TY - JOUR
T1 - BDPS
T2 - An Efficient Spark-Based Big Data Processing Scheme for Cloud Fog-IoT Orchestration
AU - Hossen, Rakib
AU - Whaiduzzaman, Md
AU - Uddin, Mohammed Nasir
AU - Islam, Md Jahidul
AU - Faruqui, Nuruzzaman
AU - Barros, Alistair
AU - Sookhak, Mehdi
AU - Mahi, Md Julkar Nayeen
N1 - Funding Information:
This research is funded through the “ICT Innovation Fund (2016-17): ICT Division, Bangladesh” and also partially supported through the Australian Research Council Discovery Project: DP190100314.
Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2021/12
Y1 - 2021/12
N2 - The Internet of Things (IoT) has seen a surge in mobile devices with the market and technical expansion. IoT networks provide end-to-end connectivity while keeping minimal latency. To reduce delays, efficient data delivery schemes are required for dispersed fog-IoT network orchestrations. We use a Spark-based big data processing scheme (BDPS) to accelerate the distributed database (RDD) delay efficient technique in the fogs for a decentralized heterogeneous network architecture to reinforce suitable data allocations via IoTs. We propose BDPS based on Spark-RDD in fog-IoT overlay architecture to address the performance issues across the network orchestration. We evaluate data processing delays from fog-IoT integrated parts using a depth-first-search-based shortest path node finding configuration, which outperforms the existing shortest path algorithms in terms of algorithmic (i.e., depth-first search) efficiency, including the Bellman–Ford (BF) algorithm, Floyd– Warshall (FW) algorithm, Dijkstra algorithm (DA), and Apache Hadoop (AH) algorithm. The BDPS exhibits low latency in packet deliveries as well as low network overhead uplink activity through a map-reduced resilient data distribution mechanism, better than in BF, DA, FW, and AH. The overall BDPS scheme supports efficient data delivery across the fog-IoT orchestration, outperforming faster node execution while proving effective results, compared to DA, BF, FW and AH, respectively.
AB - The Internet of Things (IoT) has seen a surge in mobile devices with the market and technical expansion. IoT networks provide end-to-end connectivity while keeping minimal latency. To reduce delays, efficient data delivery schemes are required for dispersed fog-IoT network orchestrations. We use a Spark-based big data processing scheme (BDPS) to accelerate the distributed database (RDD) delay efficient technique in the fogs for a decentralized heterogeneous network architecture to reinforce suitable data allocations via IoTs. We propose BDPS based on Spark-RDD in fog-IoT overlay architecture to address the performance issues across the network orchestration. We evaluate data processing delays from fog-IoT integrated parts using a depth-first-search-based shortest path node finding configuration, which outperforms the existing shortest path algorithms in terms of algorithmic (i.e., depth-first search) efficiency, including the Bellman–Ford (BF) algorithm, Floyd– Warshall (FW) algorithm, Dijkstra algorithm (DA), and Apache Hadoop (AH) algorithm. The BDPS exhibits low latency in packet deliveries as well as low network overhead uplink activity through a map-reduced resilient data distribution mechanism, better than in BF, DA, FW, and AH. The overall BDPS scheme supports efficient data delivery across the fog-IoT orchestration, outperforming faster node execution while proving effective results, compared to DA, BF, FW and AH, respectively.
KW - Depth-first search
KW - Efficient data processing
KW - In-memory accelerator
KW - Map reduction
KW - Spark
UR - http://www.scopus.com/inward/record.url?scp=85122803270&partnerID=8YFLogxK
U2 - 10.3390/INFO12120517
DO - 10.3390/INFO12120517
M3 - Article
AN - SCOPUS:85122803270
SN - 2078-2489
VL - 12
JO - Information (Switzerland)
JF - Information (Switzerland)
IS - 12
M1 - 517
ER -