PowerGen: Resources Uitlization and Power Consumption Data Generation Framework for Energy Prediction in Edge and Cloud Computing - Code and Dataset are available
The explosive adoption of IoT applications in different domains, such as healthcare, transportation, and smart home and industry, has led to the pervasive adoption of edge and cloud computing. Large-scale edge and cloud data centers, consisting of thousands of computing servers, are hungry-energy infrastructure exacerbating issues such as environmental carbon footprint and high electricity costs. Developing energy-efficient solutions for cloud infrastructure requires knowledge of the correlation between computing server resource utilization and power consumption. Power consumption modeling exhibits this relationship and is crucial for energy savings. In this paper, we propose PowerGen, a framework to generate server resources utilization and corresponding power consumption dataset. The proposed framework will aid academic researchers to formulate correlations between resources utilization and power consumption by using power prediction models, and evaluate energy-aware resource management approaches in an edge-cloud computing system. It will help edge and cloud administrators to evaluate the energy-efficiency of heterogenous severs architectures in a datacenter. We exemplify the applicability of the dataset, generated by our proposed framework, in power prediction modeling and energy-aware scheduling for green computing scenarios.
The major conrributions of this work are as follows:
- At the cutting edge, we propose an innovative power data generation framework that harnesses the power of tools, benchmarks, IoT, and big data applications to provide cloud computing data centers with the capability of power modeling to predict server’s electricity consumption. This system is designed with green computing in mind while scheduling IoT in cloud data centers.
- We employ different benchmarks to stress the servers in the proposed framework.
- We exemplify the applicability of our proposed framework using two use cases: power consumption modeling and energy-aware scheduling.
Ismail, Leila, and Huned Materwala. "PowerGen: Resources Utilization and Power Consumption Data Generation Framework for Energy Prediction in Edge and Cloud Computing." Procedia Computer Science 238 (2024): 385-395.
