DeepQSC: a GNN and Attention Mechanism-based Framework for QoS-aware Service Composition

Ren, Xiaohan; Zhang, Wenjun; Li, Bao; Song, Jinqiu; Wang, Shuai; Cao, Ruifen; Wang, Xinlei

doi:10.1109/icss53362.2021.00020

Cited by 7 publications

(3 citation statements)

References 31 publications

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“…DeepQSC, proposed by Ren et al [18], addresses the QoS-based service composition problem through an end-to-end supervised learning framework incorporating attention mechanisms. It effectively navigates challenges related to diverse service compositions, intricate topological relationships, and varying subfunction providers.…”

Section: Related Workmentioning

confidence: 99%

Performance Evaluation of Graph Neural Network-Based RouteNet Model with Attention Mechanism

Dhamala,

Dawadi,

Manzoni

et al. 2024

Future Internet

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Graph representation is recognized as an efficient method for modeling networks, precisely illustrating intricate, dynamic interactions within various entities of networks by representing entities as nodes and their relationships as edges. Leveraging the advantage of the network graph data along with deep learning technologies specialized for analyzing graph data, Graph Neural Networks (GNNs) have revolutionized the field of computer networking by effectively handling structured graph data and enabling precise predictions for various use cases such as performance modeling, routing optimization, and resource allocation. The RouteNet model, utilizing a GNN, has been effectively applied in determining Quality of Service (QoS) parameters for each source-to-destination pair in computer networks. However, a prevalent issue in the current GNN model is their struggle with generalization and capturing the complex relationships and patterns within network data. This research aims to enhance the predictive power of GNN-based models by enhancing the original RouteNet model by incorporating an attention layer into its architecture. A comparative analysis is conducted to evaluate the performance of the Modified RouteNet model against the Original RouteNet model. The effectiveness of the added attention layer has been examined to determine its impact on the overall model performance. The outcomes of this research contribute to advancing GNN-based network performance prediction, addressing the limitations of existing models, and providing reliable frameworks for predicting network delay.

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Section: Related Workmentioning

confidence: 99%

Performance Evaluation of Graph Neural Network-Based RouteNet Model with Attention Mechanism

Dhamala,

Dawadi,

Manzoni

et al. 2024

Future Internet

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“…Additionally, Bouzary et al [35] proposed using Word2Vec and LSTM-based neural network models to identify suitable candidate sets for each submitted manufacturing subtask in the cloud manufacturing platform, followed by an approach to optimal composition services using genetic algorithms. Ren et al [36] introduced DeepQSC, a deep supervision learning framework based on graph convolutional networks and attention mechanisms that can form high-QoS composition services within a limited computing time. However, this method does not account for user QoS constraints.…”

Section: Service Composition Based On Machine Learningmentioning

confidence: 99%

Skyline-Enhanced Deep Reinforcement Learning Approach for Energy-Efficient and QoS-Guaranteed Multi-Cloud Service Composition

2023

Applied Sciences

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Cloud computing has experienced rapid growth in recent years and has become a critical computing paradigm. Combining multiple cloud services to satisfy complex user requirements has become a research hotspot in cloud computing. Service composition in multi-cloud environments is characterized by high energy consumption, which brings attention to the importance of energy consumption in cross-cloud service composition. Nonetheless, prior research has mainly focused on finding a service composition that maximizes the quality of service (QoS) and overlooks the energy consumption generated during service invocation. Additionally, the dynamic nature of multi-cloud environments challenges the adaptability and scalability of cloud service composition methods. Therefore, we propose the skyline-enhanced deep reinforcement learning approach (SkyDRL) to address these challenges. Our approach defines an energy consumption model for cloud service composition in multi-cloud environments. The branch and bound skyline algorithm is leveraged to reduce the search space and training time. Additionally, we enhance the basic deep Q-network (DQN) algorithm by incorporating double DQN to address the overestimation problem, incorporating Dueling Network and Prioritized Experience Replay to speed up training and improve stability. We evaluate our proposed method using comparative experiments with existing methods. Our results demonstrate that our approach effectively reduces energy consumption in cloud service composition while maintaining good adaptability and scalability in service composition problems. According to the experimental results, our approach outperforms the existing approaches by demonstrating energy savings ranging from 8% to 35%.

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“…By perceiving the positional structural information of nodes within a graph and comprehending relationships between nodes through information propagation mechanisms, GNNs can enable more sophisticated exploration. Researchers have therefore incorporated Graph Neural Networks into service recommendations to achieve better recommendation outcomes [ 20 , 21 , 22 , 23 ]. Although service recommendation methods have achieved good results, they still have some shortcomings.…”

Section: Introductionmentioning

confidence: 99%

Time-Aware Dual LSTM Neural Network with Similarity Graph Learning for Remote Sensing Service Recommendation

Zhang,

Ma,

Zhang

et al. 2024

Sensors

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Technological progress has led to significant advancements in Earth observation and satellite systems. However, some services associated with remote sensing face issues related to timeliness and relevance, which affect the application of remote sensing resources in various fields and disciplines. The challenge now is to help end-users make precise decisions and recommendations for relevant resources that meet the demands of their specific domains from the vast array of remote sensing resources available. In this study, we propose a remote sensing resource service recommendation model that incorporates a time-aware dual LSTM neural network with similarity graph learning. We further use the stream push technology to enhance the model. We first construct interaction history behavior sequences based on users’ resource search history. Then, we establish a category similarity relationship graph structure based on the cosine similarity matrix between remote sensing resource categories. Next, we use LSTM to represent historical sequences and Graph Convolutional Networks (GCN) to represent graph structures. We construct similarity relationship sequences by combining historical sequences to explore exact similarity relationships using LSTM. We embed user IDs to model users’ unique characteristics. By implementing three modeling approaches, we can achieve precise recommendations for remote sensing services. Finally, we conduct experiments to evaluate our methods using three datasets, and the experimental results show that our method outperforms the state-of-the-art algorithms.

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DeepQSC: a GNN and Attention Mechanism-based Framework for QoS-aware Service Composition

Cited by 7 publications

References 31 publications

Performance Evaluation of Graph Neural Network-Based RouteNet Model with Attention Mechanism

Performance Evaluation of Graph Neural Network-Based RouteNet Model with Attention Mechanism

Skyline-Enhanced Deep Reinforcement Learning Approach for Energy-Efficient and QoS-Guaranteed Multi-Cloud Service Composition

Time-Aware Dual LSTM Neural Network with Similarity Graph Learning for Remote Sensing Service Recommendation

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