The Open Cloud Testbed: Supporting Open Source Cloud Computing Systems Based on Large Scale High Performance, Dynamic Network Services

2010 ◽  
pp. 89-97
Author(s):  
Robert Grossman ◽  
Yunhong Gu ◽  
Michal Sabala ◽  
Colin Bennet ◽  
Jonathan Seidman ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Open Source ◽  
High Performance ◽  
Large Scale ◽  
Dynamic Network ◽  
Network Services ◽  
Computing Systems

scholarly journals MS-PyCloud: An open-source, cloud computing-based pipeline for LC-MS/MS data analysis

2018 ◽  
Author(s):  
Li Chen ◽  
Bai Zhang ◽  
Michael Schnaubelt ◽  
Punit Shah ◽  
Paul Aiyetan ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Data Analysis ◽  
Open Source ◽  
High Performance ◽  
Large Scale ◽  
Rapid Development ◽  
Data File ◽  
Data Sets ◽  
Proteomics Data ◽  
Amazon Web Services

ABSTRACTRapid development and wide adoption of mass spectrometry-based proteomics technologies have empowered scientists to study proteins and their modifications in complex samples on a large scale. This progress has also created unprecedented challenges for individual labs to store, manage and analyze proteomics data, both in the cost for proprietary software and high-performance computing, and the long processing time that discourages on-the-fly changes of data processing settings required in explorative and discovery analysis. We developed an open-source, cloud computing-based pipeline, MS-PyCloud, with graphical user interface (GUI) support, for LC-MS/MS data analysis. The major components of this pipeline include data file integrity validation, MS/MS database search for spectral assignment, false discovery rate estimation, protein inference, determination of protein post-translation modifications, and quantitation of specific (modified) peptides and proteins. To ensure the transparency and reproducibility of data analysis, MS-PyCloud includes open source software tools with comprehensive testing and versioning for spectrum assignments. Leveraging public cloud computing infrastructure via Amazon Web Services (AWS), MS-PyCloud scales seamlessly based on analysis demand to achieve fast and efficient performance. Application of the pipeline to the analysis of large-scale iTRAQ/TMT LC-MS/MS data sets demonstrated the effectiveness and high performance of MS-PyCloud. The software can be downloaded at: https://bitbucket.org/mschnau/ms-pycloud/downloads/

A Comprehensive Study on Commercial Applications of Cloud Computing

2020 ◽  
Vol 17 (9) ◽  
pp. 4411-4418
Author(s):  
S. Jagannatha ◽  
B. N. Tulasimala
Keyword(s):  
Cloud Computing ◽  
Data Storage ◽  
High Performance ◽  
Large Scale ◽  
Service Providers ◽  
Cost Effective ◽  
Computer Hardware ◽  
Computing Technology ◽  
Computing Systems ◽  
Computational Performance

In the world of information communication technology (ICT) the term Cloud Computing has been the buzz word. Cloud computing is changing its definition the way technocrats are using it according to the environment. Cloud computing as a definition remains very contentious. Definition is stated liable to a particular application with no unanimous definition, making it altogether elusive. In spite of this, it is this technology which is revolutionizing the traditional usage of computer hardware, software, data storage media, processing mechanism with more of benefits to the stake holders. In the past, the use of autonomous computers and the nodes that were interconnected forming the computer networks with shared software resources had minimized the cost on hardware and also on the software to certain extent. Thus evolutionary changes in computing technology over a few decades has brought in the platform and environment changes in machine architecture, operating system, network connectivity and application workload. This has made the commercial use of technology more predominant. Instead of centralized systems, parallel and distributed systems will be more preferred to solve computational problems in the business domain. These hardware are ideal to solve large-scale problems over internet. This computing model is data-intensive and networkcentric. Most of the organizations with ICT used to feel storing of huge data, maintaining, processing of the same and communication through internet for automating the entire process a challenge. In this paper we explore the growth of CC technology over several years. How high performance computing systems and high throughput computing systems enhance computational performance and also how cloud computing technology according to various experts, scientific community and also the service providers is going to be more cost effective through different dimensions of business aspects.

Massive Image Treatment System Based on Cloud Computing Platform

2014 ◽  
Vol 687-691 ◽  
pp. 3733-3737
Author(s):  
Dan Wu ◽  
Ming Quan Zhou ◽  
Rong Fang Bie
Keyword(s):  
Image Processing ◽  
Cloud Computing ◽  
High Performance ◽  
Large Scale ◽  
Processing System ◽  
Virtual Space ◽  
Image Processing System ◽  
Computing Platform ◽  
Simulation Calculation ◽  
Computer Resources

Massive image processing technology requires high requirements of processor and memory, and it needs to adopt high performance of processor and the large capacity memory. While the single or single core processing and traditional memory can’t satisfy the need of image processing. This paper introduces the cloud computing function into the massive image processing system. Through the cloud computing function it expands the virtual space of the system, saves computer resources and improves the efficiency of image processing. The system processor uses multi-core DSP parallel processor, and develops visualization parameter setting window and output results using VC software settings. Through simulation calculation we get the image processing speed curve and the system image adaptive curve. It provides the technical reference for the design of large-scale image processing system.

scholarly journals Scheduling under Open stack – The Current State and Future Enhancements

2019 ◽  
Vol 8 (2) ◽  
pp. 375-382
Keyword(s):  
Cloud Computing ◽  
Open Source ◽  
Task Scheduling ◽  
Computing System ◽  
Important Task ◽  
Shared Resources ◽  
Computing Systems ◽  
Current State ◽  
Cloud Computing System ◽  
Research Findings

Cloud computing is being heavily used for implementing different kinds of applications. Many of the client applications are being migrated to cloud for the reasons of cost and elasticity. Cloud computing is generally implemented on distributing computing wherein the Physical servers are heavily distributed considering both hardware and software, the connectivity among which is established through Internet. The cloud computing systems as such have many physical servers which contain many resources. The resources can be made to be shared among many users who are the tenants to the cloud computing system. The resources can be virtualized so as to provide shared resources to the clients. Scheduling is one of the most important task of a cloud computing system which is concerned with task scheduling, resource scheduling and scheduling Virtual Machin Migration. It is important to understand the issue of scheduling within a cloud computing system more in-depth so that any improvements with reference to scheduling can be investigated and implemented. For carrying in depth research, an OPEN source based cloud computing system is needed. OPEN STACK is one such OPEN source based cloud computing system that can be considered for experimenting the research findings that are related to cloud computing system. In this paper an overview on the way the Scheduling aspect per say has been implemented within OPEN STACK cloud computing system

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.

Adaptive Threshold Based Scheduler for Batch of Independent Jobs for Cloud Computing System

2021 ◽  
pp. 2246-2266
Author(s):  
TAJ ALAM ◽  
PARITOSH DUBEY ◽  
ANKIT KUMAR
Keyword(s):  
Cloud Computing ◽  
Distributed Systems ◽  
High Performance ◽  
Large Scale ◽  
Real Life ◽  
Interval Estimation ◽  
Computing System ◽  
Adaptive Threshold ◽  
Batch Simulation ◽  
Heterogeneous Distributed Systems

Distributed systems are efficient means of realizing high-performance computing (HPC). They are used in meeting the demand of executing large-scale high-performance computational jobs. Scheduling the tasks on such computational resources is one of the prime concerns in the heterogeneous distributed systems. Scheduling jobs on distributed systems are NP-complete in nature. Scheduling requires either heuristic or metaheuristic approach for sub-optimal but acceptable solutions. An adaptive threshold-based scheduler is one such heuristic approach. This work proposes adaptive threshold-based scheduler for batch of independent jobs (ATSBIJ) with the objective of optimizing the makespan of the jobs submitted for execution on cloud computing systems. ATSBIJ exploits the features of interval estimation for calculating the threshold values for generation of efficient schedule of the batch. Simulation studies on CloudSim ensures that the ATSBIJ approach works effectively for real life scenario.

A Review: Map Reduce Framework for Cloud Computing

2018 ◽  
Vol 7 (4.6) ◽  
pp. 13
Author(s):  
Mekala Sandhya ◽  
Ashish Ladda ◽  
Dr. Uma N Dulhare ◽  
. . ◽  
. .
Keyword(s):  
Data Mining ◽  
Cloud Computing ◽  
Distributed Computing ◽  
Data Storage ◽  
High Performance ◽  
Large Scale ◽  
Distributed Storage ◽  
Large Data ◽  
Mass Data ◽  
Internet Information

In this generation of Internet, information and data are growing continuously. Even though various Internet services and applications. The amount of information is increasing rapidly. Hundred billions even trillions of web indexes exist. Such large data brings people a mass of information and more difficulty discovering useful knowledge in these huge amounts of data at the same time. Cloud computing can provide infrastructure for large data. Cloud computing has two significant characteristics of distributed computing i.e. scalability, high availability. The scalability can seamlessly extend to large-scale clusters. Availability says that cloud computing can bear node errors. Node failures will not affect the program to run correctly. Cloud computing with data mining does significant data processing through high-performance machine. Mass data storage and distributed computing provide a new method for mass data mining and become an effective solution to the distributed storage and efficient computing in data mining. 

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