Parameter Estimation-Aided Edge Server Selection Mechanism for Edge Task Offloading

As the extension of cloud computing, edge computing has witnessed remarkable progress in the last few years. In edge computing, task offloading from edge device to edge server is becoming a promising technique that substantially enhances the quality of service for edge devices. Nevertheless, appropriate edge server selection for task offloading remains a challenge if multiple edge servers are selectable nearby. In this article, we propose a parameter estimation-aided edge server selection scheme for task offloading (PEESS) with multiple potential edge servers. The proposed scheme jointly leverages Baum-Welch algorithm and forward algorithm of hidden Markov model. That is, PEESS leverages Baum-Welch algorithm to estimate the parameters of potential edge servers based on their time-sequential incoming task records. Subsequently, the forward algorithm is utilized for the probability calculation of the observations based on the estimated parameters. Eventually, the probability calculation results are sorted in descending order, and the edge device selects the target edge server based on the sequence of sending requests to the available edge servers from optimal to suboptimal. Extensive simulation results demonstrate that PEESS outperforms other conventional and state-of-the-art edge server selection schemes in terms of time consumption and energy consumption.