Cloud service composition using minimal unsatisfiability and genetic algorithm

Wakrime, Abderrahim Ait; Rekik, Mouna; Jabbour, Saïd

doi:10.1002/cpe.5282

Cited by 8 publications

(5 citation statements)

References 42 publications

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“…Asghari et al [47] introduced a hybrid shuffled frog leaping and genetic algorithm. Wakrime et al [48] proposed a workflow composition method using the minimally unsatisfiable algorithm and proposed genetic based algorithm for searching better solutions.…”

Section: Related Aco-based Methodsmentioning

confidence: 99%

An Enhanced Ant Colony Optimization Based Algorithm to Solve QoS-Aware Web Service Composition

et al. 2021

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Web Service Composition (WSC) can be defined as the problem of consolidating the services regarding the complex user requirements. These requirements can be represented as a workflow. This workflow consists of a set of abstract task sequence where each sub-task represents a definition of some user requirements. In this work, we propose a more efficient neighboring selection process and multi-pheromone distribution method named Enhanced Flying Ant Colony Optimization (EFACO) to solve this problem. The WSC problem has a challenging issue, where the optimization algorithms search the best combination of web services to achieve the functionality of the workflow's tasks. We aim to improve the computation complexity of the Flying Ant Colony Optimization (FACO) algorithm by introducing three different enhancements. We analyze the performance of EFACO against six of existing algorithms and present a summary of our conclusions.

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Section: Related Aco-based Methodsmentioning

confidence: 99%

An Enhanced Ant Colony Optimization Based Algorithm to Solve QoS-Aware Web Service Composition

et al. 2021

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“…Tang and Ai [128] implemented a knowledge-based crossover operator. Moreover, Ait Wakrime et al [129] developed a combination of exact methods (SAT encoding) with a genetic algorithm. Que et al [130] implemented a hybrid solution with information enthalpy inherited from an artificial immune system to address the deterministic probability of genetic algorithm operator, which leads to premature converge problem.…”

Section: Classification Of Hybrid Metaheuristicmentioning

confidence: 99%

Hybrid Metaheuristics for QoS-Aware Service Composition: A Systematic Mapping Study

et al. 2022

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With the advent of Service-Oriented Architecture (SOA), services can be registered, invoked, and combined by their identical Quality of Services (QoS) attributes to create a new value-added application that fulfils user requirements. Efficient QoS-aware service composition has been a challenging task in cloud computing. This challenge becomes more formidable in emerging resource-constrained computing paradigms such as the Internet of Things and Fog. Service composition has regarded as a multi-objective combinatorial optimization problem that falls in the category of NP-hard. Historically, the proliferation of services added to problem complexity and navigated solutions from exact (none-heuristics) approaches to near-optimal heuristics and metaheuristics. Although metaheuristics have fulfilled some expectations, the quest for finding a high-quality, near-optimal solution has led researchers to devise hybrid methods. As a result, research on service composition shifts towards the hybridization of metaheuristics. Hybrid metaheuristics have been promising efforts to transcend the boundaries of metaheuristics by leveraging the strength of complementary methods to overcome base algorithm shortcomings. Despite the significance and frontier position of hybrid metaheuristics, to the best of our knowledge, there is no systematic research and survey in this field with a particular focus on strategies to hybridize traditional metaheuristics. This study's core contribution is to infer a framework for hybridization strategies by conducting a mapping study that analyses 71 papers between 2008 and 2020. Moreover, it provides a panoramic view of hybrid methods and their experiment setting in respect to the problem domain as the main outcome of this mapping study. Finally, research trends, directions and challenges are discussed to benefit future endeavours.

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“…A hybrid evolutionary algorithm between shuffled frog leaping algorithm and genetic algorithm was proposed in [32]. The minimally unsatisfiable subset formal algorithm is proposed in [33] to compose the workflow and the penalty-based genetic algorithm is used to search nearoptimal solution. The correlations between services for service composition were studied in [34].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Liu et al [37] proposed an improved MMAS algorithm where the transfer and global pheromone updating rules were updated. Zhou and Yao introduced three hybrid ABC algorithms in [24], [32], [33]. In [24], [32], cuckoo search helped ABC to overcome poor exploitation and slow convergence problems.…”

Section: Literature Reviewmentioning

confidence: 99%

An Effective Multi-Agent Ant Colony Optimization Algorithm for QoS-Aware Cloud Service Composition

Dahan

2021

IEEE Access

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Recently, service composition has gained increased attention as an auspicious paradigm to optimize the data accessibility, integrity, and interoperability of cloud computing. In this work, to solve the cloud service composition (CSC) problem, we introduce an efficient agent-based ant colony optimization (ACO) algorithm. The CSC problem aims to satisfy complex and challenging requirements of enterprises/users in a cloud environment. The challenge of such problem is the proliferation of providing similar services having similar functionality with varying quality of service (QoS) properties from different providers. Several swarm-based algorithms were introduced to solve this problem because the complexity of the problem is characterized as NP-hard, which is high. These algorithms aim to maintain a good balance between exploration and exploitation mechanisms, and to achieve this, a multi-agent based on ACO is proposed and compared with four different algorithms using 25 different real datasets. The computational results on 25 real datasets confirm the effectiveness of the multi-agent distribution of ACO process. Moreover, comparisons against the results of the four algorithms in the literature indicate that the multi-agent ACO approach is competitive with state-of-the-art algorithms.INDEX TERMS Quality of Service, cloud services composition, metaheuristic algorithm, ant colony optimization.

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Cloud service composition using minimal unsatisfiability and genetic algorithm

Cited by 8 publications

References 42 publications

An Enhanced Ant Colony Optimization Based Algorithm to Solve QoS-Aware Web Service Composition

An Enhanced Ant Colony Optimization Based Algorithm to Solve QoS-Aware Web Service Composition

Hybrid Metaheuristics for QoS-Aware Service Composition: A Systematic Mapping Study

An Effective Multi-Agent Ant Colony Optimization Algorithm for QoS-Aware Cloud Service Composition

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