A Deep Learning Approach for Long Term QoS-Compliant Service Composition

Labbaci, Hamza; Medjahed, Brahim; Aklouf, Youcef

doi:10.1007/978-3-319-69035-3_20

Cited by 11 publications

(8 citation statements)

References 13 publications

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“…Wang et al [3] proposed a deep Q-learning (DQN) algorithm based on LSTM and fully connected layer. Labbaci et al [27] proposed a deep learning approach for long-term QoS. Kazem et al [28] used Bayesian network to predict new values of certain QoS attributes.…”

Section: Learning-based Algorithmsmentioning

confidence: 99%

PPDRL: A Pretraining-and-Policy-Based Deep Reinforcement Learning Approach for QoS-Aware Service Composition

Yi¹,

Jin

Wang³

et al. 2022

Security and Communication Networks

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Service composition is a mainstream paradigm for rapidly constructing large-scale distributed applications. QoS-aware service composition, i.e., selection of the optimal execution plan that maximizes the composition’s end-to-end QoS properties, is an active area of research and development endeavors in service composition. In this paper, we propose PPDRL, a pretraining-and-policy-based deep reinforcement learning approach, to solve the QoS-aware service composition problem. Its significant feature is to incorporate a maximum likelihood estimate and a policy scoring mechanism into a deep reinforcement learning framework. As a result, our approach can balance the exploitation and exploration efforts adaptively and can search for the solution space in a robust and efficient manner. We have executed our approach to solve 6 randomly generated QoS-aware service composition problems with different sizes and structures based on QWS data set including 2,507 real Web services classified into 233 categories. The results indicate that our approach can find near-optimal solutions within moderate numbers of iteration and has performance superiority in comparison with five state-of-the-art algorithms.

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Section: Learning-based Algorithmsmentioning

confidence: 99%

PPDRL: A Pretraining-and-Policy-Based Deep Reinforcement Learning Approach for QoS-Aware Service Composition

Yi¹,

Jin

Wang³

et al. 2022

Security and Communication Networks

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“…Xiong et.al [31] propose a novel personalized LSTM based matrix factorization approach that could capture the dynamic latent representations of multiple users and services. Hamza et.al [32] uses deep recurrent Long Short Term Memories (LSTMs) to forecast future QoS.…”

Section: Related Workmentioning

confidence: 99%

A Probability Distribution and Location-aware ResNet Approach for QoS Prediction

Zhang

Yan

et al. 2020

Preprint

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In recent years, the number of online services has grown rapidly, invoke the required services through the cloud platform has become the primary trend.How to help users choose and recommend high-quality services among huge amounts of unused services has become a hot issue in research. Among the existing QoS prediction methods, the collaborative filtering(CF) method can only learn low-dimensional linear characteristics, and its effect is limited by sparse data. Although existing deep learning methods could capture high-dimensional nonlinear features better, most of them only use the single feature of identity,

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“…In addition, some other methods have also been adopted to solve the problem of service composition. For instance, Bekkouche et al [31] described a novel approach based on a Harmony Search algorithm that addressed functional requirements and nonfunctional requirements simultaneously through a fitness function, to select the optimal or near-optimal solution in semantic web service composition; Jatoth et al [32] proposed a novel MapReduce-based Evolutionary Algorithm with Guided Mutation that lead to a better Big service composition with better solution and execution time; Labbaci et al [33] put forward a deep learning approach for dynamic QoS based service composition which got promising results compared with existing QoS prediction techniques. These pieces of work have shown that the service composition problem 4 Complexity is an essential part of the current research on intelligent manufacturing.…”

Section: Related Workmentioning

confidence: 99%

A Heuristic Algorithm for Optimal Service Composition in Complex Manufacturing Networks

Peng

Wang

et al. 2019

Complexity

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Service composition in a Cloud Manufacturing environment involves the adaptive and optimal assembly of manufacturing services to achieve quick responses to varied manufacturing needs. It is challenged by the inherent heterogeneity and complexity of these services in terms of their diverse and complex functions, qualities of service, execution paths, etc. In this paper, a manufacturing network is constructed to explicitly identify and describe the relationships between individual services based on their attributes. On this basis, the service composition problem can be modeled as a multiple-constrained optimal path (MCOP) selection problem by taking into account different types of composition, namely, sequence, parallel, selection, and cycle. A novel Dual Heuristic Functions based Optimal Service Composition Path algorithm (DHA_OSCP) is proposed to solve the NP-Complete MCOP problem, which involves exploiting the backward search procedure with different search targets to obtain two heuristic functions for the forward search procedure. The proposed algorithm is evaluated through a set of computational experiments in which the proposed algorithm and other popular algorithms such as MFPB_HOSTP are applied to the same dataset, and the results obtained show that DHA_OSCP can efficiently find the optimal service composition path with better Quality of Service (QoS). The viability of DHA_OSCP is further proved in a case study of services composition on a Cloud Manufacturing platform.

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A Deep Learning Approach for Long Term QoS-Compliant Service Composition

Cited by 11 publications

References 13 publications

PPDRL: A Pretraining-and-Policy-Based Deep Reinforcement Learning Approach for QoS-Aware Service Composition

PPDRL: A Pretraining-and-Policy-Based Deep Reinforcement Learning Approach for QoS-Aware Service Composition

A Probability Distribution and Location-aware ResNet Approach for QoS Prediction

A Heuristic Algorithm for Optimal Service Composition in Complex Manufacturing Networks

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