Performance Improvement of Data Analysis of IoT Applications Using Re-Storm in Big Data Stream Computing Platform

2016 ◽  
Vol 22 ◽  
pp. 141-151 ◽  
Author(s):  
Patan Rizwan ◽  
M. Rajasekhara Babu
Keyword(s):  
Energy Efficiency ◽  
Big Data ◽  
Response Time ◽  
Data Stream ◽  
Dynamic Scheduling ◽  
High Energy ◽  
Stream Computing ◽  
Time Data ◽  
Iot Applications ◽  
High Energy Efficiency

Big Data and Internet of Things (IoT) are Two Popular Technical Terms in Current IT Industry. the Analysis of Iot Data Consumes more Energy since it is Huge in Size. this Paper Proposes a Methodology re-Storm that Addresses Energy Issues and Response Time of Iot Applications Data. it Uses Big Data Stream Computing for re-Storm against Existing Method Storm. the Storm Failed to Address Dynamic Scheduling but re-Storm Deals with Energy-Efficient Traffic Aware Resource Scheduling. this Paper Presents a Model that Different Traffic Arriving Rate of Streams re-Storm at Multiple Traffic Levels for High Energy Efficiency, Low Response Time. it Deals at Three Levels, Firstly, a Mathematical Model for High Energy Efficiency, Low Response Time. Secondly, Allocation of Resources Bearing in Mind DVFS (Dynamic Voltage and Frequency Scaling) Methods and Existing Effective Optimal Consolidation Methods. Thirdly, Online Task Allocation Using Hot Swapping Technique, Streaming Graph Optimizing. Finally, the Experimental Results Show that re-Storm has been Improved the Performance 30-40% against Storm for Real Time Data of Iot Applications.

RE-STORM: REAL-TIME ENERGY EFFICIENT DATA ANALYSIS ADAPTING STORM PLATFORM

2016 ◽  
Vol 78 (10) ◽  
Author(s):  
Rizwan Patan ◽  
Rajasekhara Babu M.
Keyword(s):  
Energy Efficiency ◽  
Big Data ◽  
Response Time ◽  
Energy Efficient ◽  
Data Stream ◽  
High Performance ◽  
High Energy ◽  
Streaming Data ◽  
Stream Computing ◽  
High Energy Efficiency

It is necessary to model an energy efficient and stream optimization towards achieve high energy efficiency for Streaming data without degrading response time in big data stream computing. This paper proposes an Energy Efficient Traffic aware resource scheduling and Re-Streaming Stream Structure to replace a default scheduling strategy of storm is entitled as re-storm. The model described in three parts; First, a mathematical relation among energy consumption, low response time and high traffic streams. Second, various approaches provided for reducing an energy without affecting response time and which provides high performance in overall stream computing in big data. Third, re-storm deployed energy efficient traffic aware scheduling on the storm platform. It allocates worker nodes online by using hot-swapping technique with task utilizing by energy consolidation through graph partitioning. Moreover, re-storm is achieved high energy efficiency, low response time in all types of data arriving speeds.it is suitable for allocation of worker nodes in a storm topology. Experiment results have been demonstrated the comparing existing strategies which are dealing with energy issues without affecting or reducing response time for a different data stream speed levels. Finally, it shows that the re-storm platform achieved high energy efficiency and low response time when compared to all existing approaches.

Performance Improvement IoT Applications Through Multimedia Analytics Using Big Data Stream Computing Platforms

2018 ◽  
pp. 200-221
Author(s):  
Rizwan Patan ◽  
Rajasekhara Babu M ◽  
Suresh Kallam
Keyword(s):  
Big Data ◽  
Real Time ◽  
Performance Improvement ◽  
Data Stream ◽  
Real Data ◽  
Stream Computing ◽  
Time Data ◽  
Real Time Data ◽  
Computing Platforms ◽  
Time And Energy

A Big Data Stream Computing (BDSC) Platform handles real-time data from various applications such as risk management, marketing management and business intelligence. Now a days Internet of Things (IoT) deployment is increasing massively in all the areas. These IoTs engender real-time data for analysis. Existing BDSC is inefficient to handle Real-data stream from IoTs because the data stream from IoTs is unstructured and has inconstant velocity. So, it is challenging to handle such real-time data stream. This work proposes a framework that handles real-time data stream through device control techniques to improve the performance. The frame work includes three layers. First layer deals with Big Data platforms that handles real data streams based on area of importance. Second layer is performance layer which deals with performance issues such as low response time, and energy efficiency. The third layer is meant for Applying developed method on existing BDSC platform. The experimental results have been shown a performance improvement 20%-30% for real time data stream from IoT application.

Reactions in Activated Peroxide Systems and their Influences on Bleaching Performance

2020 ◽  
Vol 17 ◽  
Author(s):  
Xiaoyan Wang ◽  
Jinmei Du ◽  
Changhai Xu
Keyword(s):  
Aqueous Solution ◽  
Hydrogen Peroxide ◽  
Energy Efficiency ◽  
Textile Industry ◽  
High Energy ◽  
Side Reactions ◽  
Competitive Reactions ◽  
High Energy Efficiency ◽  
Hydrolysis Of ◽  
Bleach Activators

Abstract:: Activated peroxide systems are formed by adding so-called bleach activators to aqueous solution of hydrogen peroxide, developed in the seventies of the last century for use in domestic laundry for their high energy efficiency and introduced at the beginning of the 21st century to the textile industry as an approach toward overcoming the extensive energy consumption in bleaching. In activated peroxide systems, bleach activators undergo perhydrolysis to generate more kinetically active peracids that enable bleaching under milder conditions while hydrolysis of bleach activators and decomposition of peracids may occur as side reactions to weaken the bleaching efficiency. This mini-review aims to summarize these competitive reactions in activated peroxide systems and their influence on bleaching performance.

High Energy Efficiency and Stability for Photoassisted Aqueous Lithium–Iodine Redox Batteries

2016 ◽  
Vol 1 (4) ◽  
pp. 806-813 ◽  
Author(s):  
Georgios Nikiforidis ◽  
Keisuke Tajima ◽  
Hye Ryung Byon
Keyword(s):  
Energy Efficiency ◽  
High Energy ◽  
High Energy Efficiency

scholarly journals Re-Stream: Real-time and energy-efficient resource scheduling in big data stream computing environments

2015 ◽  
Vol 319 ◽  
pp. 92-112 ◽  
Author(s):  
Dawei Sun ◽  
Guangyan Zhang ◽  
Songlin Yang ◽  
Weimin Zheng ◽  
Samee U. Khan ◽  
...  
Keyword(s):  
Big Data ◽  
Real Time ◽  
Energy Efficient ◽  
Data Stream ◽  
Resource Scheduling ◽  
Stream Computing ◽  
Efficient Resource ◽  
Computing Environments ◽  
Time And Energy

Electrochemical lithium recovery with lithium iron phosphate: What causes performance degradation and how can we improve the stability?

2021 ◽  
Author(s):  
Lei Wang ◽  
Kathleen C Frisella ◽  
Pattarachai Srimuk ◽  
Oliver Janka ◽  
Guido Kickelbick ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
Manganese Oxide ◽  
Lithium Iron Phosphate ◽  
Iron Phosphate ◽  
High Energy ◽  
Performance Degradation ◽  
Electrochemical Processes ◽  
High Energy Efficiency ◽  
The Stability

Electrochemical processes enable fast lithium extraction, for example, from brines, with high energy efficiency and stability. Lithium iron phosphate (LiFePO4) and manganese oxide (λ-MnO2) have usually been employed as the...

High Energy Efficiency of O- Emission from Microstructure Device on YSZ.

2000 ◽  
Vol 33 (6) ◽  
pp. 914-917 ◽  
Author(s):  
Yoshifumi Torimoto ◽  
Kouji Shimada ◽  
Terumasa Nishioka ◽  
Masayoshi Sadakata
Keyword(s):  
Energy Efficiency ◽  
High Energy ◽  
High Energy Efficiency
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