arXiv subjects

Sidharth Sharma

Publications and source records attributed to Sidharth Sharma.

5 recordsLinked to original sources

arXiv2026

Split-Aware Function Placement with Availability Guarantees and Optical Provisioning in vRANs

The rapid evolution of beyond-5G and emerging 6G networks is driving the need for flexible, reliable, and cost-efficient virtualized Radio Access Network (vRAN) architectures capable of supporting heterogeneous services such as enhanced Mobile Broadband (eMBB), Ultra-Reliable Low-Latency Communication (URLLC), and Massive Machine-Type Communication (mMTC). Future disaggregated RAN systems are expected to rely heavily on network slicing, functional split flexibility, and optical x-haul infrastructures to support stringent performance, scalability, and availability requirements. In this paper, we present an integrated framework for reliable, slice-aware, and functional split-aware Virtual Network Function (VNF) placement with lightpath provisioning in disaggregated vRAN environments. The proposed approach maximizes mobile network operators' profit by jointly optimizing function placement and optical resource allocation under latency, processing, bandwidth, and availability constraints. We formulate the problem as an Integer Linear Programming (ILP) model with two variants: one that employs unshared backups and another that uses a more cost-efficient shared backup scheme. To address ILP complexity, we develop a heuristic algorithm and a Genetic Algorithm (GA)-based metaheuristic that yields near-optimal solutions in real time. Extensive evaluations on topologies up to 128 nodes show that shared backup variants yield up to 18% higher profit, while maintaining up to 5-10% lower normalized CPU usage than unshared counterparts.

cs.NI↗

arXiv2026

ScaleAcross: Designing Multi-Data-Center Infrastructure for Geo-Distributed AI Training

The rapid growth of AI models and increasing data sovereignty requirements are driving the transition toward geo-distributed AI training across multiple data centers. Such deployments introduce system-level challenges arising from synchronization-intensive communication, cross-site data exchange, and wide-area latency constraints. This paper investigates EVPN--VXLAN as an infrastructure foundation for geo-distributed AI training environments and presents a scalable emulation framework for systematically studying distributed AI workloads under realistic wide-area conditions. The proposed framework combines VXLAN overlays with EVPN-based inter-data-center connectivity and is implemented using ContainerLab and FRRouting (FRR). The framework further incorporates Equal-Cost Multi-Path (ECMP) routing, Bidirectional Forwarding Detection (BFD), and a queue-pair-aware traffic distribution mechanism designed to improve communication behavior for synchronization-intensive AI workloads while preserving compatibility with commodity infrastructure. Using realistic WAN emulation, we characterize communication and system behavior under distributed training workloads employing AllReduce and Parameter Server communication patterns. Results provide insights into traffic distribution, resilience, and infrastructure behavior in geo-distributed AI environments, highlighting the potential of reproducible multi-data-center infrastructure frameworks for scalable distributed AI training.

cs.NI↗

arXiv2024

Efficient Adaptive Federated Optimization

Adaptive optimization is critical in federated learning, where enabling adaptivity on both the server and client sides has proven essential for achieving optimal performance. However, the scalability of such jointly adaptive systems is often hindered by resource limitations in communication and memory. In this paper, we introduce a class of efficient adaptive algorithms, named $FedAda^2$ and its enhanced version $FedAda^2$++, designed specifically for large-scale, cross-device federated environments. $FedAda^2$ optimizes communication efficiency by avoiding the transfer of preconditioners between the server and clients. Additionally, $FedAda^2$++ extends this approach by incorporating memory-efficient adaptive optimizers on the client side, further reducing on-device memory usage. Theoretically, we demonstrate that $FedAda^2$ and $FedAda^2$++ achieve the same convergence rates for general, non-convex objectives as its more resource-intensive counterparts that directly integrate joint adaptivity. Extensive empirical evaluations on image and text datasets demonstrate both the advantages of joint adaptivity and the effectiveness of $FedAda^2$/$FedAda^2$++.

cs.LG↗

arXiv2024

VERCEL: Verification and Rectification of Configuration Errors with Least Squares

We present Vercel, a network verification and automatic fault rectification tool that is based on a computationally tractable, algorithmically expressive, and mathematically aesthetic domain of linear algebra. Vercel works on abstracting out packet headers into standard basis vectors that are used to create a port-specific forwarding matrix $\mathcal{A}$, representing a set of packet headers/prefixes that a router forwards along a port. By equating this matrix $\mathcal{A}$ and a vector $b$ (that represents the set of all headers under consideration), we are able to apply \textit{least squares} (which produces a column rank agnostic solution) to compute which headers are reachable at the destination. Reachability now simply means evaluating if vector $b$ is in the column space of $\mathcal{A}$, which can efficiently be computed using least squares. Further, the use of vector representation and least squares opens new possibilities for understanding network behavior. For example, we are able to map rules, routing policies, what-if scenarios to the fundamental linear algebraic form, $\mathcal{A}x=b$, as well as determine how to configure forwarding tables appropriately. We show Vercel is faster than the state-of-art such as NetPlumber, Veriflow, APKeep, AP Verifier, when measured over diverse datasets. Vercel is almost as fast as Deltanet, when rules are verified in batches and provides better scalability, expressiveness and memory efficiency. A key highlight of Vercel is that while evaluating for reachability, the tool can incorporate intents, and transform these into auto-configurable table entries, implying a recommendation/correction system.

cs.NI↗

arXiv2020

A Survey on Segment Routing with Emphasis on Use Cases in Large Provider Networks

Segment routing is heralded as important technology innovation in large provider networks. In the domain of large telecom service providers, segment routing has the potential to be in the same league as MPLS and IPv6 as well as pave a way towards the successful implementation of SDNs. In this regard, this paper is a survey on the existing segment routing work in the community. We begin by describing how segment routing works, inclusive of the various building blocks. The paper describes the segment routing architecture in terms of its positioning vis-a-vis existing networking technologies, as well as the data-plane and the control plane. Label encoding techniques that are central towards implementing segment routing are then discussed. In order to make segment routing relevant to the current domain of providers, we postulate various use cases, their working and issues of interoperability. The paper also reviews the current set of segment routing implementation cases in the industry. Thereafter we position segment routing among the various provider manifestations.

cs.NI↗