Adaptive Markov‐based approach for dynamic virtual machine consolidation in cloud data centers with quality‐of‐service constraints

2019 ◽  
Vol 50 (2) ◽  
pp. 161-183
Author(s):  
Hossein Monshizadeh Naeen ◽  
Esmaeil Zeinali ◽  
Abolfazl Toroghi Haghighat
Keyword(s):  
Quality Of Service ◽  
Virtual Machine ◽  
Data Centers ◽  
Cloud Data ◽  
Virtual Machine Consolidation ◽  
Cloud Data Centers ◽  
Service Constraints

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-Efficient Virtualized Scheduling and Load Balancing Algorithm in Cloud Data Centers

2021 ◽  
Vol 11 (3) ◽  
pp. 34-48
Author(s):  
J. K. Jeevitha ◽  
Athisha G.
Keyword(s):  
Quality Of Service ◽  
Load Balancing ◽  
Energy Efficient ◽  
Data Centers ◽  
Scheduling Algorithm ◽  
Cloud Data ◽  
The Third ◽  
Cloud Data Centers ◽  
Load Balancing Algorithm

To scale back the energy consumption, this paper proposed three algorithms: The first one is identifying the load balancing factors and redistribute the load. The second one is finding out the most suitable server to assigning the task to the server, achieved by most efficient first fit algorithm (MEFFA), and the third algorithm is processing the task in the server in an efficient way by energy efficient virtual round robin (EEVRR) scheduling algorithm with FAT tree topology architecture. This EEVRR algorithm improves the quality of service via sending the task scheduling performance and cutting the delay in cloud data centers. It increases the energy efficiency by achieving the quality of service (QOS).

Energy-efficient framework for virtual machine consolidation in cloud data centers

2017 ◽  
Vol 14 (10) ◽  
pp. 192-201 ◽  
Author(s):  
Kejing He ◽  
Zhibo Li ◽  
Dongyan Deng ◽  
Yanhua Chen
Keyword(s):  
Virtual Machine ◽  
Energy Efficient ◽  
Data Centers ◽  
Cloud Data ◽  
Virtual Machine Consolidation ◽  
Cloud Data Centers

scholarly journals Service Quality Assurance in Cloud Data Centers Using Migration Scaling

2020 ◽  
Vol 02 (01) ◽  
pp. 53-63
Author(s):  
Dr. Akey Sungheetha ◽  
Dr. Rajesh Sharma R
Keyword(s):  
Quality Of Service ◽  
Service Quality ◽  
User Satisfaction ◽  
Data Centers ◽  
Vital Role ◽  
Cloud Data ◽  
Similar Work ◽  
Cloud Data Centers ◽  
Multiple Metrics

The continuous and swift progress in the number of the cloud data centers have led to establishment of multitudes of the computational nodes and the huge paradigm. But the assuring the quality of services through these paradigms is still questionable. So tit has become a prominent areas of research. As the quality of service of the data centers plays a vital role in the user satisfaction. The present work carried out in the paper survey the service quality rendered in the previous similar work, identifies the drawbacks and proposes a strategy of migration taking into consideration the multiple metrics. The proposed structure is validated through the cloud simulator to evince its capability in efficiently handling the resources and guaranteeing the quality of service.

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