scholarly journals Speculation based Decision Support System for Efficient Resource Provisioning in Cloud Data Center

2017 ◽  
Vol 10 (1) ◽  
pp. 363 ◽  
Author(s):  
R. Leena Sri ◽  
N. Balaji
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Data Center ◽  
Resource Provisioning ◽  
Cloud Data Center ◽  
Cloud Data ◽  
Efficient Resource

Power Aware Meta Scheduler for Adaptive VM Provisioning in IaaS Cloud

2013 ◽  
pp. 190-204
Author(s):  
R. Jeyarani ◽  
N. Nagaveni ◽  
Satish Kumar Sadasivam ◽  
Vasanth Ram Rajarathinam
Keyword(s):  
Data Center ◽  
Power Saving ◽  
Resource Provisioning ◽  
Configurable Computing ◽  
Cloud Data Center ◽  
Workload Prediction ◽  
Cloud Data ◽  
Wise Decision ◽  
Microprocessor Technology ◽  
Lifecycle Management

Cloud Computing provides on-demand access to a shared pool of configurable computing resources. The major issue lies in managing extremely large agile data centers which are generally over provisioned to handle unexpected workload surges. This paper focuses on green computing by introducing Power-Aware Meta Scheduler, which provides right fit infrastructure for launching virtual machines onto host. The major challenge of the scheduler is to make a wise decision in transitioning state of the processor cores by exploiting various power saving states inherent in the recent microprocessor technology. This is done by dynamically predicting the utilization of the cloud data center. The authors have extended existing cloudsim toolkit to model power aware resource provisioning, which includes generation of dynamic workload patterns, workload prediction and adaptive provisioning, dynamic lifecycle management of random workload, and implementation of power aware allocation policies and chip aware VM scheduler. The experimental results show that the appropriate usage of different power saving states guarantees significant energy conservation in handling stochastic nature of workload without compromising the performance, both when the data center is in low as well as moderate utilization.

Minimization of VM Placement Change in Energy-Aware Resource Provisioning for Cloud Data Center

2013 ◽  
Vol 325-326 ◽  
pp. 1730-1733 ◽  
Author(s):  
Si Yuan Jing ◽  
Shahzad Ali ◽  
Kun She
Keyword(s):  
Energy Consumption ◽  
Data Center ◽  
Network Flow ◽  
Resource Provisioning ◽  
Approximate Algorithm ◽  
Vm Placement ◽  
Energy Aware ◽  
Cloud Data Center ◽  
Cloud Data ◽  
Placement Change

Numerous part of the energy-aware resource provision research for cloud data center just considers how to maximize the resource utilization, i.e. minimize the required servers, without considering the overhead of a virtual machine (abbreviated as a VM) placement change. In this work, we propose a new method to minimize the energy consumption and VM placement change at the same time, moreover we also design a network-flow-theory based approximate algorithm to solve it. The simulation results show that, compared to existing work, the proposed method can slightly decrease the energy consumption but greatly decrease the number of VM placement change

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 Aware Meta Scheduler for Adaptive VM Provisioning in IaaS Cloud

2011 ◽  
Vol 1 (3) ◽  
pp. 36-51 ◽  
Author(s):  
R. Jeyarani ◽  
N. Nagaveni ◽  
Satish Kumar Sadasivam ◽  
Vasanth Ram Rajarathinam
Keyword(s):  
Data Center ◽  
Virtual Machines ◽  
Power Saving ◽  
Resource Provisioning ◽  
Configurable Computing ◽  
Cloud Data Center ◽  
Workload Prediction ◽  
Cloud Data ◽  
Wise Decision ◽  
Microprocessor Technology

Cloud Computing provides on-demand access to a shared pool of configurable computing resources. The major issue lies in managing extremely large agile data centers which are generally over provisioned to handle unexpected workload surges. This paper focuses on green computing by introducing Power-Aware Meta Scheduler, which provides right fit infrastructure for launching virtual machines onto host. The major challenge of the scheduler is to make a wise decision in transitioning state of the processor cores by exploiting various power saving states inherent in the recent microprocessor technology. This is done by dynamically predicting the utilization of the cloud data center. The authors have extended existing cloudsim toolkit to model power aware resource provisioning, which includes generation of dynamic workload patterns, workload prediction and adaptive provisioning, dynamic lifecycle management of random workload, and implementation of power aware allocation policies and chip aware VM scheduler. The experimental results show that the appropriate usage of different power saving states guarantees significant energy conservation in handling stochastic nature of workload without compromising the performance, both when the data center is in low as well as moderate utilization.

An Energy-Efficient Resource Scheduling Algorithm for Cloud Computing based on Resource Equivalence Optimization

2016 ◽  
Vol 8 (2) ◽  
pp. 43-57 ◽  
Author(s):  
Li Mao ◽  
De Yu Qi ◽  
Wei Wei Lin ◽  
Bo Liu ◽  
Ye Da Li
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Energy Consumption ◽  
Data Center ◽  
Energy Efficient ◽  
Resource Scheduling ◽  
Constraint Satisfaction Problems ◽  
Cloud Data Center ◽  
Cloud Data ◽  
Efficient Resource

With the rapid growth of energy consumption in global data centers and IT systems, energy optimization has become an important issue to be solved in cloud data center. By introducing heterogeneous energy constraints of heterogeneous physical servers in cloud computing, an energy-efficient resource scheduling model for heterogeneous physical servers based on constraint satisfaction problems is presented. The method of model solving based on resource equivalence optimization is proposed, in which the resources in the same class are pruning treatment when allocating resource so as to reduce the solution space of the resource allocation model and speed up the model solution. Experimental results show that, compared with DynamicPower and MinPM, the proposed algorithm (EqPower) not only improves the performance of resource allocation, but also reduces energy consumption of cloud data center.

Application-Aware Dynamic Fine-Grained Resource Provisioning in a Virtualized Cloud Data Center

2017 ◽  
Vol 14 (2) ◽  
pp. 1172-1184 ◽  
Author(s):  
Jing Bi ◽  
Haitao Yuan ◽  
Wei Tan ◽  
MengChu Zhou ◽  
Yushun Fan ◽  
...  
Keyword(s):  
Data Center ◽  
Resource Provisioning ◽  
Cloud Data Center ◽  
Cloud Data ◽  
Fine Grained ◽  
Application Aware

An energy-aware resource provisioning scheme for real-time applications in a cloud data center

2018 ◽  
Author(s):  
Hamid Reza Faragardi ◽  
Saeid Dehnavi ◽  
Thomas Nolte ◽  
Mehdi Kargahi ◽  
Thomas Fahringer
Keyword(s):  
Real Time ◽  
Data Center ◽  
Resource Provisioning ◽  
Energy Aware ◽  
Cloud Data Center ◽  
Cloud Data ◽  
Real Time Applications

scholarly journals AN ANALYSIS AND COMPARISON OF CLOUD DATA CENTER ENERGY-EFFICIENT RESOURCE MANAGEMENT TECHNOLOGY

2014 ◽  
Vol 2 (4) ◽  
pp. 32-51 ◽  
Author(s):  
Zhihui Lu ◽  
◽  
Soichi Takashige ◽  
Yumiko Sugita ◽  
Tomohiro Morimura ◽  
...  
Keyword(s):  
Resource Management ◽  
Data Center ◽  
Energy Efficient ◽  
Cloud Data Center ◽  
Cloud Data ◽  
Management Technology ◽  
Efficient Resource

The Greening of Data Centers with Cloud Technology

2015 ◽  
Vol 5 (4) ◽  
pp. 1-23 ◽  
Author(s):  
Michael J. Pawlish ◽  
Aparna S. Varde ◽  
Stefan A. Robila
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Data Center ◽  
Data Centers ◽  
Efficient Solutions ◽  
Cloud Services ◽  
Case Based Reasoning ◽  
Related Factors ◽  
Cloud Technology

This article presents a decision support system to provide green or energy efficient solutions for data centers that maintain computers and peripherals to serve organizations. Traditionally, data centers catered to all operations using in-house servers. Cloud technology provides alternatives to outsource operations heading towards greenness. However, using cloud services for all data center operations may have its pitfalls. In this paper, the authors analyze various data center parameters such as carbon footprint and power usage effectiveness along with cloud-based and server-based models. They consider data mining techniques of decision trees and case based reasoning in their work. Among other findings, they head towards a hybrid model that meets the demands of productivity, energy efficiency and related factors. These findings lead to the development of the decision support system. The authors describe the research, development and evaluation of the system. They conclude with important outcomes deployed in real-world scenarios in data center management.

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