scholarly journals Representing Increasing Virtual Machine Security Strategy in Cloud Computing Computations

2021 ◽  
Vol 3 (2) ◽  
Author(s):  
Mohammad Shirzadi
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Load Balancing ◽  
High Performance Computing ◽  
Virtual Machine ◽  
Lower Energy ◽  
High Performance ◽  
Security Strategy ◽  
Cloud Processing ◽  
Performance Computing

This paper proposes algorithm for Increasing Virtual Machine Security Strategy in Cloud Computing computations. Imbalance between load and energy has been one of the disadvantages of old methods in providing server and hosting, so that if two virtual severs be active on a host and energy load be more on a host, it would allocated the energy of other hosts (virtual host) to itself to stay steady and this option usually leads to hardware overflow errors and users dissatisfaction. This problem has been removed in methods based on cloud processing but not perfectly, therefore,providing an algorithm not only will implement a suitable security background but also it will suitably divide energy consumption and load balancing among virtual severs. The proposed algorithm is compared with several previously proposed Security Strategy including SC-PSSF, PSSF and DEEAC. Comparisons show that the proposed method offers high performance computing, efficiency and consumes lower energy in the network.

Cloud Computing for Scientific Simulation and High Performance Computing

2013 ◽  
pp. 51-70
Author(s):  
Adrian Jackson ◽  
Michèle Weiland
Keyword(s):  
Cloud Computing ◽  
High Performance Computing ◽  
High Performance ◽  
Large Scale ◽  
Parallel Programs ◽  
Small Scale ◽  
Cloud Infrastructure ◽  
Scientific Simulations ◽  
Cloud Infrastructures ◽  
Performance Computing

This chapter describes experiences using Cloud infrastructures for scientific computing, both for serial and parallel computing. Amazon’s High Performance Computing (HPC) Cloud computing resources were compared to traditional HPC resources to quantify performance as well as assessing the complexity and cost of using the Cloud. Furthermore, a shared Cloud infrastructure is compared to standard desktop resources for scientific simulations. Whilst this is only a small scale evaluation these Cloud offerings, it does allow some conclusions to be drawn, particularly that the Cloud can currently not match the parallel performance of dedicated HPC machines for large scale parallel programs but can match the serial performance of standard computing resources for serial and small scale parallel programs. Also, the shared Cloud infrastructure cannot match dedicated computing resources for low level benchmarks, although for an actual scientific code, performance is comparable.

Green Computing

2014 ◽  
pp. 248-260
Keyword(s):  
Climate Change ◽  
Cloud Computing ◽  
High Performance Computing ◽  
High Performance ◽  
Large Scale ◽  
Green Computing ◽  
Research Topic ◽  
The Other ◽  
Cloud Infrastructures ◽  
Performance Computing

Green computing is a contemporary research topic to address climate and energy challenges. In this chapter, the authors envision the duality of green computing with technological trends in other fields of computing such as High Performance Computing (HPC) and cloud computing on one hand and economy and business on the other hand. For instance, in order to provide electricity for large-scale cloud infrastructures and to reach exascale computing, we need huge amounts of energy. Thus, green computing is a challenge for the future of cloud computing and HPC. Alternatively, clouds and HPC provide solutions for green computing and climate change. In this chapter, the authors discuss this proposition by looking at the technology in detail.

High Performance Computing on Mobile Devices

2016 ◽  
pp. 334-348
Author(s):  
Atta ur Rehman Khan ◽  
Abdul Nasir Khan
Keyword(s):  
Cloud Computing ◽  
High Performance Computing ◽  
Mobile Devices ◽  
High Performance ◽  
Mobile Cloud Computing ◽  
Computation Offloading ◽  
Mobile Cloud ◽  
Computing Technology ◽  
Wide Range ◽  
Performance Computing

Mobile devices are gaining high popularity due to support for a wide range of applications. However, the mobile devices are resource constrained and many applications require high resources. To cater to this issue, the researchers envision usage of mobile cloud computing technology which offers high performance computing, execution of resource intensive applications, and energy efficiency. This chapter highlights importance of mobile devices, high performance applications, and the computing challenges of mobile devices. It also provides a brief introduction to mobile cloud computing technology, its architecture, types of mobile applications, computation offloading process, effective offloading challenges, and high performance computing application on mobile devises that are enabled by mobile cloud computing technology.

A Hybrid Resource Reservation Method for Workflows in Clouds

2012 ◽  
Vol 4 (4) ◽  
pp. 1-21
Author(s):  
Tyng-Yeu Liang ◽  
Fu-Chun Lu ◽  
Jun-Yao Chiu
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
High Performance ◽  
Resource Reservation ◽  
Scientific Workflows ◽  
Service Oriented ◽  
Time And Energy ◽  
Gpu Architecture ◽  
Performance Computing ◽  
Oriented System

QoS and energy consumption are two important issues for Cloud computing. In this paper, the authors propose a hybrid resource reservation method to address these two issues for scientific workflows in the high-performance computing Clouds built on hybrid CPU/GPU architecture. As named, this method reserves proper CPU or GPU for executing different jobs in the same workflow based on the profile of execution time and energy consumption of each resource-to-program pair. They have implemented the proposed resource reservation method on a real service-oriented system. The experimental results show that the proposed resource reservation method can effectively maintain the QoS of workflows while simultaneously minimizing the energy consumption of executing the workflows.

scholarly journals Viability of Cloud Computing for Real-Time Numerical Weather Prediction

2016 ◽  
Vol 31 (6) ◽  
pp. 1985-1996 ◽  
Author(s):  
David Siuta ◽  
Gregory West ◽  
Henryk Modzelewski ◽  
Roland Schigas ◽  
Roland Stull
Keyword(s):  
Cloud Computing ◽  
High Performance Computing ◽  
Real Time ◽  
Numerical Weather Prediction ◽  
High Performance ◽  
Virtual Machines ◽  
Weather Prediction ◽  
Cloud Platform ◽  
Numerical Weather ◽  
Performance Computing

Abstract As cloud-service providers like Google, Amazon, and Microsoft decrease costs and increase performance, numerical weather prediction (NWP) in the cloud will become a reality not only for research use but for real-time use as well. The performance of the Weather Research and Forecasting (WRF) Model on the Google Cloud Platform is tested and configurations and optimizations of virtual machines that meet two main requirements of real-time NWP are found: 1) fast forecast completion (timeliness) and 2) economic cost effectiveness when compared with traditional on-premise high-performance computing hardware. Optimum performance was found by using the Intel compiler collection with no more than eight virtual CPUs per virtual machine. Using these configurations, real-time NWP on the Google Cloud Platform is found to be economically competitive when compared with the purchase of local high-performance computing hardware for NWP needs. Cloud-computing services are becoming viable alternatives to on-premise compute clusters for some applications.

Performance Evaluation of Cloud-Based High Performance Computing for Finite Element Analysis

2015 ◽  
Author(s):  
Dazhong Wu ◽  
Xi Liu ◽  
Steve Hebert ◽  
Wolfgang Gentzsch ◽  
Janis Terpenny
Keyword(s):  
Finite Element Analysis ◽  
Cloud Computing ◽  
Finite Element ◽  
High Performance Computing ◽  
High Performance ◽  
Interactive Effects ◽  
Element Analysis ◽  
Computing Paradigm ◽  
Run Time ◽  
Performance Computing

Cloud computing is an innovative computing paradigm that can potentially bridge the gap between increasing computing demands in computer aided engineering (CAE) applications and limited scalability, flexibility, and agility in traditional computing paradigms. In light of the benefits of cloud computing, high performance computing (HPC) in the cloud has the potential to enable users to not only accelerate computationally expensive CAE simulations (e.g., finite element analysis), but also to reduce costs by utilizing on-demand and scalable cloud computing resources. The objective of this research is to evaluate the performance of running a large finite element simulation in a public cloud. Specifically, an experiment is performed to identify individual and interactive effects of several factors (e.g., CPU core count, memory size, solver computational rate, and input/output rate) on run time using statistical methods. Our experimental results have shown that the performance of HPC in the cloud is sufficient for the application of a large finite element analysis, and that run time can be optimized by properly selecting a configuration of CPU, memory, and interconnect.

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