Intelligent Workload Scheduling for Telecom Cloud Architecture Using Reinforcement Learning

Abstract

Scheduling based on the workload is very important in the telecom networks of the modern age to maximize the resource allocation, reduce the latency, and to make sure that cloud based infrastructures establish their smooth functioning. The conventional ways of scheduling are not very efficient and do not keep pace with the dynamic character of the telecom cloud environment. To overcome these issues, this paper will Hypothesize an innovative workload scheduling method based on the use of Reinforcement Learning (RL). The framework proposed is an agent that is based on RL and which will schedule workloads dynamically depending on real-time system parameters like resource availability, network traffic, and workload demand. With the help of Q-learning, the agent becomes knowledgeable of the best scheduling policies that would be able to utilize resources without causing service interruptions. The framework consists of several elements: an environment model of the telecom cloud infrastructure, a rewarding mechanism that measures the performance of the system and a learning mechanism that modifies the decisions concerning scheduling. Experimental evidence supports the efficacy of the RL based approach over the traditional approaches in the aspect of resource utilization, response time, and system throughput. The suggested approach is scalable and can be implemented in both large-scale telecom cloud architectures with different workloads and resource settings. The study reveals the promise of RL in creating intelligent workloads scheduling in telecom clouds and forms the basis of future studies in the adaptive autonomous cloud management systems.