An Aggregate MapReduce Data Block Placement Strategy for Wireless IoT Edge Nodes in Smart Grid

Big data analytics has simplified processing complexity of large dataset in a distributed environment. Many state-of-the-art platforms i.e. smart grid has adopted the processing structure of big data and manages a large volume of data through MapReduce paradigm at distribution ends. Thus, whenever a...

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Bibliographic Details
Published inWireless personal communications Vol. 106; no. 4; pp. 2225 - 2236
Main Authors Qureshi, Nawab Muhammad Faseeh, Siddiqui, Isma Farah, Unar, Mukhtiar Ali, Uqaili, Muhammad Aslam, Nam, Choon Sung, Shin, Dong Ryeol, Kim, Jaehyoun, Bashir, Ali Kashif, Abbas, Asad
Format Journal Article
LanguageEnglish
Published New York Springer US 30.06.2019
Springer Nature B.V
Subjects
Analytics
Big Data
Communications Engineering
Computer Communication Networks
Data analysis
Data management
Datasets
Engineering
Homing
Mathematical analysis
Networks
Nodes
Signal,Image and Speech Processing
Smart grid
Wireless networks
Aggregate MapReduce block placement
HDFS
Wireless IoT edge node
Smart grid
Hadoop
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Summary:Big data analytics has simplified processing complexity of large dataset in a distributed environment. Many state-of-the-art platforms i.e. smart grid has adopted the processing structure of big data and manages a large volume of data through MapReduce paradigm at distribution ends. Thus, whenever a wireless IoT edge node bundles a sensor dataset into storage media, MapReduce agent performs analytics and generates output into the grid repository. This practice has efficiently reduced the consumption of resources in such a giant network and strengthens other components of the smart grid to perform data analytics through aggregate programming. However, it consumes an operational latency of accessing large dataset from a central repository. As we know that, smart grid processes I/O operations of multi-homing networks, therefore, it accesses large datasets for processing MapReduce jobs at wireless IoT edge nodes. As a result, aggregate MapReduce at wireless IoT edge node produces a network congestion and operational latency problem. To overcome this issue, we propose Wireless IoT Edge-enabled Block Replica Strategy (WIEBRS), that stores in-place, partition-based and multi-homing block replica to respective edge nodes. This reduces the delay latency of accessing datasets for aggregate MapReduce and increases the performance of the job in the smart grid. The simulation results show that WIEBRS effective decreases operational latency with an increment of aggregate MapReduce job performance in the smart grid.
ISSN:0929-6212
1572-834X
DOI:10.1007/s11277-018-5936-6