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

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

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.

scholarly journals Task Classification Based Energy-Aware Consolidation in Clouds

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
HeeSeok Choi ◽  
JongBeom Lim ◽  
Heonchang Yu ◽  
EunYoung Lee
Keyword(s):  
Resource Utilization ◽  
Data Center ◽  
Virtual Machines ◽  
Service Level Agreement ◽  
Energy Aware ◽  
Cloud Data Center ◽  
Cloud Data ◽  
Balance Task ◽  
Task Classification ◽  
Vm Consolidation

We consider a cloud data center, in which the service provider supplies virtual machines (VMs) on hosts or physical machines (PMs) to its subscribers for computation in an on-demand fashion. For the cloud data center, we propose a task consolidation algorithm based on task classification (i.e., computation-intensive and data-intensive) and resource utilization (e.g., CPU and RAM). Furthermore, we design a VM consolidation algorithm to balance task execution time and energy consumption without violating a predefined service level agreement (SLA). Unlike the existing research on VM consolidation or scheduling that applies none or single threshold schemes, we focus on a double threshold (upper and lower) scheme, which is used for VM consolidation. More specifically, when a host operates with resource utilization below the lower threshold, all the VMs on the host will be scheduled to be migrated to other hosts and then the host will be powered down, while when a host operates with resource utilization above the upper threshold, a VM will be migrated to avoid using 100% of resource utilization. Based on experimental performance evaluations with real-world traces, we prove that our task classification based energy-aware consolidation algorithm (TCEA) achieves a significant energy reduction without incurring predefined SLA violations.

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.

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
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