Abstract
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.
Highlights
Cloud computing is a new technology to help enterprises and organizations to subcontract information processing services [1], [2]
A new approach based on the genetic algorithm for web service composition in the cloud environment has been proposed by Wang et al [20]
For analyzing the proposed Artificial Neural Networkbased Particle Swarm Optimization (ANN-Particle Swarm Optimization (PSO)) algorithm in the service composition model, first, we have provided a simulation analysis with
Summary
Cloud computing is a new technology to help enterprises and organizations to subcontract information processing services [1], [2]. M. Hosseinzadeh et al.: Hybrid Service Selection and Composition Model an essential challenge in cloud-edge computing. A formal verification method is proposed for a hybrid ANN-based PSO (ANN-PSO) algorithm to improve the reachability rate and execution time of a service composition model in cloud-edge computing. To evaluate the reachability condition of the service composition approach that particular states of the service composition procedure can reachable, the behavioral model of the ANN-PSO constructed as an LTS. Presenting a hybrid ANN-PSO algorithm for a service composition model in cloud-edge computing. Applying a formal verification method to prove the correctness of a hybrid ANN-PSO algorithm. Providing a behavioral modeling method based on LTS to achieve the optimal QoS factors for the proposed ANN-PSO algorithm.
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