Search for: All records

Effective resource orchestration for network slicing is critical for optimizing the performance of diverse applications running on next generation communication networks. This paper presents a novel approach that leverages advancements in multi-agent reinforcement learning (MARL) to adaptively learn the resource requirements of various applications in network slices and orchestrate resources in real-time. Our proposed MARL-based orchestration scheme aims to balance the varying requirements of individual network slices, ensuring optimal performance amid dynamic application deployments with limited network information. Simulation results and comparative analyses validate the efficiency and efficacy of our methodology, demonstrating its superiority over traditional methods in terms of system performance and resource utilization. Simulation results indicate that our strategy significantly enhances system utility and efficiency, particularly with limited resources. 

Modern fifth-generation (5G) networks are increasingly moving towards architectures characterized by softwarization and virtualization. This paper addresses the complexities and challenges in deploying applications and services in the emerging multi-tiered 5G network architecture, particularly in the context of microservices-based applications. These applications, characterized by their structure as directed graphs of interdependent functions, are sensitive to the deployment tiers and resource allocation strategies, which can result in performance degradation and susceptibility to failures. Additionally, the threat of deploying potentially malicious applications exacerbates resource allocation inefficiencies. To address these issues, we propose a novel optimization framework that incorporates a probabilistic approach for assessing the risk of malicious applications, leading to a more resilient resource allocation strategy. Our framework dynamically optimizes both computational and networking resources across various tiers, aiming to enhance key performance metrics such as latency, accuracy, and resource utilization. Through detailed simulations, we demonstrate that our framework not only satisfies strict performance requirements but also surpasses existing methods in efficiency and security.