Bilal Omar Akram

Controller placement in software-defined wide area networks (SD-WANs) is commonly optimized assuming continuously available controllers, leaving the control plane vulnerable to overload and abrupt reassignment after controller unavailability. This paper presents Sentinel, an auditable supervisory mechanism that extends GMM-MARL placement into proactive elastic controller management through utilization monitoring, hysteresis-based scaling, learned aiding-controller placement, and bounded incremental migration. Deterministic supervision decides when scaling is authorized, whereas the learned policy selects placement and assignment subject to capacity and latency constraints. Evaluation uses a 95-node composite topology derived from OS3E, Darkstrand, and CRL. An operational track compares proactive Sentinel scale-out with reactive CCA-PSO, and an equal-resource ablation separates resource restoration from placement quality. Sentinel reduces operational average case latency (ACL) from 3988 to 3008 µs and limits maximum utilization to 87.5%, versus 98.4% under CCA-PSO. Under equal resources, Sentinel achieves 3453 ± 627 µs ACL, within 2.9% of static GMM-MARL re-optimization. An auxiliary matched RL application benchmark on OS3E against a reconstruction (RL-HCP*) shows lower ACL and worst-case latency for RL-HCP*, while Sentinel retains competitive load and coordination behavior. Overall, proactive controller headroom restoration, learned placement, and bounded migration provide resilient control-plane management without disruptive global reassignment.

Modern Software-Defined Wide Area Networks (SD-WANs) require adaptive controller placement addressing multi-objective optimization where latency minimization, load balancing, and fault tolerance must be simultaneously optimized. Traditional static approaches fail under dynamic network conditions with evolving traffic patterns and topology changes. This paper presents a novel hybrid framework integrating Gaussian Mixture Model (GMM) clustering with Multi-Agent Reinforcement Learning (MARL) for dynamic controller placement. The approach leverages probabilistic clustering for intelligent MARL initialization, reducing exploration requirements. Centralized Training with Decentralized Execution (CTDE) enables distributed optimization through cooperative agents. Experimental evaluation using real-world topologies demonstrates a noticeable reduction in the latency, improvement in network balance, and significant computational efficiency versus existing methods. Dynamic adaptation experiments confirm superior scalability during network changes. The hybrid architecture achieves linear scalability through problem decomposition while maintaining real-time responsiveness, establishing practical viability.

Software-Defined Wide Area Networks (SD-WANs) require optimal controller placement to minimize latency, balance loads, and ensure reliability across geographically distributed infrastructures. This paper introduces NA-GMM (Network-Aware Gaussian Mixture Model), a novel multi-objective optimization framework addressing key limitations in current controller placement approaches. Three principal contributions distinguish NA-GMM: (1) a hybrid distance metric that integrates geographic distance, network latency, topological cost, and link reliability through adaptive weighting, effectively capturing multi-dimensional network characteristics; (2) a modified expectation–maximization algorithm incorporating node importance-weighting to optimize controller placements for critical network elements; and (3) a robust clustering mechanism that transitions from probabilistic (soft) assignments to definitive (hard) cluster selections, ensuring optimal placement convergence. Empirical evaluations on real-world topologies demonstrate NA-GMM’s superiority, achieving up to 22.7% lower average control latency compared to benchmark approaches, maintaining near-optimal load distribution with node distribution ratios, and delivering a 12.9% throughput improvement. Furthermore, NA-GMM achieved exquisite computational efficiency, executing 68.9% faster and consuming 41.5% less memory than state of the art methods, while achieving exceptional load balancing. These findings confirm NA-GMM’s practical viability for large-scale SD-WAN deployments where real-time multi-objective optimization is essential.

The development of new technological applications is driving the need for deterministic network communication that exhibits reliable behavior. This is particularly relevant for applications like automation and autonomous vehicles, which require real-time network responsiveness. This has led to the development of various technologies, including Time-Sensitive Networking (TSN). Recent studies have explored the optimization of scheduling and routing in TSN, as this is crucial for meeting TSN’s objectives. Current methods primarily focus on scheduling Time-Triggered (TT) transmissions, often overlooking the scheduling of lower-priority traffic types. Many of these methods do not optimize routing, and only a few consider reliability. In this paper, we present the Optimized Hybrid Deterministic Scheduling and Routing Plus (OHDSR+) approach. This method prioritizes communications, optimizing both scheduling and routing of TT communications, while also addressing the requirements of lower-priority best-effort (BE) communications, and ensuring the reliability of TT communications. We evaluated our approach using various problem instances. The results demonstrate our approach’s effectiveness in managing different traffic types and ensuring reliable transmission for TT traffic. Our model achieves significant improvements compared to the state-of-the-art REU-CP-R method, with reductions in total latency of up to 1.03%, total response time of up to 34.70%, and scheduling response time of up to 63.97%. Notably, OHDSR+ exhibits superior scalability, successfully finding solutions for all tested network sizes with a 100% success rate, while REU-CP-R achieved only an 87.5% success rate. This highlights OHDSR+’s ability to provide optimal solutions for networks of various sizes with minimal latency and response times.

Real-time communications characterized by low-latency, deterministic, and reliable behavior are crucial for the advancement of emerging technologies. Consequently, Time-Sensitive Networking (TSN) has been developed to address the distinct demands of sectors such as automation and autonomous vehicles applications. This is currently achieved through various methods that emphasize the scheduling of critical data traffic. However, many of these methods determine routes independently, potentially impacting the schedulability of transmissions. Additionally, there is a noticeable lack of emphasis on the scheduling and routing of low-priority transmissions within TSN. In this paper, we introduce the optimized hybrid deterministic scheduling and routing (OHDSR) approach. This method takes into account the priority of communications to jointly optimize the scheduling and routing of time-triggered (TT) communications, while also catering to low-priority best-effort (BE) communications. Extensive experimental evaluations show the high efficiency of our proposed method. It ensures not only the prompt delivery of TT communications but also the delivery of BE communications within suitable time frames, with a maximum difference of 14.29% from TT communications, meeting their respective deadlines. Moreover, the evaluation demonstrates the high scalability of the proposed approach, providing improved response times compared to the latest work for both routing and scheduling.