Combination of Advanced Reservation and Resource Periodic Arrangement for RMSA in EON with Deep Reinforcement Learning

The Elastic Optical Networks (EON) provide a solution to the massive demand for connections and extremely high data traffic with the Routing Modulation and Spectrum Assignment (RMSA) as a challenge. In previous RMSA research, there was a high blocking probability because the route to be passed by the K-SP method with a deep neural network approach used the First Fit policy, and the modulation problem was solved with Modulation Format Identification (MFI) or BPSK using Deep Reinforcement Learning. The issue might be apparent in spectrum assignment because of the influence of Advanced Reservation (AR) and Resource Periodic Arrangement (RPA), which is a decision block on a connection request path with both idle and active data traffic. The study’s limitation begins with determining the modulation of m = 1 and m = 4, followed by the placement of frequencies, namely 13 with a combination of standard block frequencies 41224–24412, so that the simulation results are less than 0.0199, due to the combination of block frequency slices with spectrum allocation rule techniques.

Impact of Amplification and Regeneration Schemes on the Blocking Performance and Energy Consumption of Wide-Area Elastic Optical Networks

This paper studies the physical layer’s impact on the blocking probability and energy consumption of wide-area dynamic elastic optical networks (EONs). For this purpose, we consider five network configurations, each named with a network configuration identifier (NCI) from 1 to 5, for which the Routing, Modulation Level, and Spectrum Assignment (RMLSA) problem is solved. NCI 1–4 are transparent configurations based on all-EDFA, hybrid Raman/EDFA amplifiers (with different Raman gain ratio ΓR ), all-DFRA, and alternating span configuration (EDFA and DFRA). NCI 5 is a translucent configuration based on all-EDFA and 3R regenerators. We model the physical layer for every network configuration to determine the maximum achievable reach of optical signals. Employing simulation, we calculate the blocking probability and the energy consumption of the different network configurations. In terms of blocking, our results show that NCI 2 and 3 offer the lowest blocking probability, with at least 1 and 3 orders of magnitude of difference with respect to NCI 1 and 5 at high and low traffic loads, respectively. In terms of energy consumption, the best performing alternatives are the ones with the worst blocking (NCI 1), while NCI 3 exhibits the highest energy consumption with NCI 2ΓR=0.75 following closely. This situation highlights a clear trade-off between blocking performance and energy cost that must be considered when designing a dynamic EON. Thus, we identify NCI 2 using ΓR=0.25 as a promising alternative to reduce the blocking probability significantly in wide-area dynamic EONs without a prohibitive increase in energy consumption

Self-Evaluation Applied Mathematics 2003-2008 University of Twente

This report contains the self-study for the research assessment of the Department of Applied Mathematics (AM) of the Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS) at the University of Twente (UT). The report provides the information for the Research Assessment Committee for Applied Mathematics, dealing with mathematical sciences at the three universities of technology in the Netherlands. It describes the state of affairs pertaining to the period 1 January 2003 to 31 December 2008

A Q-learning-based approach for deploying dynamic service function chains

As the size and service requirements of today’s networks gradually increase, large numbers of proprietary devices are deployed, which leads to network complexity, information security crises and makes network service and service provider management increasingly difficult. Network function virtualization (NFV) technology is one solution to this problem. NFV separates network functions from hardware and deploys them as software on a common server. NFV can be used to improve service flexibility and isolate the services provided for each user, thus guaranteeing the security of user data. Therefore, the use of NFV technology includes many problems worth studying. For example, when there is a free choice of network path, one problem is how to choose a service function chain (SFC) that both meets the requirements and offers the service provider maximum profit. Most existing solutions are heuristic algorithms with high time efficiency, or integer linear programming (ILP) algorithms with high accuracy. It’s necessary to design an algorithm that symmetrically considers both time efficiency and accuracy. In this paper, we propose the Q-learning Framework Hybrid Module algorithm (QLFHM), which includes reinforcement learning to solve this SFC deployment problem in dynamic networks. The reinforcement learning module in QLFHM is responsible for the output of alternative paths, while the load balancing module in QLFHM is responsible for picking the optimal solution from them. The results of a comparison simulation experiment on a dynamic network topology show that the proposed algorithm can output the approximate optimal solution in a relatively short time while also considering the network load balance. Thus, it achieves the goal of maximizing the benefit to the service provider