Energy Efficient Allocation of Virtual Machines in Cloud Computing Environments Based on Demand Forecast

2012 ◽  
pp. 137-151 ◽  
Author(s):  
Jian Cao ◽  
Yihua Wu ◽  
Minglu Li
Keyword(s):  
Cloud Computing ◽  
Energy Efficient ◽  
Virtual Machines ◽  
Efficient Allocation ◽  
Demand Forecast ◽  
On Demand ◽  
Computing Environments

scholarly journals GATECloud.net: a platform for large-scale, open-source text processing on the cloud

2013 ◽  
Vol 371 (1983) ◽  
pp. 20120071 ◽  
Author(s):  
Valentin Tablan ◽  
Ian Roberts ◽  
Hamish Cunningham ◽  
Kalina Bontcheva
Keyword(s):  
Cloud Computing ◽  
Language Processing ◽  
Large Scale ◽  
Virtual Machines ◽  
Cost Benefit Analysis ◽  
Text Processing ◽  
Cost Benefit ◽  
Data Intensive ◽  
On Demand ◽  
Usage Evaluation

Cloud computing is increasingly being regarded as a key enabler of the ‘democratization of science’, because on-demand, highly scalable cloud computing facilities enable researchers anywhere to carry out data-intensive experiments. In the context of natural language processing (NLP), algorithms tend to be complex, which makes their parallelization and deployment on cloud platforms a non-trivial task. This study presents a new, unique, cloud-based platform for large-scale NLP research—GATECloud. net. It enables researchers to carry out data-intensive NLP experiments by harnessing the vast, on-demand compute power of the Amazon cloud. Important infrastructural issues are dealt with by the platform, completely transparently for the researcher: load balancing, efficient data upload and storage, deployment on the virtual machines, security and fault tolerance. We also include a cost–benefit analysis and usage evaluation.

scholarly journals Energy-Efficient Scheduling Scheme for Virtual Machines in Cloud Computing

2012 ◽  
Vol 56 (10) ◽  
pp. 19-25 ◽  
Author(s):  
Shailesh Deore ◽  
A. N. Patil ◽  
Ruchira Bhargava
Keyword(s):  
Cloud Computing ◽  
Energy Efficient ◽  
Virtual Machines ◽  
Scheduling Scheme ◽  
Energy Efficient Scheduling

A Security Framework for Secure Cloud Computing Environments

2019 ◽  
pp. 249-263 ◽  
Author(s):  
Mouna Jouini ◽  
Latifa Ben Arfa Rabai
Keyword(s):  
Cloud Computing ◽  
New Technology ◽  
Security Risk ◽  
Security Issues ◽  
On Demand ◽  
Cloud Computing Adoption ◽  
Failure Cost ◽  
Computing Environments ◽  
Secure Cloud Computing ◽  
Security Risk Assessment

Cloud computing technology is a relatively new concept of providing scalable and virtualized resources, software and hardware on demand to consumers. It presents a new technology to deliver computing resources as a service. It offers a variety of benefits like services on demand and provisioning and suffers from several weaknesses. In fact, security presents a major obstacle in cloud computing adoption. In this paper, the authors will deal with security problems in cloud computing systems and show how to solve these problems using a quantitative security risk assessment model named Multi-dimensional Mean Failure Cost (M2FC). In fact, they summarize first security issues related to cloud computing environments and then propose a generic framework that analysis and evaluate cloud security problems and then propose appropriate countermeasures to solve these problems.

scholarly journals ENERGY EFFICIENT LOAD BALANCING FOR CLOUD DATA CENTER

2017 ◽  
Vol 10 (13) ◽  
pp. 162
Author(s):  
Amey Rivankar ◽  
Anusooya G
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Energy Efficient ◽  
Large Scale ◽  
Virtual Machines ◽  
Cloud Data Center ◽  
Cloud Data ◽  
Efficient Resource ◽  
Cloud Data Centers ◽  
Near Future

Cloud computing is the latest trend in large-scale distributed computing. It provides diverse services on demand to distributive resources such asservers, software, and databases. One of the challenging problems in cloud data centers is to manage the load of different reconfigurable virtual machines over one another. Thus, in the near future of cloud computing field, providing a mechanism for efficient resource management will be very significant. Many load balancing algorithms have been already implemented and executed to manage the resources efficiently and adequately. The objective of this paper is to analyze shortcomings of existing algorithms and implement a new algorithm which will give optimized load balancingresult.

scholarly journals Energy Efficient Resource Allocation in Cloud Computing Environments

IEEE Access ◽  
2016 ◽  
Vol 4 ◽  
pp. 8544-8557 ◽  
Author(s):  
Shahin Vakilinia ◽  
Behdad Heidarpour ◽  
Mohamed Cheriet
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Energy Efficient ◽  
Efficient Resource ◽  
Computing Environments ◽  
Energy Efficient Resource Allocation

CONDE: Climate simulation ON DEmand using HPCaaS

2020 ◽  
Author(s):  
Diego A. Pérez Montes ◽  
Juan A. Añel ◽  
Javier Rodeiro
Keyword(s):  
Cloud Computing ◽  
Web Services ◽  
Climate Simulation ◽  
On Demand ◽  
Ensemble Simulations ◽  
Amazon Web Services ◽  
Computing Environments ◽  
Microsoft Azure

<p><strong>CONDE (Climate simulation ON DEmand)</strong> is the final result of our work and research about climate and meteorological simulations over an HPC as a Service (HPCaaS) model. On our architecture we run very large climate ensemble simulations using a, adapted, WRF version that is executed on-demand and that can be deployed over different Cloud Computing environments (like Amazon Web Services, Microsoft Azure or Google Cloud) and that uses BOINC as middleware for the tasks execution and results gathering. Here, we also present as well some basic examples of applications and experiments to verify that the simulations ran in our system are correct and show valid results. </p>

Sign in / Sign up

Export Citation Format

Share Document