Documenting networks is an essential tool for troubleshooting network problems. The documentation details a network's structure and context, serves as a reference and makes network management more effective. Complex network diagrams are hard to document and maintain and are not guaranteed to reflect reality. They contain many superficial icons (e.g., wall, screen and tower). Defining a single coherent network architecture and topology, similar to engineering schematics, has received great interest. We propose a fundamental approach for methodically specifying a network architecture using a diagramming method to conceptualize the network's structure. The method is called a thinging (abstract) machine, through which the network world is viewed as a single unifying element called the thing/machine (thimac), providing the ontology for modeling the network. To test its viability, the thinging-machine-based methodology was applied to an existing computer network to produce a single integrated, diagrammatic representation that incorporates communication, software and hardware. The resultant description shows a viable, coherent depiction that can replace the current methods.

Swarm intelligence meta-heuristic optimization algorithms for optimizing engineering applications have become increasingly popular. The whale optimization algorithm (WOA) is a recent and effective swarm intelligence optimization algorithm that mimics humpback whales' behaviors when optimizing a problem. Applying the algorithm to achieve optimal solutions has shown good results compared to most meta-heuristic optimization algorithms. However, complex applications might require the processing of large-scale computations, which results in down-scaling computational throughput of WOA. Apache Spark, a well-known parallel data processing framework, is the most recent distributed computing framework and has been proven to be the most efficient. In this article, we propose a WOA implementation on top of Apache Spark, represented as SBWOA, to enhance its computational performance while providing higher scalability of the algorithm for handling more complex problems. Compared with the recently reported MapReduce WOA (MR-WOA), and serial implementation of WOA, our approach achieves significant enhancements with respect to computational performance for the highest population size with the maximum number of iterations. SBWOA successfully handles higher-complexity problems which require complex computations.

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