arXiv subjects

Suresh Subramaniam

Publications and source records attributed to Suresh Subramaniam.

At least 19 recordsLinked to original sources

arXiv2026

Age of Information Optimization in Distributed Sensor Networks with Half-Duplex Channels

Motivated by cooperative distributed networks in which users dynamically alternate between transmit and receive modes under half-duplex constraints, this paper studies the Age of Information (AoI) in a distributed multi-user network using an ALOHA-based protocol. We derive closed-form expressions for the average AoI and formulate an optimization problem over transmission probabilities. After proving the convexity of the problem, we leverage the derived optimality conditions to characterize optimal policies for general network graphs, obtain closed-form solutions for $d$-regular topologies, and derive tractable optimality conditions for star topologies. Numerical results confirm that the proposed mechanism can effectively and adaptively determine user-specific optimal transmission probabilities across varying network topologies. These findings contribute to the design of adaptive and efficient distributed networks with enhanced information freshness.

cs.IT↗

arXiv2026

A benchmarking framework for PON-based fronthaul network design

As mobile networks transition toward 5G and 6G RAN architectures, Passive Optical Networks (PONs) offer a critical solution for cost-effective fronthaul transport. However, the lack of standardized evaluation models in current literature makes an objective comparison of diverse optimization strategies difficult. This paper addresses this gap by proposing a unified benchmarking framework that standardizes cost catalogs and deployment scenarios. We formulate the network design problem using Integer Linear Programming (ILP) to establish optimality bounds and evaluate three scalable heuristic strategies: a Genetic Algorithm, K-Means Clustering (KMC+), and a graph-based Randomized Successive Splitter Assignment (RSSA+) algorithm. Simulation results show that a time-limited ILP remains a strong reference point, even when optimality is not reached. Despite being rarely used in prior fronthaul planning studies, it consistently yields solutions superior to those produced by standard heuristic methods. Among scalable approaches, RSSA+ reliably attains near-ILP performance while ensuring feasibility across all evaluated scenarios, which underscores the importance of advanced, constraint-aware algorithmic designs over simpler heuristics.

cs.NI↗

arXiv2025

Digital Twin-Empowered Deep Reinforcement Learning for Intelligent VNF Migration in Edge-Core Networks

The growing demand for services and the rapid deployment of virtualized network functions (VNFs) pose significant challenges for achieving low-latency and energy-efficient orchestration in modern edge-core network infrastructures. To address these challenges, this study proposes a Digital Twin (DT)-empowered Deep Reinforcement Learning framework for intelligent VNF migration that jointly minimizes average end-to-end (E2E) delay and energy consumption. By formulating the VNF migration problem as a Markov Decision Process and utilizing the Advantage Actor-Critic model, the proposed framework enables adaptive and real-time migration decisions. A key innovation of the proposed framework is the integration of a DT module composed of a multi-task Variational Autoencoder and a multi-task Long Short-Term Memory network. This combination collectively simulates environment dynamics and generates high-quality synthetic experiences, significantly enhancing training efficiency and accelerating policy convergence. Simulation results demonstrate substantial performance gains, such as significant reductions in both average E2E delay and energy consumption, thereby establishing new benchmarks for intelligent VNF migration in edge-core networks.

cs.NI↗

arXiv2025

Intelligent Edge Resource Provisioning for Scalable Digital Twins of Autonomous Vehicles

The next generation networks offers significant potential to advance Intelligent Transportation Systems (ITS), particularly through the integration of Digital Twins (DTs). However, ensuring the uninterrupted operation of DTs through efficient computing resource management remains an open challenge. This paper introduces a distributed computing archi tecture that integrates DTs and Mobile Edge Computing (MEC) within a software-defined vehicular networking framework to enable intelligent, low-latency transportation services. A network aware scalable collaborative task provisioning algorithm is de veloped to train an autonomous agent, which is evaluated using a realistic connected autonomous vehicle (CAV) traffic simulation. The proposed framework significantly enhances the robustness and scalability of DT operations by reducing synchronization errors to as low as 5% while achieving up to 99.5% utilization of edge computing resources.

cs.NI↗

arXiv2025

A Novel Dynamic Bandwidth Allocation Design for 100G Coherent Passive Optical Network

With the rapid advancements in coherent Passive Optical Network (PON) technologies featuring 100G and higher data rates, this paper addresses the urgent requirement for sophisticated simulation and MAC layer development within the domain of coherent Time Division Multiplexing (TDM) PON and coherent Time and Frequency Division Multiplexing (TFDM) PON networks. The ever-growing demand for latency-sensitive services and expanding user populations in next-generation 100G and beyond coherent PONs, underscores the crucial need for low-latency bandwidth management and efficient Dynamic Bandwidth Allocation (DBA) mechanisms. In this paper, we present a pioneering analysis of two established DBAs from the perspective of temporal misalignments. Subsequently, a novel DBA algorithm tailored for coherent PONs featuring 100 Gbps data rate and up to 512 end-users is introduced, named the Hybrid-Switch DBA. This innovative approach allows for adaptive switching of the DBA scheme in response to real-time traffic conditions. To the best of our knowledge, this paper represents the first attempt to address the misalignment problem of DBA and proposes a novel DBA solution for both TDM- and TFDM-based coherent PON networks. This research significantly contributes to the development of coherent TDM PON and coherent TFDM PON networks by enhancing the efficiency of bandwidth allocation and addressing the challenges associated with misalignments in DBA mechanisms. As optical access networks continue to evolve to meet the ever-increasing demands of modern communication services, the Hybrid-Switch DBA algorithm presented in this paper offers a promising solution for optimizing network performance and accommodating latency-sensitive applications.

cs.NI↗

arXiv2025

Federated Deep Reinforcement Learning-Driven O-RAN for Automatic Multirobot Reconfiguration

The rapid evolution of Industry 4.0 has led to the emergence of smart factories, where multirobot system autonomously operates to enhance productivity, reduce operational costs, and improve system adaptability. However, maintaining reliable and efficient network operations in these dynamic and complex environments requires advanced automation mechanisms. This study presents a zero-touch network platform that integrates a hierarchical Open Radio Access Network (O-RAN) architecture, enabling the seamless incorporation of advanced machine learning algorithms and dynamic management of communication and computational resources, while ensuring uninterrupted connectivity with multirobot system. Leveraging this adaptability, the platform utilizes federated deep reinforcement learning (FedDRL) to enable distributed decision-making across multiple learning agents, facilitating the adaptive parameter reconfiguration of transmitters (i.e., multirobot system) to optimize long-term system throughput and transmission energy efficiency. Simulation results demonstrate that within the proposed O-RAN-enabled zero-touch network platform, FedDRL achieves a 12% increase in system throughput, a 32% improvement in normalized average transmission energy efficiency, and a 28% reduction in average transmission energy consumption compared to baseline methods such as independent DRL.

cs.NI↗

arXiv2025

Age of Information Optimization with Preemption Strategies for Correlated Systems

In this paper, we examine a multi-sensor system where each sensor monitors multiple dynamic information processes and transmits updates over a shared communication channel. These updates may include correlated information across the various processes. In this type of system, we analyze the impact of preemption, where ongoing transmissions are replaced by newer updates, on minimizing the Age of Information (AoI). While preemption is optimal in some scenarios, its effectiveness in multi-sensor correlated systems remains an open question. To address this, we introduce a probabilistic preemption policy, where the source sensor preemption decision is stochastic. We derive closed-form expressions for the AoI and frame its optimization as a sum of linear ratios problem, a well-known NP-hard problem. To navigate this complexity, we establish an upper bound on the iterations using a branch-and-bound algorithm by leveraging a reformulation of the problem. This analysis reveals linear scalability with the number of processes and a logarithmic dependency on the reciprocal of the error that shows the optimal solution can be efficiently found. Building on these findings, we show how different correlation matrices can lead to distinct optimal preemption strategies. Interestingly, we demonstrate that the diversity of processes within the sensors' packets, as captured by the correlation matrix, plays a more significant role in preemption priority than the number of updates.

cs.IT↗

arXiv2024

RMCSA Algorithm for Congestion-Aware and Service Latency Aware Dynamic Service Provisioning in Software-Defined SDM-EONs

The implementation of 5G and the future deployment of 6G necessitate the utilization of optical networks that possess substantial capacity and exhibit minimal latency. The dynamic arrival and departure of connection requests in optical networks result in particular central links experiencing more traffic and congestion than non-central links. The occurrence of congested links leads to service blocking despite the availability of resources within the network, restricting the efficient utilization of network resources. The available algorithms in the literature that aim to balance load among network links offer a trade-off between blocking performance and algorithmic complexity, thus increasing service provisioning time. This work proposes a dynamic routing-based congestion-aware routing, modulation, core, and spectrum assignment (RMCSA) algorithm for space division multiplexing elastic optical networks (SDM-EONs). The algorithm finds alternative candidate paths based on real-time link occupancy metrics to minimize blocking due to link congestion under dynamic traffic scenarios. As a result, the algorithm reduces the formation of congestion hotspots in the network owing to link-betweenness centrality. We have performed extensive simulations using two realistic network topologies to compare the performance of the proposed algorithm with relevant RMCSA algorithms available in the literature. The simulation results verify the superior performance of our proposed algorithm compared to the benchmark Yen's K-shortest paths and K-Disjoint shortest paths RMCSA algorithms in connection blocking ratio and spectrum utilization efficiency. To expedite the route-finding process, we present a novel caching strategy that allows the proposed algorithm to demonstrate a much-reduced service delay time compared to the recently developed adaptive link weight-based load-balancing RMCSA algorithm.

cs.NI↗

arXiv2024

Digital Twin Enabled Data-Driven Approach for Traffic Efficiency and Software-Defined Vehicular Network Optimization

In the realms of the internet of vehicles (IoV) and intelligent transportation systems (ITS), software defined vehicular networks (SDVN) and edge computing (EC) have emerged as promising technologies for enhancing road traffic efficiency. However, the increasing number of connected autonomous vehicles (CAVs) and EC-based applications presents multi-domain challenges such as inefficient traffic flow due to poor CAV coordination and flow-table overflow in SDVN from increased connectivity and limited ternary content addressable memory (TCAM) capacity. To address these, we focus on a data-driven approach using virtualization technologies like digital twin (DT) to leverage real-time data and simulations. We introduce a DT design and propose two data-driven solutions: a centralized decision support framework to improve traffic efficiency by reducing waiting times at roundabouts and an approach to minimize flow-table overflow and flow re-installation by optimizing flow-entry lifespan in SDVN. Simulation results show the decision support framework reduces average waiting times by 22% compared to human-driven vehicles, even with a CAV penetration rate of 40%. Additionally, the proposed optimization of flow-table space usage demonstrates a 50% reduction in flow-table space requirements, even with 100% penetration of connected vehicles.

cs.NI↗

arXiv2024

Age of Information Optimization and State Error Analysis for Correlated Multi-Process Multi-Sensor Systems

In this paper, we examine a multi-sensor system where each sensor may monitor more than one time-varying information process and send status updates to a remote monitor over a common channel. We consider that each sensor's status update may contain information about more than one information process in the system subject to the system's constraints. To investigate the impact of this correlation on the overall system's performance, we conduct an analysis of both the average Age of Information (AoI) and source state estimation error at the monitor. Building upon this analysis, we subsequently explore the impact of the packet arrivals, correlation probabilities, and rate of processes' state change on the system's performance. Next, we consider the case where sensors have limited sensing abilities and distribute a portion of their sensing abilities across the different processes. We optimize this distribution to minimize the total AoI of the system. Interestingly, we show that monitoring multiple processes from a single source may not always be beneficial. Our results also reveal that the optimal sensing distribution for diverse arrival rates may exhibit a rapid regime switch, rather than smooth transitions, after crossing critical system values. This highlights the importance of identifying these critical thresholds to ensure effective system performance.

cs.IT↗

arXiv2024

FedAuxHMTL: Federated Auxiliary Hard-Parameter Sharing Multi-Task Learning for Network Edge Traffic Classification

Federated Learning (FL) has garnered significant interest recently due to its potential as an effective solution for tackling many challenges in diverse application scenarios, for example, data privacy in network edge traffic classification. Despite its recognized advantages, FL encounters obstacles linked to statistical data heterogeneity and labeled data scarcity during the training of single-task models for machine learning-based traffic classification, leading to hindered learning performance. In response to these challenges, adopting a hard-parameter sharing multi-task learning model with auxiliary tasks proves to be a suitable approach. Such a model has the capability to reduce communication and computation costs, navigate statistical complexities inherent in FL contexts, and overcome labeled data scarcity by leveraging knowledge derived from interconnected auxiliary tasks. This paper introduces a new framework for federated auxiliary hard-parameter sharing multi-task learning, namely, FedAuxHMTL. The introduced framework incorporates model parameter exchanges between edge server and base stations, enabling base stations from distributed areas to participate in the FedAuxHMTL process and enhance the learning performance of the main task-network edge traffic classification. Empirical experiments are conducted to validate and demonstrate the FedAuxHMTL's effectiveness in terms of accuracy, total global loss, communication costs, computing time, and energy consumption compared to its counterparts.

cs.LG↗

arXiv2024

Prioritized Multi-Tenant Traffic Engineering for Dynamic QoS Provisioning in Autonomous SDN-OpenFlow Edge Networks

This letter indicates the critical need for prioritized multi-tenant quality-of-service (QoS) management by emerging mobile edge systems, particularly for high-throughput beyond fifth-generation networks. Existing traffic engineering tools utilize complex functions baked into closed, proprietary infrastructures, largely limiting design flexibility, scalability, and adaptiveness. Hence, this study introduces a software-defined networking (SDN)-based dynamic QoS provisioning scheme that prioritizes multi-tenant network traffic while focusing on the base station-edge cloud scenario. The designed scheme first separates control and data planes and enables traffic management automation using SDN programmability. It then implements dynamic QoS management via the SDN-OpenFlow protocol, which ensures ample bandwidth for multiple priority flows and efficiently manages the remaining bandwidth for non-priority traffic. Empirical experiments are conducted with a Mininet network emulator and an OpenDayLight controller. Performance evaluation validates the proposed scheme's effectiveness in meeting multi-tenant QoS criteria, offering a robust solution for traffic prioritization in SDN-based edge networks.

cs.NI↗

arXiv2023

A Bayesian Optimization Framework for Finding Local Optima in Expensive Multi-Modal Functions

Bayesian optimization (BO) is a popular global optimization scheme for sample-efficient optimization in domains with expensive function evaluations. The existing BO techniques are capable of finding a single global optimum solution. However, finding a set of global and local optimum solutions is crucial in a wide range of real-world problems, as implementing some of the optimal solutions might not be feasible due to various practical restrictions (e.g., resource limitation, physical constraints, etc.). In such domains, if multiple solutions are known, the implementation can be quickly switched to another solution, and the best possible system performance can still be obtained. This paper develops a multimodal BO framework to effectively find a set of local/global solutions for expensive-to-evaluate multimodal objective functions. We consider the standard BO setting with Gaussian process regression representing the objective function. We analytically derive the joint distribution of the objective function and its first-order derivatives. This joint distribution is used in the body of the BO acquisition functions to search for local optima during the optimization process. We introduce variants of the well-known BO acquisition functions to the multimodal setting and demonstrate the performance of the proposed framework in locating a set of local optimum solutions using multiple optimization problems.

math.OC↗

arXiv2023

How Costly Was That (In)Decision?

In this paper, we introduce a new metric, named Penalty upon Decision (PuD), for measuring the impact of communication delays and state changes at the source on a remote decision maker. Specifically, the metric quantifies the performance degradation at the decision maker's side due to delayed, erroneous, and (possibly) missed decisions. We clarify the rationale for the metric and derive closed-form expressions for its average in M/GI/1 and M/GI/1/1 with blocking settings. Numerical results are then presented to support our expressions and to compare the infinite and zero buffer regimes. Interestingly, comparing these two settings sheds light on a buffer length design challenge that is essential to minimize the average PuD.

cs.IT↗

arXiv2021

Popcorns-Pro: A Cooperative Network-Server Approach for Data Center Energy Optimization

Data centers have become a popular computing platform for various applications, and they account for nearly 2% of total US energy consumption. Therefore, it has become important to optimize data center power, and reduce their energy footprint. Most existing work optimizes power in servers and networks independently and does not address them together in a holistic fashion that has the potential to achieve greater power savings. In this article, we present PopcornsPro, a cooperative server network framework for energy optimization. We present a comprehensive power model for heterogeneous data center switches along with low power mode designs in combination with the server power model. We design job scheduling algorithms that place tasks onto servers in a power-aware manner, such that servers and network switches can take effective advantage of low power state and available network link capacities. Our experimental results show that we are able to achieve significantly higher savings up to 80% compared to the previously well-known server and network power optimization policies.

cs.AR↗

arXiv2021

Overage and Staleness Metrics for Status Update Systems

Status update systems consist of sensors that take measurements of a physical parameter and transmit them to a remote receiver. Age of Information (AoI) has been studied extensively as a metric for the freshness of information in such systems with and without an enforced hard or soft deadline. In this paper, we propose three metrics for status update systems to measure the ability of different queuing systems to meet a threshold requirement for the AoI. The {\em overage probability} is defined as the probability that the age of the most recent update packet held by the receiver is larger than the threshold. The {\em stale update probability} is the probability that an update is stale, i.e., its age has exceeded the deadline, when it is delivered to the receiver. Finally, the {\em average overage} is defined as the time average of the overage (i.e., age beyond the threshold), and is a measure of the average ``staleness'' of the update packets held by the receiver. We investigate these metrics in three typical status update queuing systems -- M/G/1/1, M/G/1/$2^*$, and M/M/1. Numerical results show the performances for these metrics under different parameter settings and different service distributions. The differences between the average overage and average AoI are also shown. Our results demonstrate that a lower bound exists for the stale update probability when the buffer size is limited. Further, we observe that the overage probability decreases and the stale update probability increases as the update arrival rate increases.

cs.IT↗

arXiv2021

On the Approximability of Related Machine Scheduling under Arbitrary Precedence

Distributed computing systems often need to consider the scheduling problem involving a collection of highly dependent data-processing tasks that must work in concert to achieve mission-critical objectives. This paper considers the unrelated machine scheduling problem for minimizing weighted sum completion time under arbitrary precedence constraints and on heterogeneous machines with different processing speeds. The problem is known to be strongly NP-hard even in the single machine setting. By making use of Queyranne's constraint set and constructing a novel Linear Programming relaxation for the scheduling problem under arbitrary precedence constraints, our results in this paper advance the state of the art. We develop a $2(1+(m-1)/D)$-approximation algorithm (and $2(1+(m-1)/D)+1$-approximation) for the scheduling problem with zero release time (and arbitrary release time), where $m$ is the number of servers and $D$ is the task-skewness product. The algorithm can be efficiently computed in polynomial time using the Ellipsoid method and achieves nearly optimal performance in practice as $D>O(m)$ when the number of tasks per job to schedule is sufficiently larger than the number of machines available. Our implementation and evaluation using a heterogeneous testbed and real-world benchmarks confirms significant improvement in weighted sum completion time for dependent computing tasks.

cs.DC↗

arXiv2020

On Age and Value of Information in Status Update Systems

Motivated by the inherent value of packets arising in many cyber-physical applications (e.g., due to precision of the information content or an alarm message), we consider status update systems with update packets carrying values as well as their generation time stamps. Once generated, a status update packet has a random initial value and a deterministic deadline after which it is not useful (ultimate staleness). In our model, value of a packet decreases in time (even after reception) starting from its generation to ultimate staleness when it vanishes. The value of information (VoI) at the receiver is additive in that the VoI is the sum of the current values of all packets held by the receiver. We investigate various queuing disciplines under potential dependence between value and service time and provide closed form expressions for average VoI at the receiver. Numerical results illustrate the average VoI for different scenarios and the contrast between average age of information (AoI) and average VoI.

cs.IT↗