2021 IEEE International Conference on Web Services (ICWS)最新文献

{"title":"Software Services Engineering Manifesto - A Cross-Cutting Declaration","authors":"Carl K. Chang, P. Ceravolo, Rong N. Chang, A. Helal, Zhi Jin, Xuanzhe Liu, Ming Hua","doi":"10.1109/ICWS53863.2021.00014","DOIUrl":"https://doi.org/10.1109/ICWS53863.2021.00014","url":null,"abstract":"As we have entered the Internet-of-Things (IoT) era, further blessed with rapid advances in several key technological areas including DevOps, AI/ML, 5G/6G/, neurocomputing, to name a few, it is imperative we think big and aim high. This new venture will require professionals in both software engineering and services computing to collaborate with an unprecedented intensity, and jointly develop the new interdisciplinary field hereby named Software Services Engineering (SSE). In SSE, the ever-deepening system dynamics emerging from both environments and humans in varying contexts are imposing steep challenges to both researchers and practitioners. Humans, both developers and the vast number of end users, are embedded ever closer to IoT environments, and are being afforded ample opportunities to continuously inject inputs during system development and after deployment. In fact, humans are increasingly playing the roles of both sensor and actuator. Traditional requirements engineering researchers are being lured more than ever into exploiting the IoT environments where human users are deeply embedded, to gather contextual information that inevitably introduces lots of ambiguity and uncertainty. Provisioning of highly adaptable and scalable microservices would be key to timely meeting ever-changing human desires and ever-evolving system requirements in the nimblest manner. As such, an ultra-agile and field-programmable development methodology and environment will be imperative to achieving such ultrafine grained microservices provisioning. Such ultra-agility and ultrafine granularity requirements imposed to the services industry obligate company executives to expect extreme manageability assurance to become the centroid of system operations and administration. The ultimate goal in pursuit of such a noble dream will be to provide genuinely individualized and trustworthy service, possibly enabled by AI, but it should be both explainable and ethical. Facing such grand challenges, this declaration samples a subset of burning issues in SSE through observations in seven themes, only meant to be starting points for the SSE community to further investigate. Through our declarations we also call for heightened attention to an assorted array of existing, barely emerging or non-existent services computing and software engineering methods for a concerted effort to research and explore.","PeriodicalId":213320,"journal":{"name":"2021 IEEE International Conference on Web Services (ICWS)","volume":"56 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":"128259684","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}

{"title":"Online Cost-effective Edge Service Renting for Content Providers in Cloud and Edge Environments","authors":"Zizhe Jin, Li Pan, Shijun Liu","doi":"10.1109/ICWS53863.2021.00075","DOIUrl":"https://doi.org/10.1109/ICWS53863.2021.00075","url":null,"abstract":"For solving the problem of high bandwidth costs and service delays faced by content service providers (CSPs), edge computing services can be used as a supplement to existing cloud data centers for building more efficient Content Delivery Networks (CDNs). When there are a large number of requests for a content in one certain area, the content service provider can choose to rent an edge computing service near this area to lower the bandwidth cost for content delivery. But if the requests then drop after that, additional costs will be incurred instead due to the edge service renting. Therefore, it is necessary to dynamically decide whether to rent an edge service according to the request arrival situations in the future, but the future is often difficult to predict. For dealing with this problem, we propose an online edge service renting approach, as well as a corresponding request redirection algorithm, which can help content service providers save bandwidth cost significantly, while without requiring any knowledge about the future. Through theoretical analysis, we prove that the cost achieved by our online algorithm won't exceed 2 - α times compared to the optimal offline algorithm, where α is the bandwidth discount between edge and cloud services. Finally, by conducting extensive simulations with both real-world and synthetic data, we verify that our online edge service renting approach can effectively save costs for CSPs.","PeriodicalId":213320,"journal":{"name":"2021 IEEE International Conference on Web Services (ICWS)","volume":"22 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":"126370606","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}

{"title":"Relational Graph Neural Network with Neighbor Interactions for Bundle Recommendation Service","authors":"Xin Wang, Xiao Liu, Jin Liu, Hao Wu","doi":"10.1109/ICWS53863.2021.00033","DOIUrl":"https://doi.org/10.1109/ICWS53863.2021.00033","url":null,"abstract":"Bundle recommendation plays a crucial role in the service ecosystem. However, most existing bundle recommendation methods are limited in several critical aspects such as the lack of injecting different relations into the representations of bundles and items, and the ignorance of neighbor interactions. To address these limitations, in this paper, we propose a relational graph neural network with neighbor interactions for bundle recommendation. Specifically, we firstly construct two relational graphs, e.g., user-bundle-item interaction graph and bundle-item affiliation graph. We utilize a relational graph neural network to inject different relations into representations of bundles and items. Secondly, we consider neighbor interactions to highlight common properties of neighbors. Finally, a multi-task learning framework is also exploited to capture users' preferences at the item level to further enhance bundle recommendation performance. Comprehensive experiments on two real-world public datasets demonstrate that our proposed method can outperform various representative bundle recommendation methods.","PeriodicalId":213320,"journal":{"name":"2021 IEEE International Conference on Web Services (ICWS)","volume":"37 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":"123873639","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}

{"title":"[Copyright notice]","authors":"","doi":"10.1109/icws53863.2021.00003","DOIUrl":"https://doi.org/10.1109/icws53863.2021.00003","url":null,"abstract":"","PeriodicalId":213320,"journal":{"name":"2021 IEEE International Conference on Web Services (ICWS)","volume":"68 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":"124413115","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}

{"title":"QoE-aware Data Caching Optimization with Budget in Edge Computing","authors":"Ying Liu, Yuzheng Han, Ao Zhang, Xiaoyu Xia, Feifei Chen, Mingwei Zhang, Qiang He","doi":"10.1109/ICWS53863.2021.00050","DOIUrl":"https://doi.org/10.1109/ICWS53863.2021.00050","url":null,"abstract":"Edge data caching has attracted tremendous attention in recent years. Service providers can consider caching data on nearby locations to provide service for their app users with relatively low latency. The key to enhance the user experience is appropriately choose to cache data on the suitable edge servers to achieve the service providers' objective, e.g., minimizing data retrieval latency and minimizing data caching cost, etc. However, Quality of Experience (QoE), which impacts service providers' caching benefit significantly, has not been adequately considered in existing studies of edge data caching. This is not a trivial issue because QoE and Quality-of-Service (QoS) are not correlated linearly. It significantly complicates the formulation of cost-effective edge data caching strategies under the caching budget, limiting the number of cache spaces to hire on edge servers. We consider this problem of QoE-aware edge data caching in this paper, intending to optimize users' overall QoE under the caching budget. We first build the optimization model and prove the NP-completeness about this problem. We propose a heuristic approach and prove its approximation ratio theoretically to solve the problem of large-scale scenarios efficiently. We have done extensive experiments to demonstrate that the MPSG algorithm we propose outperforms state-of-the-art approaches by at least 68.77%.","PeriodicalId":213320,"journal":{"name":"2021 IEEE International Conference on Web Services (ICWS)","volume":"17 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":"128698321","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}

{"title":"CONFECT: Computation Offloading for Tasks with Hard/Soft Deadlines in Edge Computing","authors":"Xin He, Jiaqi Zheng, Qiang He, Haipeng Dai, Bowen Liu, Wanchun Dou, Guihai Chen","doi":"10.1109/ICWS53863.2021.00044","DOIUrl":"https://doi.org/10.1109/ICWS53863.2021.00044","url":null,"abstract":"Edge computing provides task offloading services to extend the computational capacity of mobile users and reduce task latency. The deadline-awareness offloading algorithm plays a key role in guaranteeing the quality of service (QoS) requirement. Prior studies mainly focus on tasks with strict deadlines. However, some tasks may not always have to be finished before hard deadlines, e.g., multimedia tasks. Tasks with soft deadlines can miss their primary deadlines, but not by too much. This has not been properly considered by existing offloading approaches. In this paper, we propose CONFECT to offload tasks with mixed deadlines. We formulate the problem and prove its hardness. Then, we propose two online algorithms with proven competitive ratios to solve the problem collectively, including an algorithm that assigns tasks to edge servers and an algorithm that adjusts the task execution order on each server. Extensive experiments show that CONFECT outperforms five baseline algorithms.","PeriodicalId":213320,"journal":{"name":"2021 IEEE International Conference on Web Services (ICWS)","volume":"14 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":"128451004","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}

{"title":"Game Theory-Based Task Offloading and Resource Allocation for Vehicular Networks in Edge-Cloud Computing","authors":"Q. Jiang, Xiaolong Xu, Qiang He, Xuyun Zhang, Fei Dai, Lianyong Qi, Wanchun Dou","doi":"10.1109/ICWS53863.2021.00052","DOIUrl":"https://doi.org/10.1109/ICWS53863.2021.00052","url":null,"abstract":"With the development of the vehicular network (VN), emerging driver assistance applications are adhibited in daily life. Commonly, edge computing is adopted to satisfy the timeliness requirements of these applications, as the vehicular devices are usually insufficient in computation resources. Nevertheless, the increasing volume of service requests (SRs) are potential to overload the edge servers (ESs), thus increasing the task execution time. Besides, the randomness and the diversity of the SRs also challenge the dynamic resource allocation for the users. To deal with these challenges, a task offloading and resource allocation scheme based on game theory and reinforcement learning (RL) named TORA is proposed. Specifically, game theory is leveraged to determine the optimal task offloading strategy for improving the quality of service (QoS). Meanwhile, RL is applied to implement the dynamic resource allocation of the ES. Finally, the robust performance of the proposed method is validated by comparative experiments.","PeriodicalId":213320,"journal":{"name":"2021 IEEE International Conference on Web Services (ICWS)","volume":"109 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":"115814190","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}

{"title":"Microservice Pre-Deployment Based on Mobility Prediction and Service Composition in Edge","authors":"Jiale Deng, Bing Li, Jian Wang, Yuqi Zhao","doi":"10.1109/ICWS53863.2021.00078","DOIUrl":"https://doi.org/10.1109/ICWS53863.2021.00078","url":null,"abstract":"As an emerging computing paradigm, mobile edge computing (MEC) is receiving growing attention. In MEC, user requests on software applications are firstly sent to edge servers for processing, which can significantly reduce the latency compared with sending to cloud centers. Furthermore, a software application adopting the popular microservice architecture usually contains multiple intercommunicating microservices. This suggests that the software used by a moving user will invoke different microservices on different locations. However, a microservice request may fail as no corresponding microservice is deployed on nearby edge servers due to resource limitation and coverage limitation. Moreover, if the user is moving at high speed, the user may leave the coverage of the edge server before receiving a response. To address this issue, we propose a microservice pre-deployment approach by integrating mobility prediction and service composition. Our work aims to improve the success rate of both request and response for multi-users while reducing the resource cost of pre-deployment. Three groups of experiments demonstrate that our approach can significantly improve the performance of microservice pre-deployment compared with several baseline approaches.","PeriodicalId":213320,"journal":{"name":"2021 IEEE International Conference on Web Services (ICWS)","volume":"53 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":"114669665","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}

{"title":"Transfer Learning for Web Services Classification","authors":"Yilong Yang, Zhao-Fa Li, Jing Zhang, Yang Chen","doi":"10.1109/ICWS53863.2021.00036","DOIUrl":"https://doi.org/10.1109/ICWS53863.2021.00036","url":null,"abstract":"Web service classification is one of the common approaches to discover and reuse services. Machine learning methods are widely used for web service classification. However, due to the limited high-quality services in the public dataset, the state-of-the-art deep learning methods can not achieve high accuracy. In this paper, we propose a transfer learning approach Tr-ServeNet to reuse the knowledge of the App classification problem for web service classification. We pre-train a deep learning model for the App classification problem, in which the dataset contains high-quality data from Apple Store, and then transfer the embedded and extracted features to assist web service classification. To demonstrate the effectiveness of our approach, we compare the proposed method with other existing machine learning methods on the 50-category benchmark with 10, 000 real-world web services. The experimental results indicate that the proposed transfer learning method can reach the highest Top-1 accuracy in the benchmark of service classification.","PeriodicalId":213320,"journal":{"name":"2021 IEEE International Conference on Web Services (ICWS)","volume":"48 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":"128114596","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}

{"title":"SRaSLR: A Novel Social Relation Aware Service Label Recommendation Model","authors":"Yeqi Zhu, Mingyi Liu, Zhiying Tu, Tonghua Su, Zhongjie Wang","doi":"10.1109/ICWS53863.2021.00024","DOIUrl":"https://doi.org/10.1109/ICWS53863.2021.00024","url":null,"abstract":"With the rapid development of new technologies such as cloud, edge and mobile computing, the number and diversity of available services are dramatically exploding and services have become increasingly important to people's daily work and life. As a consequence, using service label recommendation techniques to automatically categorize services plays a crucial role in many service computing tasks, such as service discovery, service composition, and service organization. There have been many service label recommendation studies that have achieved remarkable performance. However, these studies mainly focus on using the text information in service profiles to recommend labels for services while overlooking those social relations that widely exist among services. We argue that such social relations can help to obtain more precise recommendation results. In this paper, we propose a novel Social Relation aware Service Label Recommendation model called SRaSLR, which combines text information in service profiles and social network relations among services. A deep learning based model is constructed based on feature fusion of the two perspectives. We conduct extensive experiments on the real-world Programmable Web dataset, and the experiment results show that SRaSLR yields better performance than existing methods. Additionally, we discuss how service social network affects service label recommendation performance based on the experiment results.","PeriodicalId":213320,"journal":{"name":"2021 IEEE International Conference on Web Services (ICWS)","volume":"128 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":"126930456","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}