Cloud Computing Infrastructure for Data Intensive Applications

2017 ◽  
pp. 21-62 ◽  
Author(s):  
Yuri Demchenko ◽  
Fatih Turkmen ◽  
Cees de Laat ◽  
Ching-Hsien Hsu ◽  
Christophe Blanchet ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Data Intensive ◽  
Data Intensive Applications ◽  
Computing Infrastructure

PrEstoCloud

2021 ◽  
Vol 34 (1) ◽  
pp. 66-85
Author(s):  
Yiannis Verginadis ◽  
Dimitris Apostolou ◽  
Salman Taherizadeh ◽  
Ioannis Ledakis ◽  
Gregoris Mentzas ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Software Engineering ◽  
Response Time ◽  
Fog Computing ◽  
Data Sources ◽  
Data Intensive ◽  
Service Response Time ◽  
Enabling Services ◽  
Data Intensive Applications ◽  
Multi Cloud

Fog computing extends multi-cloud computing by enabling services or application functions to be hosted close to their data sources. To take advantage of the capabilities of fog computing, serverless and the function-as-a-service (FaaS) software engineering paradigms allow for the flexible deployment of applications on multi-cloud, fog, and edge resources. This article reviews prominent fog computing frameworks and discusses some of the challenges and requirements of FaaS-enabled applications. Moreover, it proposes a novel framework able to dynamically manage multi-cloud, fog, and edge resources and to deploy data-intensive applications developed using the FaaS paradigm. The proposed framework leverages the FaaS paradigm in a way that improves the average service response time of data-intensive applications by a factor of three regardless of the underlying multi-cloud, fog, and edge resource infrastructure.

NoSQL Databases

2014 ◽  
pp. 186-215 ◽  
Author(s):  
Ganesh Chandra Deka
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Processing ◽  
Open Source ◽  
Data Storage ◽  
Big Data Processing ◽  
Nosql Databases ◽  
Data Intensive ◽  
Huge Data ◽  
Data Intensive Applications

NoSQL databases are designed to meet the huge data storage requirements of cloud computing and big data processing. NoSQL databases have lots of advanced features in addition to the conventional RDBMS features. Hence, the “NoSQL” databases are popularly known as “Not only SQL” databases. A variety of NoSQL databases having different features to deal with exponentially growing data-intensive applications are available with open source and proprietary option. This chapter discusses some of the popular NoSQL databases and their features on the light of CAP theorem.

NoSQL Databases

2015 ◽  
pp. 109-152 ◽  
Author(s):  
Mainak Adhikari ◽  
Sukhendu Kar
Keyword(s):  
Cloud Computing ◽  
Social Networking ◽  
Data Storage ◽  
Social Networking Sites ◽  
Nosql Databases ◽  
Data Intensive ◽  
Huge Data ◽  
Nosql Database ◽  
Real Time Applications ◽  
Data Intensive Applications

NoSQL database provides a mechanism for storage and access of data across multiple storage clusters. NoSQL dabases are finding significant and growing industry to meet the huge data storage requirements of Big data, real time applications, and Cloud Computing. NoSQL databases have lots of advantages over the conventional RDBMS features. NoSQL systems are also referred to as “Not only SQL” to emphasize that they may in fact allow Structured language like SQL, and additionally, they allow Semi Structured as well as Unstructured language. A variety of NoSQL databases having different features to deal with exponentially growing data intensive applications are available with open source and proprietary option mostly prompted and used by social networking sites. This chapter discusses some features and challenges of NoSQL databases and some of the popular NoSQL databases with their features on the light of CAP theorem.

scholarly journals Optimized Task Scheduling for Cloud Computing using PSO and GA

2019 ◽  
Vol 8 (2S4) ◽  
pp. 608-611
Keyword(s):  
Cloud Computing ◽  
Particle Swarm Optimization ◽  
Heuristic Algorithms ◽  
Particle Swarm ◽  
It Infrastructure ◽  
Swarm Optimization ◽  
Platform As A Service ◽  
Data Intensive ◽  
Model Cloud ◽  
Data Intensive Applications

Cloud computing started with risk-free concept: Let someone else take the ownership of setting up of IT infrastructure and let end-users tap into it, paying only for what is been used. A service offering computation resources is frequently referred to as Infrastructure as a Service (IaaS) and Software as a Service (SaaS). An environment used for construction, deployment, and management of applications is called PaaS (Platform as a Service). With the advent of cloud computing, resizable infrastructure for data analysis is now available to everyone via an on-demand maybe free model. Cloud computing totally depends on the internet to deliver its services to the users. In the modern computing environment where the amount of data to be processed is increasing day by day, the costs involved in the transmission and execution of such amount of data is mounting significantly. So there is a requirement of appropriate scheduling of tasks which will help to manage the escalating costs of data intensive applications. In this paper, we propose an algorithm that uses particle swarm optimization to schedule the tasks to get maximum benefits from the resources. The benefit can be expressed in terms of increased resource usage, minimized response time, minimize overall cost incurred. We will then schedule the tasks using genetic algorithm and compare the results given by particle swarm optimization as to what extent they are similar and which gives better results. Both algorithms are heuristic algorithms hence result may vary

Data Intensive Cloud Computing

Big Data ◽  
2016 ◽  
pp. 639-654
Author(s):  
Jayalakshmi D. S. ◽  
R. Srinivasan ◽  
K. G. Srinivasa
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Cluster Computing ◽  
Resource Provisioning ◽  
Data Intensive ◽  
Scientific Value ◽  
Data Intensive Applications ◽  
Cloud Applications ◽  
Problem Data ◽  
Huge Challenge

Processing Big Data is a huge challenge for today's technology. There is a need to find, apply and analyze new ways of computing to make use of the Big Data so as to derive business and scientific value from it. Cloud computing with its promise of seemingly infinite computing resources is seen as the solution to this problem. Data Intensive computing on cloud builds upon the already mature parallel and distributed computing technologies such HPC, grid and cluster computing. However, handling Big Data in the cloud presents its own challenges. In this chapter, we analyze issues specific to data intensive cloud computing and provides a study on available solutions in programming models, data distribution and replication, resource provisioning and scheduling with reference to data intensive applications in cloud. Future directions for further research enabling data intensive cloud applications in cloud environment are identified.

scholarly journals Cloud based Resource Scheduling Methodology for Data-Intensive Smart Cities and Industrial Applications

2021 ◽  
Vol 22 (2) ◽  
Author(s):  
Shiming Ma ◽  
Jichang Chen ◽  
Yang Zhang ◽  
Anand Shrivastava ◽  
Hari Mohan
Keyword(s):  
Cloud Computing ◽  
Large Scale ◽  
Smart Cities ◽  
Resource Planning ◽  
Cuckoo Search ◽  
Industrial Applications ◽  
Planning And Scheduling ◽  
Data Intensive ◽  
Management Platform ◽  
Data Intensive Applications

For the data-intensive applications, resource planning and scheduling has become an important part for smart cities. The cloud computing techniques are being used for planning and scheduling of resources in data-intensive applications. The regular methodologies being used are adequately successful for giving the asset allotment yet they do not provide time effectiveness during task execution. This article presents an effective and time prioritization based smart resource management platform employing the Cuckoo Search based Optimized Resource Allocation (CSO-RA) methodology. The opensource JStorm platform is utilized for dynamic asset planning while using big data analytics and the outcomes of the experimentation are observed using various assessment parameters. The proposed (CSO-RA) system is compared with the current methodologies like particle swarm optimization (PSO), ant colony optimization (ACO) and genetic algorithm (GA) based optimization methodologies and the viability of the proposed framework is established. The percentage of optimality observed for CSO-RA algorithm is 97\% and overall resource deployment rate of 28\% is achieved using CSO-RA method which is comparatively much better than PSO, GA and ACO conventional algorithms. Feasible outcomes are obtained by using the CSO-RA methodology for cloud computing based large scale optimization-based data intensive industrial applications.

Data Intensive Cloud Computing

2015 ◽  
pp. 305-320
Author(s):  
Jayalakshmi D. S. ◽  
R. Srinivasan ◽  
K. G. Srinivasa
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Cluster Computing ◽  
Resource Provisioning ◽  
Data Intensive ◽  
Scientific Value ◽  
Data Intensive Applications ◽  
Cloud Applications ◽  
Problem Data ◽  
Huge Challenge

Processing Big Data is a huge challenge for today's technology. There is a need to find, apply and analyze new ways of computing to make use of the Big Data so as to derive business and scientific value from it. Cloud computing with its promise of seemingly infinite computing resources is seen as the solution to this problem. Data Intensive computing on cloud builds upon the already mature parallel and distributed computing technologies such HPC, grid and cluster computing. However, handling Big Data in the cloud presents its own challenges. In this chapter, we analyze issues specific to data intensive cloud computing and provides a study on available solutions in programming models, data distribution and replication, resource provisioning and scheduling with reference to data intensive applications in cloud. Future directions for further research enabling data intensive cloud applications in cloud environment are identified.

GPU-Accelerated Cloud Computing for Data-Intensive Applications

2014 ◽  
pp. 105-129 ◽  
Author(s):  
Baoxue Zhao ◽  
Jianlong Zhong ◽  
Bingsheng He ◽  
Qiong Luo ◽  
Wenbin Fang ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Data Intensive ◽  
Data Intensive Applications
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