2021 IEEE International Conference on Web Services (ICWS)最新文献_第7页

Energy-effective IoT Services in Balanced Edge-Cloud Collaboration Systems 平衡边缘云协作系统中的节能物联网服务

2021 IEEE International Conference on Web Services (ICWS) Pub Date : 2021-09-01 DOI: 10.1109/ICWS53863.2021.00040

Zhengzhe Xiang, Shuiguang Deng, Yuhang Zheng, Dongjing Wang, J. Taheri, Zengwei Zheng

{"title":"Energy-effective IoT Services in Balanced Edge-Cloud Collaboration Systems","authors":"Zhengzhe Xiang, Shuiguang Deng, Yuhang Zheng, Dongjing Wang, J. Taheri, Zengwei Zheng","doi":"10.1109/ICWS53863.2021.00040","DOIUrl":"https://doi.org/10.1109/ICWS53863.2021.00040","url":null,"abstract":"The rapid development of the Internet-of-Things (IoT) makes it convenient to sense and collect real-world information with different kinds of widely distributed sensors. With plenty of web services providing diverse functions on the cloud, the collected information can be sufficiently used to complete complex tasks after being uploaded. However, the latency brought by long-distance communication and network congestion limits the development of IoT platforms. A feasible approach to solve this problem is to establish an edge-cloud collaboration (ECC) system based on the multi-access edge computing (MEC) paradigm where the collected information can be refined with the services deployed on nearby edge servers. However, as the edge servers are resource-limited, we should be more careful in allocating the edge resource to services, as well as designing the traffic scheduling strategy. In this paper, we investigated the edge-cloud cooperation mechanism of service provisioning in ECC systems, and to that end, proposed an energy-consumption model for it; we also proposed a performance model and balancing model to quantify the running state of ECC systems. Based on these, we further formulated the energy-effective ECC system optimization problem as a joint optimization problem whose decision variables are the resource allocation strategy and traffic scheduling strategy. With the convexity of this problem proved, we proposed an algorithm to solve it and conducted a series of experiments to evaluate its performance. The results showed that our approach can improve at least 4.3 % of the performance compared with representative baselines.","PeriodicalId":213320,"journal":{"name":"2021 IEEE International Conference on Web Services (ICWS)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125755642","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

MatTrip: Multi-functional Attention-based Neural Network for Semantic Travel Route Recommendation MatTrip:基于注意的多功能神经网络语义路线推荐

2021 IEEE International Conference on Web Services (ICWS) Pub Date : 2021-09-01 DOI: 10.1109/ICWS53863.2021.00030

Chenxiao Yang, Jiale Zhang, Xiaofeng Gao, Guihai Chen

{"title":"MatTrip: Multi-functional Attention-based Neural Network for Semantic Travel Route Recommendation","authors":"Chenxiao Yang, Jiale Zhang, Xiaofeng Gao, Guihai Chen","doi":"10.1109/ICWS53863.2021.00030","DOIUrl":"https://doi.org/10.1109/ICWS53863.2021.00030","url":null,"abstract":"Travel route recommendation aims to recommend a sequence of point of interests (POIs) for visitors based on their personal interests. Previous studies utilize user interest features and POI spatial information to provide travel route recommendation service. However, most of them fail to consider the implicit information in user traveling patterns, which is the key to improve recommendation quality. Additionally, few deep learning based travel route recommendation systems provide comprehensive trip planning functionalities, which is critical to improve the user experience. To alleviate these two problems, we propose a multi-functional attention-based neural network for route recommendation (named MatTrip). We first introduce an encoder-decoder structure with a novel dual bi-directional LSTM encoder as the sequence generation model to learn other users' traveling records and generates a semantic travel route based on user preference and geographical features of start/end POI. Next, multiple user-specific functionalities are supported in MatTrip by grid beam search. The functionalities include weather dependency, POI opening hours, restricted sequence length, mandatory POIs, and dynamic route revision. In addition, MatTrip adopts an online learning approach to learn from user deviation behaviors to improve recommendation performance. Experiments on two real-world datasets show that our model achieves a 20.98% improvement in performance, compared with state-of-arts.","PeriodicalId":213320,"journal":{"name":"2021 IEEE International Conference on Web Services (ICWS)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125843055","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Video Quality and Popularity-aware Video Caching in Content Delivery Networks 内容交付网络中视频质量和流行度感知视频缓存

2021 IEEE International Conference on Web Services (ICWS) Pub Date : 2021-09-01 DOI: 10.1109/ICWS53863.2021.00088

Yijun Sun, Zehua Guo, Songshi Dou, Yuanqing Xia

引用次数: 1

Instance-Frequency-Weighted Regularized, Nonnegative and Adaptive Latent Factorization of Tensors for Dynamic QoS Analysis 动态QoS分析中张量的实例-频率加权正则化、非负和自适应潜分解

2021 IEEE International Conference on Web Services (ICWS) Pub Date : 2021-09-01 DOI: 10.1109/ICWS53863.2021.00077

Hao Wu, Xin Luo

{"title":"Instance-Frequency-Weighted Regularized, Nonnegative and Adaptive Latent Factorization of Tensors for Dynamic QoS Analysis","authors":"Hao Wu, Xin Luo","doi":"10.1109/ICWS53863.2021.00077","DOIUrl":"https://doi.org/10.1109/ICWS53863.2021.00077","url":null,"abstract":"Temporally dynamic QoS data are commonly encountered in large-scale cloud services environments. They can be quantized into a high-dimensional and incomplete (HDI) tensor defined on the user×service×time. Despite its HDI nature, it contains various temporal patterns highly helpful in representing involved users and services. A latent factorization of tensors (LFT) model is able to discover such patterns from an HDI tensor, while its model generality cannot be ensured due to the complex structure and incomplete data of an HDI tensor. To address this issues, this paper proposes an Instance-frequency-weighted regularized, Nonnegative and Adaptive LFT (INAL) model with three-fold ideas: a) adopting the principle of data density-oriented modeling to reduce the computation and storage complexity; b) refining the regularization effects on each latent factor with its relevant instance-frequency for illustrating the imbalanced distribution of known data in an HDI tensor; and c) making its hyper-parameter self-adaptive via incorporating the principle of a particle swarm optimization (PSO) algorithm into the training process, thereby achieving a highly adaptive and practical model. Empirical studies on two dynamic QoS datasets from real applications demonstrate that compared with state-of-the-art models, the proposed model achieves significant gain in prediction accuracy for unobserved dynamic QoS data and achieves highly competitive computational efficiency.","PeriodicalId":213320,"journal":{"name":"2021 IEEE International Conference on Web Services (ICWS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130844069","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 12

Time-aware User Modeling with Check-in Time Prediction for Next POI Recommendation 下一个POI推荐的具有时间感知的用户建模与签入时间预测

2021 IEEE International Conference on Web Services (ICWS) Pub Date : 2021-09-01 DOI: 10.1109/ICWS53863.2021.00028

Xin Wang, Xiao Liu, Li Li, Xiao Chen, Jin Liu, Hao Wu

{"title":"Time-aware User Modeling with Check-in Time Prediction for Next POI Recommendation","authors":"Xin Wang, Xiao Liu, Li Li, Xiao Chen, Jin Liu, Hao Wu","doi":"10.1109/ICWS53863.2021.00028","DOIUrl":"https://doi.org/10.1109/ICWS53863.2021.00028","url":null,"abstract":"POI (point-of-interest) recommendation as an important type of location-based services has received increasing attention with the rise of location-based social networks. Although significant efforts have been dedicated to learning and recommending users' next POIs based on their historical mobility traces, there still lacks consideration of the discrepancy of users' check-in time preferences and the inherent relationships between POIs and check-in times. To fill this gap, this paper proposes a novel recommendation method which applies multi-task learning over historical user mobility traces known to be sparse. Specifically, we design a cross-graph neural network to obtain time-aware user modeling and control how much information flows across different semantic spaces, which makes up the inadequate representation of existing user modeling methods. In addition, we design a check-in time prediction task to learn users' activities from a time perspective and learn internal patterns between POIs and their check-in times, aiming to reduce the search space to overcome the data sparsity problem. Comprehensive experiments on two real-world public datasets demonstrate that our proposed method outperforms several representative POI recommendation methods with 8.93% to 20.21 % improvement on Recall@1, 5, 10, and 9.25% to 17.56% improvement on Mean Reciprocal Rank.","PeriodicalId":213320,"journal":{"name":"2021 IEEE International Conference on Web Services (ICWS)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130899678","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8

Program Committee ICWS 2021 ICWS 2021项目委员会

2021 IEEE International Conference on Web Services (ICWS) Pub Date : 2021-09-01 DOI: 10.1109/icws53863.2021.00009

引用次数: 0

A Generic Method to Rapidly Release Internet Services on Commercial Platforms 互联网服务在商业平台上快速发布的通用方法

2021 IEEE International Conference on Web Services (ICWS) Pub Date : 2021-09-01 DOI: 10.1109/ICWS53863.2021.00055

Xinyu Zhou, Zhiyong Feng, Jianmao Xiao, Shizhan Chen, Xiao Xue, Hongyue Wu

引用次数: 1

2021 IEEE World Congress on Services: Message from Congress General Chairs 2021年IEEE世界服务大会:大会主席致辞

2021 IEEE International Conference on Web Services (ICWS) Pub Date : 2021-09-01 DOI: 10.1109/icws53863.2021.00006

引用次数: 0

Mining Temporal Dependency among Proactive Data Services and Its Delivery to System-level Anomaly Prediction 主动数据服务的时间依赖性挖掘及其在系统级异常预测中的应用

2021 IEEE International Conference on Web Services (ICWS) Pub Date : 2021-09-01 DOI: 10.1109/ICWS53863.2021.00090

Chen Liu, Xiaoqi Li

引用次数: 1

Service Composition Considering QoS Fluctuations and Anchoring Cost 考虑QoS波动和锚定成本的服务组合

2021 IEEE International Conference on Web Services (ICWS) Pub Date : 2021-09-01 DOI: 10.1109/ICWS53863.2021.00056

Haomai Shi, Hanchuan Xu, Xiaofei Xu, Zhongjie Wang

{"title":"Service Composition Considering QoS Fluctuations and Anchoring Cost","authors":"Haomai Shi, Hanchuan Xu, Xiaofei Xu, Zhongjie Wang","doi":"10.1109/ICWS53863.2021.00056","DOIUrl":"https://doi.org/10.1109/ICWS53863.2021.00056","url":null,"abstract":"Traditional service composition focuses on how to construct optimal composite solutions that satisfy end-to-end QoS constraints raised by users. Although there have been rich well-recognized service composition approaches, most of them ignore an obvious phenomenon: users have to go through a cognition process on unacquainted services to gradually get familiar with these services, and directly using unacquainted services in composite solutions might lead to potential risk on user experiences. QoS fluctuations of services might affect the cognition process to some extent, too. In this paper, we define a new concept anchoring cost to measure such degree of unfamiliarity and give its measurement in terms of historical service usage records and QoS fluctuations. A multi-objective optimization model considering QoS fluctuations and anchoring cost is introduced: besides pursuing the optimality of QoS attributes, to reduce the risk that is caused by importing services with higher anchoring cost into composite solutions is another optimization objective. A set of Pareto-optimal non-dominated composite solutions are obtained by the NSGA-II algorithm and different types of users in terms of risk aversion (e.g., balanced, conservative, risk-taking) may choose their preferred solutions from them. Experiments are conducted on real QoS datasets in three scenarios to demonstrate the rationality and significance of the proposed approach. To the best of our knowledge, this is the first time that cognition cost is incorporated into the service composition problem.","PeriodicalId":213320,"journal":{"name":"2021 IEEE International Conference on Web Services (ICWS)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130471468","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3