In the world of data currency for computational transaction from end user to the edge, then to the cloud, this sequence of digitally encoded coherent signals (homogenous and heterogenous data packets) used to transmit or receive information becomes necessary to understudy. The paper seeks to tabularly survey the implication of data stream workload in various applications vis-à-vis Technology Driver, Defects/Limitations and Support for Big Data Stream mobile Computing (BDSMC). Three major databases, Scopus, ScienceDirect and EBSCO, which indexes journals and conferences that are promoted by entities such as IEEE, ACM, SpringerLink, and Elsevier were explored as data sources. Out of the initial 119 papers that resulted from the first search string, 40 papers were found to be relevant to the research concern after implementing the inclusion and exclusion criteria. In conclusion, it was recommended that research efforts should be geared towards developing scalable frameworks and algorithms that will accommodate data stream offloading, effective resource management strategy and workload issues to accommodate the ever-growing size and complexity of data. Keywords: Data stream, Stream computing, Data stream limitations, fog computing

Recently, Enterprises that operate over vast geographical areas uses multiple data centers to collect, store and process data in real time via energy-efficient acquisition, wirelessly transport clients or users data to the cloud. This paper seeks to technically review existing Data Center Networks (DCN) considering BDSMC applications. In this research, related works on Distributed Data Center Networks will be presented. Within the stream computing ecosystems, there are various network models but the pool of possible DCN topologies/architectures to adopt appears little and unfit for the purpose of BDSMC optimization. However, most related Data center architecture will be reviewed. Investigate efforts on both server centric and switch-centric models adaptable to BDSMC network layer. The extent of work done on distributed spine-leaf re-designed server-centric network construction so as to automatically harvest network interconnection into a ‘stellar’ dual-port server-centric SG network; how classical graph-based interconnection network translate network performance similar to generic works for BDSMC ecosystems. Review stellar transformation using the well-studied generalized hypercube family of interconnection networks for BDSMC ecosystems. The literature was searched from the databases: IEEE Xplore Digital Library, Springer Link Digital Library, and Google Scholar, IET Digital Library, Frontiers Library, ACM Digital Library repositories resulting in 98 papers after several eliminations ranging from year 2000-2022. In conclusion, state-of-the-art dual-port server-centric DCNs (FiConn, DCell, DPillar), etc, while looking at possible architectures with excellent comparative performance for BDSMC ecosystems. Research gaps are revealed for further study. Keywords: Data, Center, Network, Data Stream, Mobile Computing and applications