arXiv subjects

Venkatesh Chebolu

Publications and source records attributed to Venkatesh Chebolu.

4 recordsLinked to original sources

arXiv2026

Leveraging Multi-Step Traffic Forecasts for Multi-Period Planning Optical Networks

In this work, multi-step traffic predictions are leveraged to enable multi-period planning in reconfigurable optical networks. The proposed framework aims to achieve spectrum savings by adapting the network to predicted time-varying conditions while ensuring the necessary quality-of-service (QoS) levels. Since frequent network (re)configurations may lead to undesired service disruptions, traffic predictions spanning various prediction horizons are exploited to balance the trade-off between spectrum savings and service disruptions. For multi-step-ahead prediction, an encoder-decoder deep learning model is employed to analyze real traffic traces. Subsequently, an Integer Linear Programming (ILP) formulation and heuristic algorithms are developed that use the predictions to proactively (re)optimize future network configurations, enhancing spectrum efficiency while minimizing service disruptions. The approaches are utilized under different scenarios, with the ILP achieving better solutions overall, and the heuristics achieving solutions close to the ILP at significantly lower running times. Further, the results present the effect of the prediction horizon on disruptions and over- and under- provisioning, showcasing that the prediction horizon selection greatly depends on the network operator targets in both network performance and predefined service level agreements.

cs.NI↗

arXiv2024

Online RMLSA in EONs with $A^3G$: Adaptive ACO with Augmentation of Graph

Routing and Spectrum Assignment (RSA) represents a significant challenge within Elastic Optical Networks (EONs), particularly in dynamic traffic scenarios where the network undergoes continuous changes. Integrating multiple modulation formats transforms it into Routing Modulation Level and Spectrum Assignment (RMLSA) problem, thereby making it more challenging. Traditionally, addressing the RSA problem involved identifying a fixed number of paths and subsequently allocating spectrum among them. Numerous heuristic and metaheuristic approaches have been proposed for RSA using this two-step methodology. However, solving for routing and assignment of spectrum independently is not recommended due to their interdependencies and their impact on resource utilization, fragmentation and bandwidth blocking probability. In this paper, we propose a novel approach to solve the RMLSA problem jointly in dynamic traffic scenarios, inspired by Ant Colony Optimization (ACO). This approach involves augmenting the network into an Auxiliary Graph and transforming conventional ACO into a constraint-based ACO variant that adapts to the constraints of EONs. This adaptation also includes an adaptive initiation process and an aggressive termination strategy aimed at achieving faster convergence. Moreover, we have introduced a novel objective/fitness function, to minimize average network fragmentation while ensuring optimal spectrum resource utilization, thereby reducing overall blocking probability.

cs.NI↗

arXiv2024

ABACUS: An Impairment Aware Joint Optimal Dynamic RMLSA in Elastic Optical Networks

The challenge of optimal Routing and Spectrum Assignment (RSA) is significant in Elastic Optical Networks. Integrating adaptive modulation formats into the RSA problem - Routing, Modulation Level, and Spectrum Assignment - broadens allocation options and increases complexity. The conventional RSA approach entails predetermining fixed paths and then allocating spectrum within them separately. However, expanding the path set for optimality may not be advisable due to the substantial increase in paths with network size expansion. This paper delves into a novel approach called RMLSA, which proposes a comprehensive solution addressing both route determination and spectrum assignment simultaneously. An objective function named ABACUS, Adaptive Balance of Average Clustering and Utilization of Spectrum, is chosen for its capability to adjust and assign significance to average clustering and spectrum utilization. Our approach involves formulating an Integer Linear Programming model with a straightforward relationship between path and spectrum constraints. The model also integrates Physical Layer Impairments to ensure end-to-end Quality of Transmission for requested connections while maintaining existing ones. We demonstrate that ILP can offer an optimal solution for a dynamic traffic scenario within a reasonable time complexity. To achieve this goal, we adopt a structured formulation approach where essential information is determined beforehand, thus minimizing the need for online computations.

cs.NI↗

arXiv2022

Robust QoT Assured Resource Allocation in Shared Backup Path Protection Based EONs

Survivability is mission-critical for elastic optical networks (EONs) as they are expected to carry an enormous amount of data. In this paper, we consider the problem of designing shared backup path protection (SBPP) based EON that facilitates the minimum quality-of-transmission (QoT) assured allocation against physical layer impairments (PLIs) under any single link/shared risk link group (SRLG) failure for static and dynamic traffic scenarios. In general, the effect of PLIs on lightpath varies based on the location of failure of a link as it introduces different active working and backup paths. To address these issues in the design of SBPP EON, we formulate a mixed integer linear programming (MILP) based robust optimization framework for static traffic with the objective of minimizing overall fragmentation. In this process, we use the efficient bitloading technique for spectrum allocation for the first time in survivable EONs. In addition, we propose a novel SBPP-impairment aware (SBPP-IA) algorithm considering the limitations of MILP for larger networks. For this purpose, we introduce a novel sorting technique named most congested working-least congested backup first (MCW-LCBF) to sort the given set of static requests. Next, we employ our SBPP-IA algorithm for dynamic traffic scenario and compare it with existing algorithms in terms of different QoT parameters. We demonstrated through simulations that our study provides around 40% more QoT guaranteed requests compared to existing ones.

cs.NI↗