Thermal-Aware Hybrid Workload Management in a Green Datacenter towards Renewable Energy Utilization

The increase in massive data processing and computing in datacenters in recent years has resulted in the problem of severe energy consumption, which also leads to a significant carbon footprint and a negative impact on the environment. A growing number of IT companies with operating datacenters are...

Full description

Saved in:
Bibliographic Details
Published in:Energies (Basel) 2019-04, Vol.12 (8), p.1494
Main Authors: Li, Yuling, Wang, Xiaoying, Luo, Peicong, Pan, Qingyi
Format: Article
Language:English
Subjects:
Algorithms
Alternative energy sources
Back propagation
Clean energy
Cloud computing
Computer centers
Computers
Cooling
Data centers
Electricity
Employment
Energy conservation
Energy consumption
Energy management
Energy utilization
Environmental effects
green datacenter
Historical account
Load
Long short-term memory
Maximum power
Methods
Microprocessors
Neural networks
Optimization techniques
Power consumption
Quality of service
Recurrent neural networks
renewable energy
Renewable energy sources
Renewable resources
Scheduling
Servers
Solar energy
Solar power
Temperature
thermal-aware
Virtual environments
Working conditions
Workload
workload management
Workloads
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The increase in massive data processing and computing in datacenters in recent years has resulted in the problem of severe energy consumption, which also leads to a significant carbon footprint and a negative impact on the environment. A growing number of IT companies with operating datacenters are adopting renewable energy as part of their energy supply to offset the consumption of brown energy. In this paper, we focused on a green datacenter using hybrid energy supply, leveraged the time flexibility of workloads in the datacenter, and proposed a thermal-aware workload management method to maximize the utilization of renewable energy sources, considering the power consumption of both computing devices and cooling devices at the same time. The critical knob of our approach was workload shifting, which scheduled more delay-tolerant workloads and allocated resources in the datacenter according to the availability of renewable energy supply and the variation of cooling temperature. In order to evaluate the performance of the proposed method, we conducted simulation experiments using the Cloudsim-plus tool. The results demonstrated that the proposed method could effectively reduce the consumption of brown energy while maximizing the utilization of green energy.
ISSN:1996-1073
1996-1073
DOI:10.3390/en12081494