Two-Phase Industrial Manufacturing Service Management for Energy Efficiency of Data Centers

Data-driven industrial manufacturing services are proliferating. They use large amounts of data generated from Industrial-Internet-of-Things (IIoT) devices for intelligent to end-service-users. However, cloud centers hosting these consume a huge amount energy, resulting in high operational cost. To address this issue, an energy-efficient resource allocation framework is proposed article services. It operates two phases. First, multithreshold-based host CPU utilization classification scheme developed classify hosts into four groups improved allocation. designed through analyzing by using the least median squares regression technique. Thereby, limits search space, thus reducing time complexity. In second phase, with metaheuristic search, energy- and thermal-aware method find allocating resources From real center workload traces, extensive experiments show that our outperforms existing baseline approaches 6.9%, 33.75%, 34.1% on average terms temperature, energy consumption, service-level-agreement violation, respectively.