Automata Approach to Reduce Power Consumption in Smart Grid Cloud Data Center

2018 ◽  
pp. 248-257
Author(s):  
J. Usha ◽  
S. R. Jayasimha ◽  
S. G. Srivani
Keyword(s):  
Smart Grid ◽  
Power Consumption ◽  
Data Center ◽  
Cloud Data Center ◽  
Reduce Power Consumption ◽  
Cloud Data

scholarly journals Efficient Resource Utilization Based on Energy Management in Cloud Data Center

2019 ◽  
Vol 8 (6) ◽  
pp. 340-344
Keyword(s):  
Power Consumption ◽  
Resource Utilization ◽  
Data Center ◽  
Vital Role ◽  
Cloud Data Center ◽  
Cloud Data ◽  
Network Bandwidth ◽  
Efficient Resource ◽  
Novel Method ◽  
Dynamic Resource

Efficient resource utilization plays a vital role in cloud computing since the shared computational power of the resources is offered on demand. During dynamic resource allocation sometimes a server may be over utilized or underutilized thus leading to excess of energy consumption in the data centers. So the proposed system calculates the over utilization and underutilization of a CPU and RAM usage and also considers the network bandwidth usage to reduce power consumption in the cloud data center. Hence, a novel method is used for minimizing power consumption in the data center

Power Consumption-Aware Virtual Machine Placement in Cloud Data Center

2017 ◽  
Vol 1 (4) ◽  
pp. 541-550 ◽  
Author(s):  
Giuseppe Portaluri ◽  
Davide Adami ◽  
Andrea Gabbrielli ◽  
Stefano Giordano ◽  
Michele Pagano
Keyword(s):  
Power Consumption ◽  
Virtual Machine ◽  
Data Center ◽  
Virtual Machine Placement ◽  
Cloud Data Center ◽  
Cloud Data

Virtual Machine Placement Strategy for Cloud Data Center

2015 ◽  
pp. 261-287
Author(s):  
Sourav Kanti Addya ◽  
Bibhudutta Sahoo ◽  
Ashok Kumar Turuk
Keyword(s):  
Power Consumption ◽  
Virtual Machine ◽  
Data Center ◽  
Bin Packing ◽  
Virtual Machine Placement ◽  
Vm Placement ◽  
Cloud Data Center ◽  
Placement Problem ◽  
Cloud Data ◽  
Minimum Power Consumption

The data center is the physical infrastructure layer in cloud architecture. To run a large data center requires a huge amount of power. A proper strategy can minimize the number of servers used. Minimization of active servers caused minimization of power consumption. But the maximum number of virtual machine placement will be a monetary benefit for cloud service providers. To earn maximum revenue, the CSP is to maximize resource utilization. VM placement is one of the major issues to achieve minimum power consumption as well as to earn maximum revenue by CSP. In this research chapter, we have formulated an optimization problem for initial VM placement in the data center. An iterative heuristic using simulated annealing has been used for VM placement problem. The proposed heuristic has been analysis to be scalable and the coding scheme shows that the proposed technique is outperforming traditional FFD on bin packing technique.

Virtual Machine Placement Strategy for Cloud Data Center

2016 ◽  
pp. 783-808
Author(s):  
Sourav Kanti Addya ◽  
Bibhudatta Sahoo ◽  
Ashok Kumar Turuk
Keyword(s):  
Power Consumption ◽  
Virtual Machine ◽  
Data Center ◽  
Bin Packing ◽  
Virtual Machine Placement ◽  
Vm Placement ◽  
Cloud Data Center ◽  
Placement Problem ◽  
Cloud Data ◽  
Minimum Power Consumption

The data center is the physical infrastructure layer in cloud architecture. To run a large data center requires a huge amount of power. A proper strategy can minimize the number of servers used. Minimization of active servers caused minimization of power consumption. But the maximum number of virtual machine placement will be a monetary benefit for cloud service providers. To earn maximum revenue, the CSP is to maximize resource utilization. VM placement is one of the major issues to achieve minimum power consumption as well as to earn maximum revenue by CSP. In this research chapter, we have formulated an optimization problem for initial VM placement in the data center. An iterative heuristic using simulated annealing has been used for VM placement problem. The proposed heuristic has been analysis to be scalable and the coding scheme shows that the proposed technique is outperforming traditional FFD on bin packing technique.

Request–Response Distributed Power Management in Cloud Data Centers

2013 ◽  
Vol 22 (4) ◽  
pp. 437-451
Author(s):  
Jianxiang Li ◽  
Youchun Zhang
Keyword(s):  
Power Consumption ◽  
Power Management ◽  
Data Center ◽  
Linear Time ◽  
Service Level Agreement ◽  
Centralized Control ◽  
Virtual Machine Migration ◽  
Cloud Data Center ◽  
Distributed Power ◽  
Cloud Data

AbstractPower provision is coming to be the most important constraint to data center development. The efficient management of power consumption according to the loads of the data center is urgent. As the load for every application hosted in every server node (SN) of the data center and corresponding Service Level Agreement (SLA) requirements can be quite different, it is hard to deploy a power strategy at application. The asynchronies and abruptness characteristics of workload fluctuation make power management policymaking using periodic resource scheduling method invalid. In this article, the design and implementation of the request–response distributed power management scheme is elaborated. Bound by linear time complexity, the method proposed integrates dynamic voltage/frequency scaling, power-on–power-off, and virtual machine migration mechanisms and dynamically optimizes the power consumption of a cloud data center. The significant advantage of the scheme is that it does not need synchronous scheduling between all SNs. Simulation results showed that the scheme could effectively decrease the power consumption of the data center, with a tiny reduction in performance as centralized control methods.

Sign in / Sign up

Export Citation Format

Share Document