Cloud computing systems have become widely used for Big Data processing, providing access to a wide variety of computing resources and a greater distribution between multi-clouds. This trend has been strengthened by the rapid development of the Internet of Things (IoT) concept. Virtualization via virtual machines and containers is a traditional way of organization of cloud computing infrastructure. Containerization technology provides a lightweight virtual runtime environment. In addition to the advantages of traditional virtual machines in terms of size and flexibility, containers are particularly important for integration tasks for PaaS solutions, such as application packaging and service orchestration. In this paper, we overview the current state-ofthe-art of virtualization and containerization approaches and technologies in the context of Big Data tasks solution. We present the results of studies which compare the efficiency of containerization and virtualization technologies to solve Big Data problems. We also analyze containerized and virtualized services collaboration solutions to support automation of the deployment and execution of Big Data applications in the cloud infrastructure.
<p class="0abstract">Smart industry systems are based on integrating historical and current data from sensors with physical and digital systems to control product states. For example, Digital Twin (DT) system predicts the future state of physical assets using live simulation and controls the current state through real-time feedback. These systems rely on the ability to process big data stream to provide real-time responses. For, example it is estimated that one autonomous vehicle (AV) could produce 30 terabytes of data per day. AV will not be on the road before using an effective way to managing its big data and solve latency challenges. Cloud computing failed in the latency challenge, while Fog computing addresses it by moving parts of the computations from the Cloud to the edge of the network near the asset to reduce the latency. This work studies the challenges in data stream processing for DT in a fog environment. The challenges include fog architecture, the necessity of loosely-coupling design, the used virtual machine versus container, the stateful versus stateless operations, the stream processing tools, and live migration between fog nodes. The work also proposes a fog computing architecture and provides a vision of the prerequisites to meet the challenges.</p>
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