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Dynamic stream processing in cloud environments presents unique challenges due to fluctuating data rates, heterogeneous workloads, and constrained resource availability. Traditional static and heuristic-based resource allocation techniques often fail to adapt effectively to these real-time dynamics, leading to suboptimal throughput, excessive latency, and violation of service-level agreements (SLAs). In this paper, we propose a Hierarchical Multi-Agent Deep Reinforcement Learning (H-MADRL) framework to address these limitations through autonomous, data-driven adaptation. Our system integrates Proximal Policy Optimization (PPO) agents with Kubernetes orchestration and Graph Neural Networks (GNNs) for topology-aware scaling decisions. Experiments under simulated and live workloads demonstrate the PPO agent's ability to maintain stable performance during load surges up to 5,000 events/sec without over-provisioning. Comparative evaluation with heuristic rule-based methods shows that the proposed DRL-based framework improves elasticity and responsiveness by up to 35% lower latency and 22% higher throughput, particularly under bursty or deceptive low-CPU workloads where heuristics fail to detect overload. Furthermore, the DRL agent consistently optimizes resource utilization, reducing unnecessary replica scaling while maintaining SLA targets. The system's reward function jointly balances throughput maximization, latency reduction, and cost efficiency, providing a robust alternative to traditional methods.
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GreenNFV: Energy-Efficient Network Function Virtualization with Service Level Agreement Constraints
Network Function Virtualization (NFV) platforms consume significant energy, introducing high operational costs in edge and data centers. This paper presents a novel framework called GreenNFV that optimizes resource usage for network function chains using deep reinforcement learning. GreenNFV optimizes resource parameters such as CPU sharing ratio, CPU frequency scaling, last-level cache (LLC) allocation, DMA buffer size, and packet batch size. GreenNFV learns the resource scheduling model from the benchmark experiments and takes Service Level Agreements (SLAs) into account to optimize resource usage models based on the different throughput and energy consumption requirements. Our evaluation shows that GreenNFV models achieve high transfer throughput and low energy consumption while satisfying various SLA constraints. Specifically, GreenNFV with Throughput SLA can achieve 4.4× higher throughput and 1.5× better energy efficiency over the baseline settings, whereas GreenNFV with Energy SLA can achieve 3× higher throughput while reducing energy consumption by 50%.
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- Award ID(s):
- 2007829
- PAR ID:
- 10565485
- Publisher / Repository:
- ACM
- Date Published:
- ISBN:
- 9798400701092
- Page Range / eLocation ID:
- 1 to 12
- Subject(s) / Keyword(s):
- Network function virtualization energy efficiency performance service level agreements deep reinforcement learning.
- Format(s):
- Medium: X
- Location:
- Denver CO USA
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Conference Paper:
- https://doi.org/10.1145/3581784.3607090
