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

Abstract

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