Distributed Database System Optimization for Improved Service Delivery in Mobile and Cloud BigData Applications

Many issues associated with managing centralized database include data isolation, redundancy, inconsistency, and atomicity of updates, among others; however, distributed database implementation over high-performance compute nodes maximizes information value across the networks. Also, analysis of bigdata generated/consumed over the mobile Internet, Internet of Things (IoT) and cloud computations necessitates low-latency reads and updates over cloud clusters. Conventionally, services in distributed systems demand optimized transactions. This paper examines transaction generation over distributed storage pool using suggested reference architectures of fragmentation using hybrid semi-join operations to offer mobility transparency as an additional ingredient of integrity transparency offer of DDBMS. Distributed storage pool is simulated using configured WLAN to activate multiple file transfers concurrently, engaging mobile nodes and large file sizes. Major functionality desired in the storage pool is improvised by storage virtualization whereby a global schema query optimizer effects transaction management to characterized latency-driven throughputs achieved by joint optimization of network and storage virtualization. Measurements and evaluations gave the best overall performance of low-latency reads and updates using the provisioned mobiletransmission control protocol (M-TCP). Appreciable improvement in service delivery is offered using distributed storage pool (DSP) facilitated with hybridized RAID construction and copy mechanisms. Improved response-time and speed-up transmissions evidently showed low-latency read and update transactions, depicting improved service delivery. Evaluating the DDBMS model simulated in the DSP architecture, all complexity (overheads) associated with conventional shared systems were minimized.