GA for QoS Satisfaction Degree Optimal Web Service Composition Selection Model

Chen, Minghua; Wang, Qingjun; Sun, Wei; Song, Xin; Chu, Na

doi:10.1109/besc48373.2019.8962994

Cited by 7 publications

(9 citation statements)

References 8 publications

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“…The three strategies presented in the articles [ 46 , 54 , 55 ]. are compared with our proposed method.…”

Section: Experiments Resultsmentioning

confidence: 99%

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A Quality-of-Service-Aware Service Composition Method in the Internet of Things Using a Multi-Objective Fuzzy-Based Hybrid Algorithm

Hamzei,

Khandagh,

Jafari Navimipour

2023

Sensors

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The Internet of Things (IoT) represents a cutting-edge technical domain, encompassing billions of intelligent objects capable of bridging the physical and virtual worlds across various locations. IoT services are responsible for delivering essential functionalities. In this dynamic and interconnected IoT landscape, providing high-quality services is paramount to enhancing user experiences and optimizing system efficiency. Service composition techniques come into play to address user requests in IoT applications, allowing various IoT services to collaborate seamlessly. Considering the resource limitations of IoT devices, they often leverage cloud infrastructures to overcome technological constraints, benefiting from unlimited resources and capabilities. Moreover, the emergence of fog computing has gained prominence, facilitating IoT application processing in edge networks closer to IoT sensors and effectively reducing delays inherent in cloud data centers. In this context, our study proposes a cloud-/fog-based service composition for IoT, introducing a novel fuzzy-based hybrid algorithm. This algorithm ingeniously combines Ant Colony Optimization (ACO) and Artificial Bee Colony (ABC) optimization algorithms, taking into account energy consumption and Quality of Service (QoS) factors during the service selection process. By leveraging this fuzzy-based hybrid algorithm, our approach aims to revolutionize service composition in IoT environments by empowering intelligent decision-making capabilities and ensuring optimal user satisfaction. Our experimental results demonstrate the effectiveness of the proposed strategy in successfully fulfilling service composition requests by identifying suitable services. When compared to recently introduced methods, our hybrid approach yields significant benefits. On average, it reduces energy consumption by 17.11%, enhances availability and reliability by 8.27% and 4.52%, respectively, and improves the average cost by 21.56%.

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“…The three strategies presented in the articles [ 46 , 54 , 55 ]. are compared with our proposed method.…”

Section: Experiments Resultsmentioning

confidence: 99%

“…Chen, Wang [ 55 ] introduced an optimal objective for web service composition selection, incorporating the concept of QoS satisfaction degree. The authors proposed the use of a genetic algorithm to solve this problem and provided test results that demonstrated the feasibility and effectiveness of their proposed solution.…”

Section: Related Workmentioning

confidence: 99%

A Quality-of-Service-Aware Service Composition Method in the Internet of Things Using a Multi-Objective Fuzzy-Based Hybrid Algorithm

Hamzei,

Khandagh,

Jafari Navimipour

2023

Sensors

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“…We applied the QWS (The Quality of Service for Web Services Data set) data set [39] that contains 2500 web services. To show the feasibility of our method, some basic algorithms such as the Genetic Algorithm (GA) [40], PSO [41] and GAPSO algorithm are compared. Also, for showing the evaluation of availability, response time, and price, the number of applied services is 10, 30, 50, 70, and 100 on the QWS dataset with 100 iterations.…”

Section: A Simulation Resultsmentioning

confidence: 99%

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

et al. 2020

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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 smallscale 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 Networkbased Particle Swarm Optimization (ANN-PSO) Algorithm presented to enhance the QoS factors in cloudedge 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.INDEX TERMS Cloud-edge computing, Internet of Things, service composition, formal verification, quality of service, artificial neural network, and particle swarm optimization.

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“…Many works have applied evolution algorithms to the WSCP, such as the intelligent water drops algorithm [18], artificial bee colony optimization [19,20], genetic programming [21,22], particle swarm optimization [23], grey wolf optimizer [24,25], and ant colony optimization [26,27]. To overcome the limitations of single evolutionary algorithms, some studies have utilized hybrid heuristic algorithms.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Strategy Improved SPEA2 Algorithm for Multi-Objective Web Service Composition

Wang,

Du,

Chen

2024

Applied Sciences

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Service-oriented architectures have become increasingly prevalent in internet application design, leading to a proliferation of services. Managing and composing these web services pose a classic non-deterministic polynomial-time hard problem. The web service composition problem (WSCP) based on the input–output model (IOM) involves the automatic composition of services without relying on a pre-defined workflow. Multi-objective evolutionary algorithms offer a solution that goes beyond simple weighted average calculations for quality of service, catering to diverse user needs in service composition. This paper introduces a multi-objective heuristic algorithm based on the SPEA2 called MSPEA2+. Specific modifications are incorporated in MSPEA2+ to support the breeding, crossover, and mutation processes, tailored for IOM-based WSCP. Validating the algorithm performance through finding suitable combination results for each task in the WSC-08 dataset, our experiments showed that MSPEA2+ has better iterative efficiency than existing multi-objective methods and outperforms them in terms of quality of service. MSPEA2+ achieved the lowest IGD value across all seven tasks, an improvement of 81.7% IGD value compared with the SPEA2 algorithm in WSC-0804.

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GA for QoS Satisfaction Degree Optimal Web Service Composition Selection Model

Cited by 7 publications

References 8 publications

A Quality-of-Service-Aware Service Composition Method in the Internet of Things Using a Multi-Objective Fuzzy-Based Hybrid Algorithm

A Quality-of-Service-Aware Service Composition Method in the Internet of Things Using a Multi-Objective Fuzzy-Based Hybrid Algorithm

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

A Hybrid Strategy Improved SPEA2 Algorithm for Multi-Objective Web Service Composition

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