# BDS: An Efficient Spark-Based Big Data Processing Scheme for Cloud Fog-IoT Orchestration
## 1 Introduction  
The growing IoT network [1, 2, 3], end-to-end packet distribution, finds mesh networks for end devices, along with short-range communication [4, 5, 6]. The anonymous users and excessive delays in heterogeneous network hops make it challenging to deliver data with better scalability and elasticity [7, 8, 9]. Increasing user loads within IoT connections raises latency-based delays toward gateway endpoints [10, 11, 12]. When distributed processes have been merged and aligned to continue [13, 14] through the network gateway, they occur due to delayed package delivery and inconsistent planning of processes for high memory gain at the edge of the network nodes [15, 16]. Fog computing reduces latency problems by distributing processes across different shared hops, splitting them at their right ends, and assigning network data within a timed frame (i.e., time-based or peer-to-peer programming manner). The end devices or the IoT network receive their collected data by managing a lower latency period and sharing defined (i.e., planned) workloads in federated fog-IoT orchestrations [17]. In connection with these IoT gateways, custom fogs achieve a lower memory gain through less delay-based data provision [18, 19] across these IoT gateways. When processing large amountsof data (e.g., big data), IoT gateways experience problems with the distribution to the intended users, and errors occur during data processing. The middleware fog architecture of the middleware can also help reduce the overhead of cloud data processing as the data delivery (i.e., mapping) is loaded onto validated users [20]. Typical big data architecture works with Apache Hadoop-based data mapping deliveries concerning veracity, validity, velocity, variability, volatility, and vulnerability. However, Hadoop makes it difficult and increases additional latency-based delay when considering validity, variability, and volatility across data delivery mappings [21]. Hadoop is highly volatile, and hence, a resilient data delivery mechanism is a reasonable need for the appropriate data validation. Thus, for efficient data delivery at the IoT hop ends, a resilient data distribution-based mapping mechanism [22] is a better choice, and fog service deliverables (i.e., gateways, and microservice extenders) act here as a beneficiary helper for the three-tier architecture [23]. The overlay tree topology for interconnects works as mesh-based peer-to-peer data sharing [24; 25], a master-slave and slave-master pattern. IoT networks often encounter a delay in the two-way verified communication of latencies [26] at the hop-to-hop ends offloading [27], causing latencies based on delay at the network ends [28] of the devices depending on their data delivery priorities, end-to-end [29; 30]. Fog networks have difficulty in making appropriate decisions for end-to-end data distribution. Therefore, process planners at the fog endpoints [31; 32] must access and share through the IoT data allocation procedures. A valid developed schema based on proper planning resource allocation can fine-tune fog services [33], enabling integrated fog-IoT orchestrations. Without the verification from the cloud, the fog cluster cannot validate the scheduled data distribution. Hence, for rapid data delivery and efficient in-memory scheduling across the network ends, a low latency-based data distribution scheme is still needed for fog-IoT overlay networks. We propose BDPS, a delay optimization scheme to run with in-memory Spark processing to reduce the Apache Hadoop [34] extra delay problem for the verification, validation, and execution of files caused by distribution of data processing. \"BDPS\" can manage data processing [35] for architectures or massive storage modules to fast process scheduling to run different frame intervals in a single frame slot and minimize the end-to-end hop latency problem.