# 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, comparing with computational delay and box plot statistics, we can derive that BDPS performs better compared to the others. Hence, BDPS is a desirable and beneficial scheme for the fog-IoT hierarchical overlay mesh architecture. Figure 8: Network efficiency generation scenario over the FW, AH, BF, DA and BDPS algorithms in cloud, fog, and IoT orchestration. Figure 10: Box plot statistics of time delay among existing algorithms and BDPS. Figure 9: Histogram view of computational delay over node execution among existing algorithms and BDPS. ## 6 Discussion

In this work, we employed a scheme to ensure rapid data delivery within the fog-IoT network through a Spark-RDD in-memory processing-based accelerator scheme (i.e., BDPS). We implemented a heterogeneous hierarchical-tree-based mesh overlay network to display the process executions within the distributed fog-IoT orchestrations scenario with the BDPS scheme. In the IoT layer, if a data overflow occurs at the end of an IoT device, the running information in the processing frame is scheduled for verification and validation to get feed-forwarded through BDPS. Our configured scheme acts through timer-based session password generation (SP) from fog to IoT and fog to cloud in terms of data execution. Hence, our scheme is developed as a three-layer architecture, where the central cloud server acts as a hidden backbone (i.e., reservoir) for the fog file verification and validation. The central cloud processes the operations by collecting from the fog session, giving the command to regenerate the queries for validation, and starting the BDPS scheme toward fog-IoT orchestration deliveries. The newly processed stack (i.e., overloaded encapsulate packets) from the fogs are assured, compiled, and delivered across the IoT bases. The fog cluster generates the BDPS process only if there occurs an overflow of process jamming or bottleneck across the fog-IoT network ends. The in-memory Spark-RDD rapid data delivery mechanism supports a high-speed data execution procedure by encapsulating previous jammed packets and distributing them to their intended authority (i.e., dwelled devices) around the fog-IoT network mediums. Thus, the BDPS scheme provides less network latency and delays, resulting in efficient process deliveries across the connected network ends. The individual devices also have a hashing agreement to collect, process, and deliver data toward their respective ends. Thus, the occupied data-link layer safety for less data loss is assured and verified. The SP of each device puts a record over the cloud end if it goes through the BDPS scheme verification of rapid data transfer agreements. Our established BDPS scheme delivers an in-memory RAMA data scheduling and processing strategy that is efficient in providing less delay time over data executions at the hop-to-hop ends. In our new algorithmic scheme--BDPS--the over-flooded IoT network nodes have new hashing sessions of information (e.g., previous and encapsulated ones). The hashing table of the fog-IoT nodes is carried upon packet overflow in a data table. Thus, the BDPS scheme is verified to feedforward the over-flooded packets and convert them into bigger packets while delivering them rapidly within a prioritized manner. In this architecture, the client sides (e.g., IoT and fog) rely on the BDPS scheme, whereas the server-side (e.g., cloud) acts only in a needed situation to verify the scheme. In the data overflow situation, the fog node carries the alert and delivers it via cloud verification to enable BDPS needed for the desired nodes or devices. The intermediate node carriers (i.e., devices in awaiting pipeline for BDPS) act according to their prioritized packets, and hence, deliver themselves for the respective BDPS feed-forwarding data acceleration process within the fog-IoT network orchestrations. The established BDPS scheme continues to generate the number of prioritizing packet schedulers iteratively until the data bottleneck occurs across the desired network ends.