arXiv subjects

Riti Gour

Publications and source records attributed to Riti Gour.

6 recordsLinked to original sources

arXiv2026

Network Slice Embedding over Space Division Multiplexed Elastic Optical Networks

Network slicing over space division multiplexed elastic optical networks (SDM EONs) enables efficient multiservice provisioning on a shared optical substrate. However, embedding such slices requires coordinated spectrum and compute resource management under dynamic traffic, which most existing RMCSA studies treat independently. This paper focuses on the network slice embedding problem over space division multiplexed elastic optical networks (SDM EONs), aiming to develop efficient resource allocation strategies that ensure both high utilization and reliable service performance. While prior studies have investigated routing, modulation format, core, and spectrum allocation (RMCSA), they typically consider these dimensions separately from compute placement. To address this gap, this paper proposes a Waypoint Assisted Multi Segment Slice Mapping (WMSM) scheme, which integrates compute placement with spectrum allocation in a sequential but coupled manner to enable more flexible resource placement and improved spectrum efficiency. Numerical results show that WMSM improves acceptance ratios by up to 27% under high load conditions, while achieving up to 47% lower total provisioning cost relative to the baseline strategy. These results highlight the benefits of integrated compute spectrum provisioning and provide design insights for scalable, compute aware optical slice mapping.

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arXiv2024

Multi-domain Network Slice Partitioning: A Graph Neural Network Algorithm

In the context of multi-domain network slices, multiple domains need to work together to provide a service. The problem of determining which part of the service fits within which domain is referred to as slice partitioning. The partitioning of multi-domain network slices poses a challenging problem, particularly when striving to strike the right balance between inter-domain and intra-domain costs, as well as ensuring optimal load distribution within each domain. To approach the optimal partition solution while maintaining load balance between domains, a framework has been proposed. This framework not only generates partition plans with various characteristics but also employs a Graph Neural Network solver, which significantly reduces the plan generation time. The proposed approach is promising in generating partition plans for multi-domain network slices and is expected to improve the overall performance of the network.

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arXiv2022

Dynamic Bandwidth Allocation for PON Slicing with Performance-Guaranteed Online Convex Optimization

The emergence of diverse network applications demands more flexible and responsive resource allocation for networks. Network slicing is a key enabling technology that provides each network service with a tailored set of network resources to satisfy specific service requirements. The focus of this paper is the network slicing of access networks realized by Passive Optical Networks (PONs). This paper proposes a learning-based Dynamic Bandwidth Allocation (DBA) algorithm for PON access networks, considering slice-awareness, demand-responsiveness, and allocation fairness. Our online convex optimization-based algorithm learns the implicit traffic trend over time and determines the most robust window allocation that reduces the average latency. Our simulation results indicate that the proposed algorithm reduces the average latency by prioritizing delay-sensitive and heavily-loaded ONUs while guaranteeing a minimal window allocation to all ONUs.

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arXiv2019

DeepPR: Progressive Recovery for Interdependent VNFs with Deep Reinforcement Learning

The increasing reliance upon cloud services entails more flexible networks that are realized by virtualized network equipment and functions. When such advanced network systems face a massive failure by natural disasters or attacks, the recovery of the entire system may be conducted in a progressive way due to limited repair resources. The prioritization of network equipment in the recovery phase influences the interim computation and communication capability of systems, since the systems are operated under partial functionality. Hence, finding the best recovery order is a critical problem, which is further complicated by virtualization due to dependency among network nodes and layers. This paper deals with a progressive recovery problem under limited resources in networks with VNFs, where some dependent network layers exist. We prove the NP-hardness of the progressive recovery problem and approach the optimum solution by introducing DeepPR, a progressive recovery technique based on Deep Reinforcement Learning (Deep RL). Our simulation results indicate that DeepPR can achieve the near-optimal solutions in certain networks and is more robust to adversarial failures, compared to a baseline heuristic algorithm.

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arXiv2019

Improving the Survivability of Clustered Interdependent Networks by Restructuring Dependencies

The interdependency between different network layers is commonly observed in Cyber Physical Systems and communication networks adopting the dissociation of logic and hardware implementation, such as Software Defined Networking and Network Function Virtualization. This paper formulates an optimization problem to improve the survivability of interdependent networks by restructuring the provisioning relations. A characteristic of the proposed algorithm is that the continuous availability of the entire system is guaranteed during the restructuring of dependencies by the preservation of certain structures in the original networks. Our simulation results demonstrate that the proposed restructuring algorithm can substantially enhance the survivability of interdependent networks, and provide insights into the ideal allocation of dependencies.

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arXiv2018

On Reducing IoT Service Delay via Fog Offloading

With the Internet of Things (IoT) becoming a major component of our daily life, understanding how to improve the quality of service (QoS) for IoT applications through fog computing is becoming an important problem. In this paper, we introduce a general framework for IoT-fog-cloud applications, and propose a delay-minimizing collaboration and offloading policy for fog-capable devices that aims to reduce the service delay for IoT applications. We then develop an analytical model to evaluate our policy and show how the proposed framework helps to reduce IoT service delay.

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