Abstract
With the rapid development of big data technology and the Internet, the requirements of human activities for data are getting higher and higher, and the increasing data volume has a high demand for data processing. The paradigm of the Internet of Things (IoT) has become a key component for edge-cloud-hybrid systems. In the edge environment, multiple IoT-data-intensive services will form a service combination. Due to the data transmission between different service components, there is a huge transmission delay in the process of IoT data transmission, which will affect the performance of the entire system. Therefore, by regarding the reduction of transmission delay as our optimization goal, we put forward iDiSC: a new heuristic approach for IoT-data-intensive service component deployment in the edge-cloud-hybrid system. We also design the iDiSC model, then we optimize the model to select the optimal deployment scenario with the minimum guaranteed latency. Through a series of experiments, compared to the genetic algorithm and the simulated annealing algorithm, the experimental results show that the iDiSC algorithm has higher efficiency and performance for the problem of data-intensive service component deployment problem in the edge-cloud-hybrid environment.
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