A New Hybrid Approach Using Genetic Algorithm and Q-learning for QoS-aware Web Service Composition

Elsayed, Doaa H.; Nasr, Eman S.; Ghazali, Alaa El Din M. El; Gheith, Mervat

doi:10.1007/978-3-319-64861-3_50

Cited by 9 publications

(7 citation statements)

References 16 publications

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“…In recent years, Machine Learning (ML) has gradually become a viable hybrid strategy to incorporate into metaheuristics. The machine learning techniques including Qlearning [137], deep learning [156], reinforcement learning [174] has been employed to improve the search mechanism on different grounds. While machine learning incorporated methods generating a high-quality population, they are not highly competitive from the time of execution perspective attributed to the sophistication of ML enabled solutions.…”

Section: S-5mentioning

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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Section: S-5mentioning

confidence: 99%

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

et al. 2022

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“…In addition, a self‐organization mechanism and convex‐hull operator are used to derive the weight of QoS objectives and find the Pareto optimal solutions. In Reference 46, the authors propose a hybrid approach to find the optimal WSC. This latter method is based on the GA. To improve the results, the Q‐learning is combined with GA.…”

Section: State‐of‐the‐artmentioning

confidence: 99%

Web service selection and composition based on uncertain quality of service

Yasmina

Fethallah

Fadoua

2021

Concurrency and Computation

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Web services are becoming a major utility for accomplishing complex tasks over the Internet. In practice, the end‐users usually search for Web service compositions that best meet the quality of service (QoS) requirements (i.e., QoS global constraints). Since the number of services is constantly increasing and their respective QoS is inherently uncertain (due to environmental conditions), the task of selecting optimal compositions becomes more challenging. To tackle this problem, we propose a heuristic based on majority judgment that allows for reducing the search space. In addition, we perform a constraint programming search to select the Top K compositions that fulfill the QoS global constraints. The experimental results demonstrate the high performance of our approach.

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“…Other works have proposed several heuristic algorithms [20], [21], and [22] to reduce the complexity of search time in local or global search related to web services composition. In [23], the authors proposed a new approach that combines the use of genetic algorithms and Q-learning to find the optimal composition. In [24], the composition of services taking into account QoS and transactional properties is modeled as a path construction problem in a directed acyclic graph.…”

Section: Literature Reviewmentioning

confidence: 99%

How Can Transactional Semantics Enhance the Commit Rate of Context-aware Service Composition in Advanced Pervasive Systems?

Ettazi

Riane²,

Nassar

2021

Int. J. Recent Contrib. Eng. Sci. IT

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Context-aware composition of services exhibiting transactional properties poses several challenges. A major challenge is the transactional behavior of candidate services which is subject to perpetual change while the composition is running. Compositions of services displaying transactional properties must be dynamically adapted at run time to cope with context fluctuations. By dynamic adaptation, we refer to the ability to alter the composition behavior in response to changes affecting its execution. We focus on changes impacting the successful commit rate of transactional service composition. This has led us to explore the trail of a flexible homeomorphism between alternative behaviors. We propose a behavioral adaptation approach that adjusts the behavior of transactional compositions of services in a proactive and transparent manner. This strategy is based on the Profiled Task Class concept. A service composition generator has also been developed for the performance evaluation of components implementing the behavioral adaptation strategy in order to identify its impact on the commit rate of CATS compositions.

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A New Hybrid Approach Using Genetic Algorithm and Q-learning for QoS-aware Web Service Composition

Cited by 9 publications

References 16 publications

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

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

Web service selection and composition based on uncertain quality of service

How Can Transactional Semantics Enhance the Commit Rate of Context-aware Service Composition in Advanced Pervasive Systems?

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