scholarly journals Improving energy efficiency of buffer cache in virtual machines

2012 ◽  
Author(s):  
Lei Ye ◽  
Chris Gniady
Keyword(s):  
Energy Efficiency ◽  
Virtual Machines ◽  
Buffer Cache

scholarly journals SNMP for Cloud Environment Energy Efficiency

2021 ◽  
Author(s):  
Marta Chinnici ◽  
Asif Iqbal ◽  
ah lian kor ◽  
colin pattinson ◽  
eric rondeau
Keyword(s):  
Energy Efficiency ◽  
Cloud Computing ◽  
Power Consumption ◽  
Empirical Evidence ◽  
Rapid Growth ◽  
Virtual Machines ◽  
Cloud Environment ◽  
Second Stage ◽  
The Core ◽  
Snmp Protocol

Abstract Cloud computing has seen rapid growth and environments are now providing multiple physical servers with several virtual machines running on those servers. Networks have grown larger and have become more powerful in recent years. A vital problem related to this advancement is that it has become increasingly complex to manage networks. SNMP is one standard which is applied as a solution to this management of networks problem. This work utilizes SNMP to explore the capabilities of SNMP protocol and its features for monitoring, control and automation of virtual machines and hypervisors. For this target, a stage-wise solution has been formed that obtains results of experiments from the first stage uses SNMPv3 and feed to the second stage for further processing and advancement. The target of the controlling experiments is to explore the extent of SNMP capability in the control of virtual machines running in a hypervisor, also in terms of energy efficiency. The core contribution based on real experiments is conducted to provide empirical evidence for the relation between power consumption and virtual machines.

SAVE: self-adaptive consolidation of virtual machines for energy efficiency of CPU-intensive applications in the cloud

2019 ◽  
Vol 75 (11) ◽  
pp. 7076-7100 ◽  
Author(s):  
Wenxia Guo ◽  
Ping Kuang ◽  
Yaqiu Jiang ◽  
Xiang Xu ◽  
Wenhong Tian
Keyword(s):  
Energy Efficiency ◽  
Virtual Machines ◽  
Self Adaptive

Energy Efficiency of Cloud Virtual Machines: From Traffic Pattern and CPU Affinity Perspectives

2017 ◽  
Vol 11 (2) ◽  
pp. 835-845 ◽  
Author(s):  
Chi Xu ◽  
Ziyang Zhao ◽  
Haiyang Wang ◽  
Ryan Shea ◽  
Jiangchuan Liu
Keyword(s):  
Energy Efficiency ◽  
Virtual Machines ◽  
Traffic Pattern

A Virtual Machine Placement Algorithm with Energy-Efficiency in Cloud Computing

2017 ◽  
Vol 8 (2) ◽  
pp. 20-36
Author(s):  
Yu Cai
Keyword(s):  
Energy Efficiency ◽  
Energy Efficient ◽  
Virtual Machines ◽  
Live Migration ◽  
Vm Placement ◽  
Server Cluster ◽  
Simulated Environment ◽  
Resource Requirements ◽  
Placement Algorithm

Energy efficient virtual machines (VM) management and distribution on cloud platforms is an important research subject. Mapping VMs into PMs (Physical Machines) requires knowing the capacity of each PM and the resource requirements of the VMs. It should also take into accounts of VM operation overheads, the reliability of PMs, Quality of Service (QoS) in addition to energy efficiency. In this article, the authors propose an energy efficient statistical live VM placement scheme in a heterogeneous server cluster. Their scheme supports VM requests scheduling and live migration to minimize the number of active servers in order to save the overall energy in a virtualized server cluster. Specifically, the proposed VM placement scheme incorporates all VM operation overheads in the dynamic migration process. In addition, it considers other important factors in relation to energy consumption and is ready to be extended with more considerations on user demands. The authors conducted extensive evaluations based on HPC jobs in a simulated environment. The results prove the effectiveness of the proposed scheme.

Improving Energy Efficiency of Virtual Machines with Timer Tick Variations

2015 ◽  
Vol 11 (3) ◽  
pp. 401-405 ◽  
Author(s):  
Bijoy A. Jose ◽  
Abhishek Agrawal
Keyword(s):  
Energy Efficiency ◽  
Virtual Machines

scholarly journals A Heuristic Research of the Designing and Evaluation of Energy Efficiency using Dynamic VM Consolidation in Green Cloud Data Centres

2019 ◽  
Vol 8 (6S4) ◽  
pp. 1532-1540
Keyword(s):  
Carbon Dioxide ◽  
Energy Efficiency ◽  
Quality Of Service ◽  
Virtual Machines ◽  
Web Based ◽  
Cloud Data ◽  
The Earth ◽  
Data Centres ◽  
Vm Consolidation

Allotted computing is a blasting innovation that tenders effective assets, and smooth accessibility through web based processing. however, the growing wishes of clients for such administrations are convincing the cloud professional corporations to send huge portions of strength hungry server farms which element awful effect to the earth with the aid of the usage of plenteous Carbon Dioxide discharge. To limit control usage and strengthen the quality of service (QoS) inside the server farm assesses the strength usage in an assortment of plans in IaaS of dispensed computing situation. Dynamic Virtual Machines’ Consolidation and Placement(DVMCP) is an in a position strategies for enhancing using assets and proficient power usage in Cloud DataCenters. in this exploration, we proposed a calculation, Energy Conscious Greeny Cloud Dynamic (ECGCD) set of rules that accomplishes live VM relocation that is turning off the inert has or located it to lowcontrol mode (i.e., rest or hibernation),that builds up power productivity and succesful usage of property in the dynamic hosts. The take a look at stop result confirmations with duplicate that, the proposed calculation achieves good sized diploma of lower in electricity usage in correlation with the modern-day-day VM combination calculations.

scholarly journals Load Aware Hotspot Selection for SLA Improvement in Cloud Computing and Protect Environment by Reduction In CO2 Emissions

2021 ◽  
Vol 889 (1) ◽  
pp. 012028
Author(s):  
A.P Vaneet Kumar ◽  
Balkrishan Jindal
Keyword(s):  
Energy Efficiency ◽  
Cloud Computing ◽  
Virtual Machines ◽  
Everyday Objects ◽  
Proposed Model ◽  
Classical Models ◽  
Computer Based ◽  
Selection For ◽  
Cloud Users ◽  
Vm Consolidation

Abstract Internet of Things (IoT) is a leading concept that envisions everyday objects around us as a part of internet. In order to accomplish this attribution, cloud computing provides a pathway to deliver all the promises with IoT enabled devices. The outbreak of COVID-19 coronavirus, namely SARS-CoV-2, acts as feather to the cap for the growth of Cloud users. With the increasing traffic of applications on cloud computing infrastructure and the explosion in data center sizes, QoS along with energy efficiency to protect environment, reducing CO2 emissions is need of the hour. This strategy is typically achieved using Three Layer upper Threshold (TLTHR) policy to analyze and perform VM consolidation. The proposed model controls number of migrations by placement of virtual machines, based on VMs and their utilization capacity on host. The efficacy of the proposed technique is exhibited by comparing it with other baseline algorithms using computer based simulation. Hence better QoS and energy efficiency has been obtained than other classical models.

scholarly journals Toward Enhancing the Energy Efficiency and Minimizing the SLA Violations in Cloud Data Centers

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
E. I. Elsedimy ◽  
Fahad Algarni
Keyword(s):  
Energy Efficiency ◽  
Bin Packing ◽  
Virtual Machines ◽  
Optimal Solution ◽  
Packing Problem ◽  
Service Level ◽  
Entropy Method ◽  
Cloud Data Center ◽  
Cloud Data ◽  
And Performance

Recently, the problem of Virtual Machine Placement (VMP) has received enormous attention from the research community due to its direct effect on the energy efficiency, resource utilization, and performance of the cloud data center. VMP is considered as a multidimensional bin packing problem, which is a type of NP-hard problem. The challenge in VMP is how to optimally place multiple independent virtual machines into a few physical servers to maximize a cloud provider’s revenue while meeting the Service Level Agreements (SLAs). In this paper, an effective multiobjective algorithm based on Particle Swarm Optimization (PSO) technique for the VMP problem, referred to as VMPMOPSO, is proposed. The proposed VMPMOPSO utilizes the crowding entropy method to optimize the VMP and to improve the diversity among the obtained solutions as well as accelerate the convergence speed toward the optimal solution. VMPMOPSO was compared with a simple single-objective algorithm, called First-Fit-Decreasing (FFD), and two multiobjective ant colony and genetic algorithms. Two simulation experiments were conducted to verify the effectiveness and efficiency of the proposed VMPMOPSO. The first experiment shows that the proposed algorithm has better performance than the algorithms we compared it to in terms of power consumption, SLA violation, and resource wastage. The second indicates that the Pareto optimal solutions obtained by applying VMPMOPSO have a good distribution and a better convergence than the comparative algorithms.

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