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
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Godspower I. Akawuku, Rapheal O. Okonkwo, Samuel Adejumo, Kennedy C. Okofor (2023).Implication of Data stream offloading (DO) in Fog Computing applications: A Comparative Study. IDOSR JOURNAL OF APPLIED SCIENCES 8(1) 173-190. https://doi.org/10.59298/IDOSR/2023/12.1.7908