Novel Software Containers for Engineering and Scientific Simulations in the Cloud

2016 ◽  
Vol 8 (1) ◽  
pp. 38-49 ◽  
Author(s):  
Wolfgang Gentzsch ◽  
Burak Yenier
Keyword(s):  
Cloud Computing ◽  
High Performance ◽  
Data Transfer ◽  
Current Status ◽  
Loss Of Control ◽  
On Demand ◽  
Scientific Simulations ◽  
Software Licensing ◽  
Lock In ◽  
Computing Infrastructure

The adoption of cloud computing for engineering and scientific applications is still lagging behind, although many cloud providers today offer powerful computing infrastructure as a service, and enterprises are already making routine use of it. Reasons for this slow adoption are many: complex access to clouds, inflexible software licensing, time-consuming big data transfer, loss of control over their assets, service provider lock-in, to name a few. But recently, with the advent of the UberCloud's novel high-performance software container technology, many of these roadblocks are currently being removed. In this paper the authors describe the current status and landscape of clouds for engineers and scientists, the benefits and challenges, and how UberCloud is providing an online solution platform and container technology which reduce or even remove many of the current roadblock, and thus offer every engineer and scientist additional compute power on demand, in an easily accessible way.

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.

KIET Framework for Cloud Adoption

2018 ◽  
Vol 8 (4) ◽  
pp. 72-87
Author(s):  
Lalit Mohan Sanagavarapu ◽  
Gangadharan G.R. ◽  
Raghu Reddy Y.
Keyword(s):  
Cloud Computing ◽  
Banking Sector ◽  
Diffusion Theory ◽  
Disruptive Technology ◽  
Loss Of Control ◽  
Application Development ◽  
It Investments ◽  
The Public ◽  
On Demand ◽  
It Systems

The expenses in the sustenance of IT investments has become a major ledger item in businesses to the extent that in some cases business priorities had to be changed for sustaining IT systems. Cloud computing, a disruptive technology, is changing the sustenance model with on-demand and metered service approach. However, the adoption of this technology has not been consistent across sectors due to fear on loss of control and changes required in application development and deployment. Authors propose KIET (Knowing, Initiating, Evolving and Transforming) framework based on diffusion theory for adoption of cloud computing in organizations that have strong regulatory framework. Authors implemented the proposed framework on the Indian Banking sector, with majority of the banks being in the public sector. After the implementation of the framework, 49.4% of the banks have adopted cloud computing and another 27.8% of the banks have started the initial steps for adoption.

scholarly journals Contemporary Perception Of Task Scheduling Techniques In Cloud: A Review

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Cloud Computing ◽  
High Performance Computing ◽  
Task Scheduling ◽  
High Performance ◽  
Communications Technology ◽  
Small Companies ◽  
Network Access ◽  
On Demand ◽  
Computing Services ◽  
Performance Computing

Typically, the constant changes in computers and communications technology led to the need of on-demand network access to a shared computing resources to reduce cost and time and this is known as Cloud computing, which delivers computing services to users as a pay-as-you-go manner by emerging several distributed and high performance computing concepts. The cloud makes reaching any information or source possible from anywhere eliminating the setup and instillation step such that the user and the hardware may co-exist in different places. This comes beneficial for the users or the small companies that cannot effort to pay for the hardware, storage or resources as the big companies. Many of the studies on cloud computing was dedicated to the performance efficiency of task scheduling. Scheduling is a wide concept and it is one of the most important issues that generally work on mapping tasks to appropriate resources efficiently and effectively using one or more strategy. This paper have reviewed and classified the most recent scheduling algorithms in cloud computing and gave examples on each.

Faster and cheaper: how graphics processing units on spot-market instances minimize turnaround time and budget

2020 ◽  
pp. 1-67
Author(s):  
Nicholas T. Okita ◽  
Tiago A. Coimbra
Keyword(s):  
Cloud Computing ◽  
Graphics Processing Units ◽  
High Performance ◽  
Imaging Techniques ◽  
Spot Market ◽  
Turnaround Time ◽  
On Demand ◽  
Order Of Magnitude ◽  
Graphics Processing ◽  
Price Differences

Cloud computing is enabling users to instantiate and access high-performance computing clusters quickly. However, without proper knowledge of the type of application and the nature of the instances, it can become quite expensive. The objective is to show that adequately choosing the instances provides a fast execution, which, in turn, leads to a low execution price, using the pay-as-you-go model on cloud computing. We used graphics processing units instances on the spot market to execute a seismic-dataset interpolation job and compared their performance to regular on-demand CPU instances. Furthermore, we explored how scaling could also improve the execution times at small price differences. The experiments have shown that, by using an instance with eight accelerators on the spot market, we obtain up to three hundred times speed-up compared to the on-demand CPU options, while being one hundred times cheaper. Finally, our results have shown that seismic-imaging processing can be sped up by order of magnitude with a low budget, resulting in faster and cheaper turn around processing time and enabling possible new imaging techniques.

Comparative Study on XEN, KVM, VSphere, and Hyper-V

2016 ◽  
pp. 233-261
Author(s):  
Prashanta Kumar Das
Keyword(s):  
Cloud Computing ◽  
Distributed Computing ◽  
Service Delivery ◽  
Comparative Study ◽  
Open Source ◽  
Qualitative Comparison ◽  
Hardware Virtualization ◽  
Delivery Model ◽  
On Demand ◽  
Computing Infrastructure

This Chapter provides a quantitative and qualitative comparison of four popular virtualization platforms, open-source hypervisors Xen, KVM and proprietary hypervisors VMware vSphere (ESXi), Microsoft Hyper-V. Cloud Computing is on Demand, Pay-per-use distributed computing service delivery model in which computing resources can be used as Utility like other utility such as water, electricity etc. as per requirement. Cloud computing has made it possible to provide virtually unlimited computing infrastructure i.e. IaaS on demand using virtualization technology. Intel and AMD have independently developed virtualization extensions to the x86 architecture referred to as hardware virtualization.

Solving Security and Availability Challenges in Public Clouds

2014 ◽  
pp. 280-302
Author(s):  
Maxim Schnjakin ◽  
Christoph Meinel
Keyword(s):  
Cloud Computing ◽  
Cloud Storage ◽  
Data Distribution ◽  
Cloud Provider ◽  
On Demand ◽  
Perceived Availability ◽  
Overall Performance ◽  
Additional Charges ◽  
The Cost ◽  
Lock In

Cloud Computing as a service-on-demand architecture has grown in importance over the previous few years. One driver of its growth is the ever-increasing amount of data that is supposed to outpace the growth of storage capacity. The usage of cloud technology enables organizations to manage their data with low operational expenses. However, the benefits of cloud computing come along with challenges and open issues such as security, reliability, and the risk to become dependent on a provider for its service. In general, a switch of a storage provider is associated with high costs of adapting new APIs and additional charges for inbound and outbound bandwidth and requests. In this chapter, the authors present a system that improves availability, confidentiality, and reliability of data stored in the cloud. To achieve this objective, the authors encrypt users' data and make use of the RAID-technology principle to manage data distribution across cloud storage providers. Further, they discuss the security functionality and present a proof-of-concept experiment for the application to evaluate the performance and cost effectiveness of the approach. The authors deploy the application using eight commercial cloud storage repositories in different countries. The approach allows users to avoid vendor lock-in and reduces significantly the cost of switching providers. They also observe that the implementation improved the perceived availability and, in most cases, the overall performance when compared with individual cloud providers. Moreover, the authors estimate the monetary costs to be competitive to the cost of using a single cloud provider.

Solving Security and Availability Challenges in Public Clouds

2015 ◽  
pp. 1999-2021
Author(s):  
Maxim Schnjakin ◽  
Christoph Meinel
Keyword(s):  
Cloud Computing ◽  
Cloud Storage ◽  
Data Distribution ◽  
Cloud Provider ◽  
On Demand ◽  
Perceived Availability ◽  
Overall Performance ◽  
Additional Charges ◽  
The Cost ◽  
Lock In

Cloud Computing as a service-on-demand architecture has grown in importance over the previous few years. One driver of its growth is the ever-increasing amount of data that is supposed to outpace the growth of storage capacity. The usage of cloud technology enables organizations to manage their data with low operational expenses. However, the benefits of cloud computing come along with challenges and open issues such as security, reliability, and the risk to become dependent on a provider for its service. In general, a switch of a storage provider is associated with high costs of adapting new APIs and additional charges for inbound and outbound bandwidth and requests. In this chapter, the authors present a system that improves availability, confidentiality, and reliability of data stored in the cloud. To achieve this objective, the authors encrypt users' data and make use of the RAID-technology principle to manage data distribution across cloud storage providers. Further, they discuss the security functionality and present a proof-of-concept experiment for the application to evaluate the performance and cost effectiveness of the approach. The authors deploy the application using eight commercial cloud storage repositories in different countries. The approach allows users to avoid vendor lock-in and reduces significantly the cost of switching providers. They also observe that the implementation improved the perceived availability and, in most cases, the overall performance when compared with individual cloud providers. Moreover, the authors estimate the monetary costs to be competitive to the cost of using a single cloud provider.

Replication and Resubmission Based Adaptive Decision for Fault Tolerance in Real Time Cloud Computing

2016 ◽  
Vol 7 (2) ◽  
pp. 46-60 ◽  
Author(s):  
Prasenjit Kumar Patra ◽  
Harshpreet Singh ◽  
Rajwinder Singh ◽  
Saptarshi Das ◽  
Nilanjan Dey ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Fault Tolerance ◽  
Real Time ◽  
Cloud Infrastructure ◽  
Reliability Factor ◽  
On Demand ◽  
Computing Node ◽  
Computing Infrastructure ◽  
Demand Service

Cloud computing an adoptable technology is the upshot evolution of on demand service in the computing epitome of immense scale distributed computing. With the raising asks and welfares of cloud computing infrastructure, society can take leverage of intensive computing capability services and scalable, virtualized vicinity of cloud computing to carry out real time tasks executed on a remote cloud computing node. Due to the indeterminate latency and minimal control over computing node, sway the reliability factor. Therefore, there is a raise of requisite for fault tolerance to achieve reliability in the real time cloud infrastructure. In this paper, a model which provides fault tolerance named “Replication and resubmission based adaptive decision for fault tolerance in real-time cloud computing (RRADFTRC)” for real time cloud computing is projected with result. In the projected model, the system endure the faults and makes the adaptive decision on the basis of proper resource allocation of tasks with a new style of approach in real time cloud vicinity.

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