Combined use of coral reefs optimization and multi-agent deep Q-network for energy-aware resource provisioning in cloud data centers using DVFS technique

2021 ◽  
Author(s):  
Ali Asghari ◽  
Mohammad Karim Sohrabi
Keyword(s):  
Coral Reefs ◽  
Data Centers ◽  
Resource Provisioning ◽  
Energy Aware ◽  
Cloud Data ◽  
Combined Use ◽  
Multi Agent ◽  
Cloud Data Centers

scholarly journals Energy aware dynamic virtual machine consolidation in cloud data centers

2018 ◽  
Vol 7 (2.8) ◽  
pp. 550 ◽  
Author(s):  
G Anusha ◽  
P Supraja
Keyword(s):  
Energy Consumption ◽  
Virtual Machine ◽  
Data Centers ◽  
Energy Aware ◽  
Large Power ◽  
Complex Tasks ◽  
Cloud Data ◽  
Network Bandwidth ◽  
Virtual Machine Consolidation ◽  
Cloud Data Centers

Cloud computing is a growing technology now-a-days, which provides various resources to perform complex tasks. These complex tasks can be performed with the help of datacenters. Data centers helps the incoming tasks by providing various resources like CPU, storage, network, bandwidth and memory, which has resulted in the increase of the total number of datacenters in the world. These data centers consume large volume of energy for performing the operations and which leads to high operation costs. Resources are the key cause for the power consumption in data centers along with the air and cooling systems. Energy consumption in data centers is comparative to the resource usage. Excessive amount of energy consumption by datacenters falls out in large power bills. There is a necessity to increase the energy efficiency of such data centers. We have proposed an Energy aware dynamic virtual machine consolidation (EADVMC) model which focuses on pm selection, vm selection, vm placement phases, which results in the reduced energy consumption and the Quality of service (QoS) to a considerable level.

Energy-aware virtual machine allocation and selection in cloud data centers

2017 ◽  
Vol 23 (6) ◽  
pp. 1917-1932 ◽  
Author(s):  
V. Dinesh Reddy ◽  
G. R. Gangadharan ◽  
G. Subrahmanya V. R. K. Rao
Keyword(s):  
Virtual Machine ◽  
Data Centers ◽  
Energy Aware ◽  
Cloud Data ◽  
Cloud Data Centers ◽  
Machine Allocation ◽  
Virtual Machine Allocation

scholarly journals An Distributed Virtual Machine Placement Algorithm for Balanced Resource Utilization and Low Energy Consumption

2018 ◽  
Vol 173 ◽  
pp. 03092
Author(s):  
Bo Li ◽  
Yun Wang
Keyword(s):  
Virtual Machine ◽  
Data Centers ◽  
Virtual Machine Placement ◽  
Vm Placement ◽  
Energy Aware ◽  
Cloud Data ◽  
Low Energy Consumption ◽  
Cloud Data Centers ◽  
Placement Algorithm ◽  
Heterogeneous Cloud

Virtual machine placement is the process of selecting the most suitable server in large cloud data centers to deploy newly-created VMs. Traditional load balancing or energy-aware VM placement approaches either allocate VMs to PMs in centralized manner or ignore PM’s cost-capacity ratio to implement energy-aware VM placement. We address these two issues by introducing a distributed VM placement approach. A auction-based VM placement algorithm is devised for help VM to find the most suitable server in large heterogeneous cloud data centers. Our algorithm is evaluated by simulation. Experimental results show two major improvements over the existing approaches for VM placement. First, our algorithm efficiently balances the utilization of multiple types of resource by minimizing the amount of physical servers used. Second, it reduces system cost compared with existing approaches in heterogeneous environment.

Energy-Aware Fault-Tolerant Dynamic Task Scheduling Scheme for Virtualized Cloud Data Centers

2018 ◽  
Vol 24 (3) ◽  
pp. 1063-1077 ◽  
Author(s):  
Avinab Marahatta ◽  
Youshi Wang ◽  
Fa Zhang ◽  
Arun Kumar Sangaiah ◽  
Sumarga Kumar Sah Tyagi ◽  
...  
Keyword(s):  
Task Scheduling ◽  
Data Centers ◽  
Fault Tolerant ◽  
Energy Aware ◽  
Cloud Data ◽  
Dynamic Task ◽  
Scheduling Scheme ◽  
Cloud Data Centers ◽  
Dynamic Task Scheduling
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