scholarly journals Automation of Aircraft Pre-design Using a Versatile Data Transfer and Storage Format in a Distributed Computing Environment

2009 ◽  
Author(s):  
Arne Bachmann ◽  
Markus Kunde ◽  
Markus Litz ◽  
Andreas Schreiber ◽  
Lothar Bertsch
Keyword(s):  
Distributed Computing ◽  
Data Transfer ◽  
Computing Environment ◽  
Storage Format ◽  
And Storage

scholarly journals A brief exploration of fog computing architecture, technologies and challenges

2018 ◽  
Vol 7 (2.7) ◽  
pp. 96
Author(s):  
Ramaiah Challa ◽  
K Kiran Kumar
Keyword(s):  
Cloud Computing ◽  
Distributed Computing ◽  
Data Transfer ◽  
Fog Computing ◽  
Cloud Services ◽  
Rapid Rate ◽  
Computing Architecture ◽  
Efficient Approach ◽  
System Data ◽  
And Storage

Now a day’s IoT systems are being used in rapid rate, so much data is being generated by  massive ubiquitous things handling of that much data is not a simple issue it very critical task. Then again, despite the fact that that distributed computing has filled in as an efficient approach to process and store these information, in any case, challenges, for example, the expanding requests of ongoing or dormancy delicate applications and the impediment of system data transfer capacity, still can't be tackled by utilizing just cloud computing. Accordingly, another computing known as fog computing was proposed as extension of cloud computing. It brings the cloud services that are communication, computation and storage near to edge devices and users so latency can be reduced. In this papers details of fog computing are discussed.

scholarly journals IoT-enabled directed acyclic graph in spark cluster

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Jahwan Koo ◽  
Nawab Muhammad Faseeh Qureshi ◽  
Isma Farah Siddiqui ◽  
Asad Abbas ◽  
Ali Kashif Bashir
Keyword(s):  
Distributed Computing ◽  
Real Time ◽  
Directed Acyclic Graph ◽  
Random Access ◽  
Heterogeneous Data ◽  
The Body ◽  
Computing Environment ◽  
Time Data ◽  
Acyclic Graph ◽  
Sensory Data

Abstract Real-time data streaming fetches live sensory segments of the dataset in the heterogeneous distributed computing environment. This process assembles data chunks at a rapid encapsulation rate through a streaming technique that bundles sensor segments into multiple micro-batches and extracts into a repository, respectively. Recently, the acquisition process is enhanced with an additional feature of exchanging IoT devices’ dataset comprised of two components: (i) sensory data and (ii) metadata. The body of sensory data includes record information, and the metadata part consists of logs, heterogeneous events, and routing path tables to transmit micro-batch streams into the repository. Real-time acquisition procedure uses the Directed Acyclic Graph (DAG) to extract live query outcomes from in-place micro-batches through MapReduce stages and returns a result set. However, few bottlenecks affect the performance during the execution process, such as (i) homogeneous micro-batches formation only, (ii) complexity of dataset diversification, (iii) heterogeneous data tuples processing, and (iv) linear DAG workflow only. As a result, it produces huge processing latency and the additional cost of extracting event-enabled IoT datasets. Thus, the Spark cluster that processes Resilient Distributed Dataset (RDD) in a fast-pace using Random access memory (RAM) defies expected robustness in processing IoT streams in the distributed computing environment. This paper presents an IoT-enabled Directed Acyclic Graph (I-DAG) technique that labels micro-batches at the stage of building a stream event and arranges stream elements with event labels. In the next step, heterogeneous stream events are processed through the I-DAG workflow, which has non-linear DAG operation for extracting queries’ results in a Spark cluster. The performance evaluation shows that I-DAG resolves homogeneous IoT-enabled stream event issues and provides an effective stream event heterogeneous solution for IoT-enabled datasets in spark clusters.

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