scholarly journals Smarter Round Robin Scheduling Algorithm for Cloud Computing and Big Data

2018 ◽  
Vol Special Issue on Scientific... ◽  
Author(s):  
Hicham GIBET TANI ◽  
Chaker EL AMRANI
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Scheduling Algorithm ◽  
Growth Management ◽  
Power Scheduling ◽  
Processing Power ◽  
Business Market ◽  
High Efficient ◽  
Exploitation Model ◽  
Computing Models

Cloud Computing and Big Data are the upcoming Information Technology (IT) computing models. These groundbreaking paradigms are leading IT to a new set of rules that aims to change computing resources delivery and exploitation model, thus creating a novel business market that is exponentially growing and attracting more and more investments from both providers and end users that are looking forward to make profits from these innovative models of computing. In the same context, researchers and investigators are wrestling time in order to develop, test and optimize Cloud Computing and Big Data platforms, whereas several studies are ongoing to determine and enhance the essential aspects of these computing models especially compute resources allocation. The processing power scheduling is crucial when it comes to Cloud Computing and Big Data because of the data growth management and delivery design proposed by these new computing models, that requires faster responses from platforms and applications. Hence originates the importance of developing high efficient scheduling algorithms that are compliant with these computing models platforms and infrastructures requirement. Cloud Computing et Big Data sont les prochains modèles informatiques. Ces paradigmes révolutionnaires conduisent l'informatique à un nouveau jeu de règles qui vise à changer la livraison des ressources informatiques et le modèle d'exploitation, créant ainsi un monde d'affaires nouveau qui croît de façon exponentielle et attire de plus en plus d'investissements des fournisseurs et des utilisateurs finaux qui attendent Amener profit de ces modèles innovants de l'informatique. Dans le même contexte, les chercheurs combattent pour développer, tester et optimiser les plates-formes Cloud Computing et Big Data, alors que plusieurs études sont en cours pour déterminer et améliorer les aspects essentiels de ces modèles informatiques, en particulier l'allocation des ressources. La planification de la puissance de traitement est cruciale quand il s'agit de Cloud Computing et Big Data en raison de la gestion de la croissance des données et la conception de livraison proposée par ces nouveaux modèles informatiques, qui nécessite des réponses plus rapides des plates-formes et des applications. D'où l'origine de l'importance de développer des algorithmes d'ordonnancement efficaces qui sont conformes à ces plates-formes de modèles informatiques et aux exigences d'infrastructure.

Big Data in Massive Parallel Processing

2018 ◽  
pp. 276-302 ◽  
Author(s):  
Vijayalakshmi Saravanan ◽  
Anpalagan Alagan ◽  
Isaac Woungang
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Storage ◽  
Mobile Phones ◽  
Scientific Data ◽  
Computational Power ◽  
Data Intensive ◽  
Heterogeneous Devices ◽  
Processing Power ◽  
Massive Parallel Processing

With the advent of novel wireless technologies and Cloud Computing, large volumes of data are being produced from various heterogeneous devices such as mobile phones, credit cards, and computers. Managing this data has become the de-facto challenge in the current Information Systems. According to Moore's law, processor speeds are no longer doubling, the processing power also continuing to grow rapidly which leads to a new scientific data intensive problem in every field, especially Big Data domain. The revolution of Big Data lies in the improved statistical analysis and computational power depend on its processing speed. Hence, the need to put massively multi-core systems on the job is vital in order to overcome the physical limits of complexity and speed. It also arises with many challenges such as difficulties in capturing massive applications, data storage, and analysis. This chapter discusses some of the Big Data architectural challenges in the perspective of multi-core processors.

scholarly journals ENHANCED EDGE MODEL FOR BIG DATA IN THE INTERNET OF THINGS BASED APPLICATIONS

2019 ◽  
Vol 01 (01) ◽  
pp. 63-73 ◽  
Author(s):  
Pasumpon Pandian A
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Internet Of Things ◽  
Data Flow ◽  
The Internet ◽  
Network Simulator ◽  
Efficient Alternative ◽  
The Internet Of Things ◽  
Vast Range ◽  
Computing Models

The edge computing that is an efficient alternative of the cloud computing, for handling of the tasks that are time sensitive, has become has become very popular among a vast range of IOT based application especially in the industrial sides. The huge amount of information flow and the services requisition from the IOT has made the traditional cloud computing incompatible on the time of big data flow. So the paper proposes an enhanced edge model for the by incorporating the artificial intelligence along with the integration of caching to the edge for handling of the big data flow in the applications of the internet of things. The performance evaluation of the same in the network simulator 2 for enormous flow of task that are time sensitive , evinces that the proposed method has a minimized delay compared the traditional cloud computing models.

Task Scheduling and Resource Allocation of Cloud Computing Based on QoS

2014 ◽  
Vol 915-916 ◽  
pp. 1382-1385 ◽  
Author(s):  
Bai Lin Pan ◽  
Yan Ping Wang ◽  
Han Xi Li ◽  
Jie Qian
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Computing System ◽  
Computing Power ◽  
Power Scheduling ◽  
Cloud Computing System ◽  
User Demand

With the enlargement of the scope of cloud computing application, the number of users and types also increases accordingly, the special demand for cloud computing resources has also improved. Cloud computing task scheduling and resource allocation are key technologies, mainly responsible for assigning user jobs to the appropriate resources to perform. But the existing scheduling algorithm is not fully consider the user demand for resources is different, and not well provided for different users to meet the requirements of its resources. As the demand for quality of service based on cloud computing and cloud computing original scheduling algorithm, the computing power scheduling algorithm is proposed based on the QoS constraints to research the cloud computing task scheduling and resource allocation problems, improving the overall efficiency of cloud computing system.

scholarly journals ELASTIC CLOUD COMPUTING APPROACH FOR BIG DATA ANALYTICS

2019 ◽  
Vol 2019 (02) ◽  
pp. 87-96
Author(s):  
Suma V
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Analytics ◽  
Information Technologies ◽  
Big Data Analytics ◽  
Resource Provisioning ◽  
Network Bandwidth ◽  
Processing Power ◽  
Industrial Operations ◽  
Computing Approach

The latest developments in the communication and the information technologies have turned out to be the foundation for the emergence of the industrial developments and progress in the business causing digital transformation in the industrial and the business operations. The clubbing of the internet and the information technology along with the tangible things that are the responsible for the industrial operations, generate a huge set of data that requires enormous network bandwidth, high processing power and accessible resolutions. So the paper presents the elastic cloud computing approach to bring down the complexities in deploying, cost of the groundwork and maintenance and provide an automated resource provisioning according to the demands. This method increases the amount of the computing power available, network properties and the storage. The experimental results for the novel elastic approach for the big data analytics is obtained by the simulation through MATLAB in terms response time, processing power and cost.

A Novel Improved Grey Wolf Optimization Algorithm Based Resource Management Strategy for Big Data Systems

2021 ◽  
Vol 18 (4) ◽  
pp. 1227-1232
Author(s):  
L. R. Aravind Babu ◽  
J. Saravana Kumar
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Resource Management ◽  
Scheduling Algorithm ◽  
Data Systems ◽  
Grey Wolf ◽  
End User ◽  
Grey Wolf Optimization ◽  
Cloud Resource ◽  
Big Data Systems

Presently, big data is very popular, since it finds helpful in diverse domains like social media, E-commerce transactions, etc. Cloud computing offers services on demand, broader networking access, source collection, quick flexibility and calculated services. The cloud sources are usually different and the application necessities of the end user are rapidly changing from time to time. So, the resource management is the tedious process. At the same time, resource management and scheduling plays a vital part in cloud computing (CC) results, particularly while the environment is employed in the analysis of big data, and minimum predictable workload dynamically enters into the cloud. The identification of the optimal scheduling solutions with diverse variables in varying platform still remains a crucial problem. Under cloud platform, the scheduling techniques should be able to adapt the changes quickly and according to the input workload. In this paper, an improved grey wolf optimization (IGWO) algorithm with oppositional learning principle has been important to carry out the scheduling task in an effective way. The presented IGWO based scheduling algorithm achieves optimal cloud resource usage and offers effective solution over the compared methods in a significant way.

Research on Dynamic Scheduling of Grid Monitoring Data Processing Tasks Based Storm

2014 ◽  
Vol 651-653 ◽  
pp. 1051-1055
Author(s):  
Shao Guang Yuan ◽  
Yong Li Zhu ◽  
Guo Liang Zhou ◽  
Ming Kun Wang
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Electric Power ◽  
Real Time ◽  
Scheduling Algorithm ◽  
Electric Power Industry ◽  
Power Industry ◽  
Fair Share ◽  
Computing Platform ◽  
Cloud Computing Platform

Development of smart grid spawned the big data in electric power industry, the cloud computing platform provided the solution for the big data in electric power industry, it has a significant effect for batch jobs, but its real-time is not guaranteed. For the real-time problem of cloud computing platform, the Storm platform will be introduced to monitor the grid power. This paper studies the fair share scheduling algorithm under the Storm platform. It introduced the concept of Storm framework briefly, then, proposed the fair share scheduling algorithm according to the lack of current Storm scheduling algorithm, finally, the experiment proved that the scheduling algorithm based on fair share improved the resource utilization of Storm cluster and reduced the processing delay of the data.

scholarly journals From Networked Robotics to Cloud and Big Data Supercharged Robotics: A Survey and Analysis

2021 ◽  
Author(s):  
Muhammed Tawfiq Chowdhury ◽  
Feng Yan
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Dominant Factor ◽  
Multiple Robots ◽  
Networked Robots ◽  
Survey Paper ◽  
Cloud Robotics ◽  
Networked Robotics ◽  
Processing Power ◽  
Cloud Servers

Abstract In recent years, with the prosperity of big data and cloud computing, robotics is evolving from conventional networked robotics to internet-scale connected, big data driven, and cloud resources supercharged multi-robot systems. In this survey paper, we present a survey of an advancing, pioneering, and multi-disciplinary field of research at the intersection of wireless sensor networks (WSN), robotics and big data. We discuss the concepts of networked robots. Networked robots refer to multiple robots working together in coordination with different types of embedded computers, sensors, and human users. Networked robotics allows multiple robots and supporting entities to execute tasks that are well beyond the capabilities of a single robot. The recent initiation of cloud technologies is opening new prospects for the provisioning of advanced robotic services based on the cooperation of some connected robots, smart environments and devices powered by the huge computational and storage capability of the cloud servers. We have recently witnessed the emergence of cloud computing on one hand and robotics platforms on the other hand. These two areas have been merging and resulting in the cloud robotics model to offer more distant services. Since networked robots require high computational and processing power, big data is becoming a dominant factor in networked and cloud robotics. This survey paper elaborates the primary concepts of networked and cloud robotics, their applications, challenges as well as the importance of big data in robotics, particularly, networked and cloud robotics and the noteworthy works in these areas.

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