# BDS: An Efficient Spark-Based Big Data Processing Scheme for Cloud Fog-IoT Orchestration
## 4 System Model and Methodology
### Components of the Fog-IoT Hierarchical Overlay Network and BDPS Architecture
However, having a resilient map reduction data architecture, BDPS shows lower packet drops of 11 ms in fog, 10 ms in IoT and 12 ms in cloud, respectively, and the packet drop or data hand-off remains at 0.2-0.3%, respectively, in 1000 packet deliveries. This data execution pattern of BDPS shows gradual consistency within the three-tier data distribution architecture across intended nodes. Less packet drop resembles better data deliveries across the fog-IoT network continuum. The FW, DA and AH algorithms show a gradual increase in packet drop similar to BF throughout the cloud, fog, and IoT orchestration, and employ higher data hand-off. Comparing to BF and others, in terms of packet drop, BDPS performs better, delivering the map reduction data delivery pattern toward the network edges. Figure 6: Network overhead among existing algorithms and BDPS over fog-IoT mesh architecture. Network Efficiency Generation Scenario over the FW, AH, BF, DA and BDPS Algorithms in Cloud, Fog, and IoT Orchestration

Figure 8 depicts that the Bellman-Ford (BF) algorithm has better efficiency in the fog continuum rather than IoT and cloud. BF does not employ data mapping toward intended users. The packet hand-offs are higher in the IoT, and cloud redirects the middleware inconsistency of data processing within the three-tier architecture and fog continuum misplaced stack handlers. BF declares higher efficiency of 12 ms in fog, showing a multiplication of misplaced stack handling of 2.5% and faster process execution in 1000 packet deliveries. However, having a resilient map reduction data architecture, BDPS shows better efficiency, without misplaced data handling, of 12 ms in fog, 10 ms in IoT and 7 ms in cloud, respectively. The misplaced packet handling remains at 1.2% in 1000 packet deliveries. This data execution pattern of BDPS shows consistency within the three-tier data distribution architecture across fog-IoT intended nodes. Less misplaced packet handling resembles better data deliveries across the fog-IoT network continuum among other algorithms, namely FW, DA and AH. FW, DA and AH algorithms show less misplaced data handling around the cloud, fog, and IoT orchestration but cannot deliver resilient data mapping toward the intended edge nodes. Comparing to BF and others, BDPS performs better, delivering map-reduction data delivery patterns toward the network edges. Figure 7: Packet drop generation scenario over the FW, AH, BF, DA and BDPS algorithms in cloud, fog and IoT orchestration. Computational Delay Generation Scenario over the FW, AH, BF, DA and BDPS Algorithms in Cloud, Fog, and IoT Orchestration

Figures 9 and 10 show that the Dijkstra algorithm (DA) has a higher computational delay in the fog-IoT continuum and does not employ data mapping toward edge network intended users. The data hand-offs and packet delays across the edge nodes are higher, which redirects the inconsistency of data processing within the three-tier mesh architecture. The Dijkstra algorithm declares an overall higher computational delay of 45 ms in 40 edge node data executions. Having a resilient map reduction data architecture, the BDPS shows an overall lower computational delay of 2 ms in 3 edge node execution and 26 ms overall computational delay for 40 edge node execution. The Bellman-Ford, Floyd-Warshall and Apache Hadoop algorithms show 66 ms, 85 ms and 80 ms, respectively, for 40 edge node data execution. This data execution pattern of BDPS shows consistency within the three-tier data distribution architecture across intended nodes. Less computational delay from the box plot statistics resembles better data deliveries across the fog-IoT network continuum. The histogram plot shows that we have maximum edge node data delivery in 5-40 ms. That supports our BDPS higher data delivery capacity across the edge nodes. Considering the FW, DA, AH and BF algorithms, in terms of computational delay and having a resilient data delivery architecture, BDPS is a better choice for fog-IoT-based data orchestration.