scholarly journals SMART RESOURCE USAGE PREDICTION USING CLOUD COMPUTING FOR MASSIVE DATA PROCESSING SYSTEMS

2019 ◽  
Vol 01 (02) ◽  
pp. 108-118 ◽  
Author(s):  
Abraham Chandy
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Data Processing ◽  
Processing System ◽  
Massive Data ◽  
Network Simulator ◽  
Learning Approaches ◽  
Memory Usage ◽  
Data Processing System ◽  
Massive Data Processing

Resource management plays the vital role in the cloud computing as the requirement for the massive data processing system such as heath sectors, business solutions and the internet of things keeps on increasing in at an exponential range. Allocation of proper and perfect resources remains as the mains reasons for the successful computation of the applications. However the conventional resources management methodologies, that totally depends on the simple heuristic based methods fails to accomplish a performance that is predictable. The appropriate resource allocation is directly related to the workload demand prediction as the would help to bring down the cost, time and power and the memory usage. The proposed method in the paper leverages the machine learning approaches to manage the resource allocation in the cloud computing for the massive data processing system, the simulation of the proposed model using the network simulator -2 enables to achieve a better performance and resources utilization at a decreased cost, time, power and memory usage.

scholarly journals Research and Application of Massive Data Processing in Oil Services

2012 ◽  
Vol 6-7 ◽  
pp. 1036-1040
Author(s):  
Bao An Li
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Internet Of Things ◽  
Data Processing ◽  
Rapid Growth ◽  
Massive Data ◽  
Oil Drilling ◽  
Data Problem ◽  
Traditional Industry ◽  
Massive Data Processing

Big data problem has caused widespread concern from industry to academia in recent years. As the amount of data produced by various industries and sectors of rapid growth, increasing demands on data processing and analysis capabilities, how to face the challenges of data, discover new opportunities, the issue has received wide attention. As a traditional industry, the oil drilling or refinery enterprise is facing the operational status of the system to produce large amounts of data. This text introduced an approach to massive data processing for oil enterprise based on cloud computing and Internet of Things.

Design of Big Data Processing System Architecture Based on Hadoop under the Cloud Computing

2014 ◽  
Vol 556-562 ◽  
pp. 6302-6306 ◽  
Author(s):  
Chun Mei Duan
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Processing ◽  
Data Storage ◽  
System Architecture ◽  
Processing System ◽  
System Stability ◽  
Security Model ◽  
Data Processing System ◽  
Big Data Processing

In allusion to limitations of traditional data processing technology in big data processing, big data processing system architecture based on hadoop is designed, using the characteristics of quantification, unstructured and dynamic of cloud computing.It uses HDFS be responsible for big data storage, and uses MapReduce be responsible for big data calculation and uses Hbase as unstructured data storage database, at the same time a system of storage and cloud computing security model are designed, in order to implement efficient storage, management, and retrieval of data,thus it can save construction cost, and guarantee system stability, reliability and security.

scholarly journals Uma análise do consumo de energia de ambientes de processamento de dados massivos em nuvem

2018 ◽  
Author(s):  
Nestor D. O. Volpini ◽  
Vinicius S. Conceição ◽  
Raphael L. Pontes ◽  
Dorgival Guedes
Keyword(s):  
Data Mining ◽  
Cloud Computing ◽  
Big Data ◽  
Data Processing ◽  
Performance Metrics ◽  
Massive Data ◽  
Distributed Environments ◽  
Big Data Applications ◽  
Massive Data Processing ◽  
Data Mining Application

Massive data processing (big-data) related fields and cloud computing have been growing conjointly. Thus, data processing is among the largest resource consumers in datacenters, consuming around 2% of global energy. Comprehension of how elements such as virtualized environments and applications' parallelization degree affect such consumption is therefore an urgent need. This article relies on a monitoring solution that provides performance metrics, data mining application logs, and data produced in distributed environments to assess how power consumption of virtualized big-data applications varies on allocated resources.

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