Machine learning compliance-aware dynamic software allocation for energy, cost and resource-efficient cloud environment

Abstract

With the growing number of cloud services protected by licenses, compliance management and assurance is becoming critical need to support the development of trustworthy cloud systems. In these systems, the multiplication of services and the inefficient resource utilization incurred energy consumption and costs increase despite the consolidation initiatives underway. Few works deal with resource allocation optimization at the SaaS level, which does not consider compliance aspects. Generally, the reported consolidation work does not address license management in the cloud environment as a whole, particularly from a resource management perspective, and the vast majority of consolidation work focuses on resource optimization at the infrastructure level. Thus, we propose a software license consolidation scheme based on multi-objective reinforcement learning that enables efficient use of resources and optimizes energy consumption, resource wastage, and costs while ensuring compliance with the processor-based licensing model. The experimental results show that our solution outperforms the baseline approaches in different scenarios with homogeneous and heterogeneous resources under different data center scales.