Cost Efficient Resource Allocation in Cloud Environment

Abstract

Cloud computing has transformed IT infrastructure by offering scalable, pay-as-you-go resources. However, efficiently allocating cloud resources under dynamic workloads while minimizing operational costs and maintaining service-level agreements (SLAs) remains a critical challenge. This paper presents a hybrid AI-driven framework for cost-efficient resource allocation in cloud environments. The proposed model integrates Long Short-Term Memory (LSTM) networks for workload forecasting, reinforcement learning (RL) for dynamic decision-making, heuristic scheduling (inspired by ACO and GA) for optimal task assignments, and economic pricing using the ERA model. A real-world data set from Google Cluster Trace is used to validate the framework. Simulation results show a forecasted CPU utilization of 10.79% with an estimated cost of ₹1.08 and one SLA violation, demonstrating the potential of hybrid AI models for real-time and cost-aware resource provisioning.