scholarly journals Parallel MapReduce: Maximizing Cloud Resource Utilization and Performance Improvement Using Parallel Execution Strategies

2018 ◽  
Vol 2018 ◽  
pp. 1-17
Author(s):  
Ahmed Abdulhakim Al-Absi ◽  
Najeeb Abbas Al-Sammarraie ◽  
Wael Mohamed Shaher Yafooz ◽  
Dae-Ki Kang
Keyword(s):  
Cloud Computing ◽  
Performance Improvement ◽  
Biomedical Applications ◽  
Large Data ◽  
Cost Effective ◽  
Parallel Execution ◽  
Computing Platform ◽  
Execution Strategy ◽  
Large Data Analysis ◽  
Cloud Resources

MapReduce is the preferred cloud computing framework used in large data analysis and application processing. MapReduce frameworks currently in place suffer performance degradation due to the adoption of sequential processing approaches with little modification and thus exhibit underutilization of cloud resources. To overcome this drawback and reduce costs, we introduce a Parallel MapReduce (PMR) framework in this paper. We design a novel parallel execution strategy of Map and Reduce worker nodes. Our strategy enables further performance improvement and efficient utilization of cloud resources execution of Map and Reduce functions to utilize multicore environments available with computing nodes. We explain in detail makespan modeling and working principle of the PMR framework in the paper. Performance of PMR is compared with Hadoop through experiments considering three biomedical applications. Experiments conducted for BLAST, CAP3, and DeepBind biomedical applications report makespan time reduction of 38.92%, 18.00%, and 34.62% considering the PMR framework against Hadoop framework. Experiments' results prove that the PMR cloud computing platform proposed is robust, cost-effective, and scalable, which sufficiently supports diverse applications on public and private cloud platforms. Consequently, overall presentation and results indicate that there is good matching between theoretical makespan modeling presented and experimental values investigated.

E-Commerce Recommendation System Based on Cloud Computing

2014 ◽  
Vol 926-930 ◽  
pp. 2570-2573
Author(s):  
Wei Zhao ◽  
Hong Tao Zhang
Keyword(s):  
Cloud Computing ◽  
Data Analysis ◽  
Data Exchange ◽  
Recommendation System ◽  
Large Data ◽  
Analysis Data ◽  
The Novel ◽  
Computing Platform ◽  
Large Data Analysis ◽  
Business Flow

According to the Present Situations were that there is an urgent demand for large data analysis in Electronic Commerce, by using cloud computings advantage in storing and analyzing mass data, the solution of new project which can analysis data was proposed, based on the cloud computing. Firstly, aiming to the advantages of the cloud computing platform, the novel data-analysis architecture was designed, then the business flow chart by using cloud computing analysis on the architecture. Finally the project is validated by practical application and possesses certain reference meaning in data exchange based on cloud computing.

Optimization and Management in Order to Drive Targeted Networking Memory Database

2015 ◽  
Vol 727-728 ◽  
pp. 965-968
Author(s):  
Ji Liu
Keyword(s):  
Cloud Computing ◽  
Wireless Communication ◽  
Data Processing ◽  
Real Time ◽  
System Architecture ◽  
Large Data ◽  
Processing Efficiency ◽  
Computing Platform ◽  
Cloud Computing Platform ◽  
Vehicle Networking

In today's vehicle networking system architecture is mainly composed of four parts: sensor networks, wireless communication networks, cloud computing platforms and vehicle terminal. Wireless sensor network is responsible for the front of the real-time collection of traffic information, a wireless communication network to send information to the backend of the cloud computing platform, cloud computing platform to handle a large number of vehicles to collect real-time information from the front, and finally sends the information to the end user. In this thesis, this car networking research background, analyze vehicle networking system architecture consisting of performance indicators for each part of the system recognize cloud platform for large data processing efficiency as well as room for improvement. Then put forward the traditional computing platform I / O disk database with in-memory database to replace the cloud to enhance cloud computing platform for large data processing efficiency.

scholarly journals An Analysis of Data Security and Privacy for Cloud Computing

2020 ◽  
Vol 9 (1) ◽  
pp. 27-39
Author(s):  
Akella Subhadra
Keyword(s):  
Cloud Computing ◽  
Data Security ◽  
Large Data ◽  
Cost Effective ◽  
Cloud Service ◽  
Third Party ◽  
Security And Privacy ◽  
Service Users ◽  
Digital Information ◽  
Cloud Computing Service

Cloud Computing is the important buzzword in the today’s world of computer. Cloud is global platform that allows digital information to be stored and distributed at very less cost and very fast to use. In these days since the data is very big in size many users are interested to store their valuable data in the cloud .The application software and databases in cloud computing are moved to the centralized large data centers, where the management of the data and services may not be fully trustworthy. Cloud Computing is scalable, fast, flexible, and cost-effective technology platform for IT enabled services over the internet. There are various advantages of cloud computing but ultimately cloud service users have to put their data over the cloud i.e., third party servers which are not directly controlled by the data owner Data security has consistently been a major issue in information technology In cloud computing in users perspective mainly in government ,industry and business Data security and ivacy protection issues are relevant to both hardware and software in the cloud architecture. Cloud security is becoming a key differentiator and competitive edge between cloud providers. In spite of various benefits that are provided by the cloud computing services, cloud computing service users are very much afraid about the security of their data .So this paper focuses on various issues regarding cloud computing, data security and how cloud provides data integrity, confidentiality, availability over user’s data? How data stored over cloud storage servers will be protected from attackers? Risk management of data present on the Cloud is another challenge. There is a requirement to identify the risks an organization would be taking while hosting data and services on the Cloud. In this paper, we present those issues that are preventing people from adopting the cloud and to minimize risks of these issues.

scholarly journals An Adaptive Procedure for Task Scheduling Optimization in Mobile Cloud Computing

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Pham Phuoc Hung ◽  
Eui-Nam Huh
Keyword(s):  
Cloud Computing ◽  
Task Scheduling ◽  
Mobile Cloud Computing ◽  
Network Connectivity ◽  
Cost Effective ◽  
Mobile Cloud ◽  
Google Glass ◽  
New Paradigm ◽  
Adaptive Procedure ◽  
Computing Platform

Nowadays, mobile cloud computing (MCC) has emerged as a new paradigm which enables offloading computation-intensive, resource-consuming tasks up to a powerful computing platform in cloud, leaving only simple jobs to the capacity-limited thin client devices such as smartphones, tablets, Apple’s iWatch, and Google Glass. However, it still faces many challenges due to inherent problems of thin clients, especially the slow processing and low network connectivity. So far, a number of research studies have been carried out, trying to eliminate these problems, yet few have been found efficient. In this paper, we present an enhanced architecture, taking advantage of collaboration of thin clients and conventional desktop or laptop computers, known as thick clients, particularly aiming at improving cloud access. Additionally, we introduce an innovative genetic approach for task scheduling such that the processing time is minimized, while considering network contention and cloud cost. Our simulation shows that the proposed approach is more cost-effective and achieves better performance compared with others.

Workflow Scheduling for Scientific Application in Homogeneous Cloud Environment

2017 ◽  
Vol 7 (7) ◽  
pp. 137
Author(s):  
. Monika ◽  
Pardeep Kumar ◽  
Sanjay Tyagi
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Large Data ◽  
Workflow Scheduling ◽  
Cloud Environment ◽  
Computing Environment ◽  
Scientific Application ◽  
Cloud Computing Environment ◽  
Flow Of Information

In Cloud computing environment QoS i.e. Quality-of-Service and cost is the key element that to be take care of. As, today in the era of big data, the data must be handled properly while satisfying the request. In such case, while handling request of large data or for scientific applications request, flow of information must be sustained. In this paper, a brief introduction of workflow scheduling is given and also a detailed survey of various scheduling algorithms is performed using various parameter.

Power Management of Virtualized Cloud Computing Platform

2012 ◽  
Vol 35 (6) ◽  
pp. 1262 ◽  
Author(s):  
Ke-Jiang YE ◽  
Zhao-Hui WU ◽  
Xiao-Hong JIANG ◽  
Qin-Ming HE
Keyword(s):  
Cloud Computing ◽  
Power Management ◽  
Computing Platform ◽  
Cloud Computing Platform

Review on Methodologies Used in the Synthesis of Metal Nanoparticles: Significance of Phytosynthesis Using Plant Extract as an Emerging Tool

2020 ◽  
Vol 26 (40) ◽  
pp. 5188-5204
Author(s):  
Uzair Nagra ◽  
Maryam Shabbir ◽  
Muhammad Zaman ◽  
Asif Mahmood ◽  
Kashif Barkat
Keyword(s):  
Green Synthesis ◽  
Metal Nanoparticles ◽  
Plant Extracts ◽  
Biomedical Applications ◽  
Noble Metals ◽  
Cost Effective ◽  
Green Technology ◽  
Critical Parameters ◽  
Nanosized Particles ◽  
Preparation Methods

Nanosized particles, with a size of less than 100 nm, have a wide variety of applications in various fields of nanotechnology and biotechnology, especially in the pharmaceutical industry. Metal nanoparticles [MNPs] have been synthesized by different chemical and physical procedures. Still, the biological approach or green synthesis [phytosynthesis] is considered as a preferred method due to eco-friendliness, nontoxicity, and cost-effective production. Various plants and plant extracts have been used for the green synthesis of MNPs, including biofabrication of noble metals, metal oxides, and bimetallic combinations. Biomolecules and metabolites present in plant extracts cause the reduction of metal ions into nanosized particles by one-step preparation methods. MNPs have remarkable attractiveness in biomedical applications for their use as potential antioxidant, anticancer and antibacterial agents. The present review offers a comprehensive aspect of MNPs production via top-to-bottom and bottom-to-top approach with considerable emphasis on green technology and their possible biomedical applications. The critical parameters governing the MNPs formation by plant-based synthesis are also highlighted in this review.

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