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Cloud-edge computing is a hybrid model of computing where resources and services provided via the Internet of Things (IoT) between large-scale and long-term data informs of the cloud layer and small-scale and short-term data as edge layer. The main challenge of the cloud service providers is to select the optimal candidate services that are doing the same work but offer different Quality of Service (QoS) values in IoT applications. Service composition in cloud-edge computing is an NP-hard problem; therefore, many meta-heuristic methods introduced to solve this issue. Also, the correctness of meta-heuristic and machine learning algorithms for evaluating service composition problem should be proven using formal methods to guarantee functional and non-functional specifications. In this paper, a hybrid Artificial Neural Network-based Particle Swarm Optimization (ANN-PSO) Algorithm presented to enhance the QoS factors in cloud-edge computing. To illustrate the correctness and improve the reachability rate of candidate composited services and QoS factors for the proposed hybrid algorithm, we present a formal verification method based on a labeled transition system to check some critical Linear Temporal Logics (LTL) formulas. The experimental results illustrated the high performance of the proposed model in terms of minimum verification time, memory consumption, and guaranteeing critical specifications rules as the Linear Temporal Logic (LTL) formulas. Also, we observed that the proposed model has optimal response time, availability, and price with maximum fitness function value than other service composition algorithms.

A Hybrid Service Selection and Composition Model for Cloud-Edge Computing in the Internet of Things