STQ-Scheduler: A Secure and Throughput-Aware Deep Reinforcement Learning Framework for QoE-Driven Resource Scheduling in Distributed Video Streaming Systems

RL-SDNTE: Reinforcement Learning-Driven Traffic Engineering in SDN for QoE Optimization in Video Streaming

Video streaming now accounts for over 80% of global Internet bandwidth, yet most SDN traffic engineering (TE) solutions still optimize for throughput and link utilization rather than what users actually experience. Poor startup times, frequent re-buffering, and unstable bit-rate remain common even on well-managed networks -- largely because the control plane has no visibility into application-layer quality. We present RL-SDNTE, a Reinforcement Learning-based TE framework built directly into an SDN controller that targets end-user Quality of Experience (QoE) as its primary objective. Rather than relying on a single proxy metric, RL-SDNTE feeds four perceptual indicators -- startup latency, re-buffering ratio, mean video quality, and bit-rate oscillation -- into a unified reward function that drives routing decisions. A Deep Q-Network (DQN) agent uses the controller’s global network view together with real-time client feedback to continuously adjust path selection. Testing on a Mini-net emulation platform and a physical 12-node SDN test-bed showed gains of up to 34% in composite QoE, 28% fewer re-buffering events, 22% lower startup latency, and 17% less quality oscillation compared to ECMP, OSPF, DEFO, and heuristic QoE- aware baselines [5]-[7],[15]. The system also scales to topologies beyond 100 nodes without exceeding operationally acceptable convergence times, making it viable for real-world SDN deployments.

Feedback-Driven Online Deep Reinforcement Learning for Hotspot Aware Adaptive Traffic Steering in Non-Stationary Networks

Modern communication networks increasingly operate under non-stationary traffic conditions, where busty traffic and flash crowds challenge traditional static rule-based network control mechanisms. Despite the fact that reinforcement learning has already been explored for network optimization, most existing methods rely on offline-trained policies that lack stable adaptation to traffic in the network that causes high dimensional state. This paper proposes a feedback-driven online deep reinforcement learning framework for versatile traffic steering in mesh networks. The traffic steering problem is considered as a closed loop evaluation process in which a Deep Q-Network (DQN) constantly updates its policies during runtime. To balance the performance in the network, the framework implements a hotspot-aware lightweight state representation, composed of queue length and link utilization for the top three most congested links alongside end-to-end delay, packet loss rate, and throughput. The proposed framework achieves lower delay and packet loss, faster adaptation, and stable throughput compared to the existing static routing and offline-trained RL policies, while maintaining low monitoring overhead.

Deep reinforcement learning for cost-efficient resource management in hybrid cloud environments

Sensitivity and ablation studies confirm stable learning and controllable latency-cost trade-offs, demonstrating that lightweight RL can effectively deliver cost-efficient, adaptive autoscaling in hybrid cloud environments.

PPO-MS: Confidence-Aware and Collaborative Traffic Management for Multimedia Streaming in SDN

The rapid growth of multimedia streaming poses critical challenges, including bursty traffic and congestion, leading to playback delays. The existing separate prediction and control mechanisms for multimedia traffic scheduling, which are based on software-defined networks (SDN), are unable to proactively manage bursty traffic under uncertain conditions. This limitation is particularly evident in SDN-enabled backbone and multimedia-aware access networks, which typically assume centralized control and stable topologies. They lack integration of traffic prediction, traffic shaping, and real-time perception scheduling through reinforcement learning, resulting in low efficiency when exploring multiple paths in dynamic networks. To address this challenge, we propose PPO-MS (Proximal Policy Optimization-based Multimedia Scheduler), an SDN-based multimedia traffic scheduling algorithm integrating three key innovations: 1) A novel LSTM+HTB synergy where LSTM’s confidence intervals dynamically adjust HTB (Hierarchical Token Bucket) shaping parameters, enabling adaptive rate control under prediction uncertainty and overcoming the limitations of static LSTM+HTB hybrids; 2) A Deep Reinforcement Learning (DRL)-optimized path pruning method that reduces state and action spaces by generating a constrained set of $k$ disjoint candidate paths via an improved redundant tree algorithm. Unlike traditional multi-path schemes, this method tightly couples path preselection with the RL decision loop for adaptive, context-aware routing; 3) Generalized Advantage Estimation (GAE)-accelerated PPO for stable convergence in dynamic environments. In contrast to prior works (e.g., LSTM+RL for QoE or standalone tree algorithms), PPO-MS uniquely unifies these modules through confidence-aware traffic shaping and hierarchical decision-making, validated via comparative experiments. Results demonstrate that PPO-MS, through the synergistic integration of confidence-aware traffic shaping and DRL-optimized path pruning, significantly outperforms decoupled baselines. In particular, via isolation studies against simpler alternatives (e.g., mean-prediction and fixed-margin shaping), the confidence-aware shaping mechanism is validated to be superior under bursty traffic conditions. Overall, PPO-MS reduces end-to-end latency by 17.3% and packet loss by 32.4% while achieving 24.4% better load balancing during traffic bursts.

Autonomous Data Pipeline Optimization Using Reinforcement Learning Techniques

Modern large-scale data pipelines support analytics, AI, ML, and real-time applications but face challenges related to scalability, resource utilization, reliability, and changing workloads. This paper proposes a reinforcement learning (RL)-based autonomous optimization framework that integrates RL agents with data orchestration platforms to continuously monitor pipeline states and optimize operations. The framework uses system metrics such as workload patterns, queue lengths, execution delays, resource consumption, and failure rates to make intelligent decisions on task scheduling, resource allocation, workload balancing, and fault recovery. Three RL algorithms—Q-learning, Deep Q-Networks (DQN), and Proximal Policy Optimization (PPO)—are evaluated. Experimental results demonstrate improved throughput, reduced latency, enhanced fault tolerance, and better resource efficiency compared to traditional rule-based approaches. The proposed framework enables adaptive, self-managing data pipelines that improve scalability, resilience, and operational efficiency across enterprise, cloud, and edge environments.

A Deep Reinforcement Learning Framework for Scalable Cloud Service Orchestration