of the Aggregator's portfolio and minimizing the aggregator's imbalances.</p

Experimental comparison of impairment-aware RWA algorithms in a GMPLS-controlled dynamic optical network

The European research project DICONET proposed and implemented a multi-plane impairment-aware solution for flexible, robust and cost-effective core optical networks. The vision of DICONET was realized via a set of cross-layer optimization algorithms designed to serve the network during planning and operation. The cross-layer modules were incorporated in a common software platform forming a planning and operation tool that takes into account physical-layer impairments in the decision making. The overall solution relies on a GMPLS-based control plane that was extended to disseminate the physical layer information required by the cross-layer modules. One of the key activities in DICONET concerns the routing and wavelength assignment of traffic demands that arrive dynamically during the network operation. Identifying the important role of dynamic lightpath provisioning, in this work we focused on the performance of routing algorithms in dynamic optical networks. We tested the suitability and performance of two different online IA-RWA algorithms in a 14-node experimental test-bed that employed centralized control-plane architecture under the same network and traffic conditions. The parameters used to evaluate the two routing engines included the lightpath setup time and the blocking ratio in a traffic scenario where connections arrive and depart from the network dynamically. Results for different traffic loads showed that optimum impairment-aware decisions are made at the expense of higher lightpath setup times.Postprint (published version