Reinforcement learning based methodology for energy-efficient resource allocation in cloud data centers

Thein, Thandar; Myo, Myint Myat; Parvin, Salma; Gawanmeh, Amjad

doi:10.1016/j.jksuci.2018.11.005

Cited by 38 publications

(22 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Studies using RL in this field usually refer to states as calibrated PMs load state, rewards, but unlike previous study, this one defines actions as moving out or migrating any specific VM and also adds a transition probabilities (P). Again, on the basic of reinforcement learning mechanism and fuzzy logic for green solutions [92], each host state in this study has four SN Computer Science dimensions: the current energy consumption of DC, candidate host, utilization of candidate host in MIPS, and then CPU demand of VM in MIPS. Possible actions were either to allocate or not allocate.…”

Section: Supervised and Unsupervised Machine Learning Techniquesmentioning

confidence: 99%

A Survey on the Current Challenges of Energy-Efficient Cloud Resources Management

et al. 2020

View full text Add to dashboard Cite

Given the perpetual surging of cloud services' requests, energy consumption of cloud data centers with their related CO 2 emissions still represents major issues. Efficient use of cloud's resources becomes then the driven force ensuring both, the satisfaction of service-level agreements and the sobriety of cloud's energy consumption. This paper purports to survey for the first time, a comprehensive literature of some actual challenges facing various cloud's resources utilization scheduling approaches dealing with energy conservation. Indeed, cloud resources involve not only computing servers, but also a wide set of intra-and inter-cloud network's resources to be considered. These resources are mainly provisioned through two wellknown technologies: virtualization and/or containerization. As existing studies in the area of cloud resources provisioning are generally categorized into different groups, this research depicts a complete taxonomy of energy-efficient cloud resources scheduling. In this same perspective, we survey some recent efforts made in energy-efficient virtual and containerized resources, by emphasizing first the most used reactive then proactive techniques for managing the whole cloud resources energy efficiency scheduling.

show abstract

Section: Supervised and Unsupervised Machine Learning Techniquesmentioning

confidence: 99%

A Survey on the Current Challenges of Energy-Efficient Cloud Resources Management

et al. 2020

View full text Add to dashboard Cite

show abstract

“…(b) power management of data center systems [23]; (c) thermal management and approaches [24], [25] and (d) power consumption management in virtual fields [26], [27]. Moreover, a few researchers projected power efficiency or cost-effectiveness provisioning techniques for workloads [28], [29].…”

Section: Related Workmentioning

confidence: 99%

PEMC: Power Efficiency Measurement Calculator to Compute Power Efficiency and CO₂ Emissions in Cloud Data Centers

et al. 2020

View full text Add to dashboard Cite

The power consumption of cloud data centers has a considerable impact on the environment and climate change nowadays. Researchers are seeking to find practical solutions to reduce power consumption in data centers while guaranteeing the desired level of services and service level objectives. With the establishment of the data center industry, the demand for computation and data storage has been continually rising. Energy efficiency is one of the most significant issues faced by these big data centers to meet such high computational requirements. There are many industry acceptable metrics available such as PUE, DCiE, DCP, etc. Power Usage Effectiveness (PUE) metric has proven to be the most popular in measuring energy efficiency; however, it measures the power efficiency alone with no consideration for CO 2 emissions and the costs involved in total power usage across data centers. In this paper, we proposed a novel Power Efficiency Measurement Calculator (PEMC) that combines and calculates the power efficiency, CO 2 emissions, and the total annual costs incurred. The pseudocode and algorithm to perform these specific PUE, DCiE, and CO 2 emission functions are given to explain the working of proposed work. Finally, the proposed PEMC calculator was tested and validated through a case study performed in one of the tierlevel data centers in Malaysia and the results demonstrate its effectiveness compared with Power Usage Effectiveness (PUE) and other known calculators.

show abstract

“…Numerous studies have explained 'non-stationary' or 'dynamic' demand response using different algorithms [12,36,[77][78][79][80]. Most of these studies are simulation based.…”

Section: Types Of Demand Response and Its Potentialitymentioning

confidence: 99%

Energy-saving behaviour as a demand-side management strategy in the developing world: the case of Bangladesh

Khan

2019

Int J Energy Environ Eng

View full text Add to dashboard Cite

Although demand-side management (DSM) needs to be more customer centred, either with or without smart technologies (e.g. smart grid), less attention has been paid to the developing world in relation to DSM strategy development. The main reasons have been lack of appropriate technology and capital costs. Importantly, there are alternative DSM strategies that require minimum or no cost to implement and provide immediate results, of which energy-saving behaviour of the occupants at residences is one. This study explores the potentiality of this energy-saving behaviour as a DSM strategy for the least developed economies, focusing particularly on Bangladesh. The literature suggests that energy-saving behaviour could reduce energy demand by a maximum of 21.9%. However, this potential DSM scheme seems underestimated in the national DSM programme of Bangladesh. The Energy Efficiency and Conservation Master Plan (EECMP) of Bangladesh (a DSM program) shows that efficiency improvement in the use of home appliances could reduce electricity demand in the residential sector by about 28.8%, but this does require a long time to be implemented, whereas the inclusion of energy-saving behaviour as a demand response strategy in residences along with the EECMP might achieve demand reduction of up to 50.7%. Although the findings from this study are specific to Bangladesh, these could be useful guidelines for the policymakers of other developing nations where national DSM strategy development is underway.

show abstract

Reinforcement learning based methodology for energy-efficient resource allocation in cloud data centers

Cited by 38 publications

References 24 publications

A Survey on the Current Challenges of Energy-Efficient Cloud Resources Management

A Survey on the Current Challenges of Energy-Efficient Cloud Resources Management

PEMC: Power Efficiency Measurement Calculator to Compute Power Efficiency and CO₂ Emissions in Cloud Data Centers

Energy-saving behaviour as a demand-side management strategy in the developing world: the case of Bangladesh

Contact Info

Product

Resources

About