2021 IEEE World Congress on Services (SERVICES)最新文献

Modeling and Measuring for Emotion Communication based on DIKW 基于DIKW的情感交流建模与测量

2021 IEEE World Congress on Services (SERVICES) Pub Date : 2021-09-01 DOI: 10.1109/services51467.2021.00031

Ting Hu, Yucong Duan

引用次数: 6

Dynamic Trust Enforcing Pricing Scheme for Sensors-as-a-Service in Sensor-Cloud Infrastructure 传感器云基础设施中传感器即服务的动态信任强制定价方案

2021 IEEE World Congress on Services (SERVICES) Pub Date : 2021-09-01 DOI: 10.1109/services51467.2021.00018

A. Chakraborty, A. Mondal, Arijit Roy, S. Misra

引用次数: 1

A Decentralized Runtime Environment for Service Collaboration: the Architecture and a Case Study 服务协作的分散运行时环境:体系结构和案例研究

2021 IEEE World Congress on Services (SERVICES) Pub Date : 2021-09-01 DOI: 10.1109/services51467.2021.00044

Jing Gao, Guiling Wang, Zhongguo Yang, Zhuofeng Zhao

引用次数: 0

Modeling and Performance Analysis on Federated Learning in Edge Computing 边缘计算中联邦学习的建模与性能分析

2021 IEEE World Congress on Services (SERVICES) Pub Date : 2021-09-01 DOI: 10.1109/services51467.2021.00034

Q. Duan, Maryam Roshanaei

{"title":"Modeling and Performance Analysis on Federated Learning in Edge Computing","authors":"Q. Duan, Maryam Roshanaei","doi":"10.1109/services51467.2021.00034","DOIUrl":"https://doi.org/10.1109/services51467.2021.00034","url":null,"abstract":"Federated Learning (FL) deployed in edge computing may achieve some advantages such as private data protection, communication cost reduction, and lower training latency compared to cloud-centric training approaches. The Anything-as-a-Service (XaaS) paradigm, as the main service provisioning model in edge computing, enables various flexible FL deployments. On the other hand, the distributed nature of FL together with the highly diverse computing and networking infrastructures in an edge environment introduce extra latency that may degrade FL performance. Therefore, delay performance evaluation on edge-based FL systems becomes an important research topic. However, XaaS-based FL deployment brings new challenges to performance analysis that cannot be well addressed by conventional analytical approaches. In this paper, we attempt to address such challenges by proposing a profile-based modeling and analysis method for evaluating delay performance of edge-based FL systems. The insights obtained from the modeling and analysis may offer useful guidelines to various aspects of FL design. Application of network calculus techniques makes the proposed method general and flexible, thus may be applied to FL systems deployed upon the heterogeneous edge infrastructures.","PeriodicalId":210534,"journal":{"name":"2021 IEEE World Congress on Services (SERVICES)","volume":"20 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":"115933838","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

In Memoriam: Professor Mikio Aoyama 纪念:青山幸男教授

2021 IEEE World Congress on Services (SERVICES) Pub Date : 2021-09-01 DOI: 10.1109/services51467.2021.00011

引用次数: 0

Message from Services Workshop Program Chairs 来自服务研讨会项目主席的信息

2021 IEEE World Congress on Services (SERVICES) Pub Date : 2021-09-01 DOI: 10.1109/services51467.2021.00007

引用次数: 0

Plenary Panel 3 - Advances & Challenges in Software Service Engineering 全体讨论小组3 -软件服务工程的进展与挑战

2021 IEEE World Congress on Services (SERVICES) Pub Date : 2021-09-01 DOI: 10.1109/services51467.2021.00060

引用次数: 0

Cache-based Executive Request Dispatching Method in The Distributed Workflow System 分布式工作流系统中基于缓存的执行请求调度方法

2021 IEEE World Congress on Services (SERVICES) Pub Date : 2021-09-01 DOI: 10.1109/services51467.2021.00046

Bo Lv, Weilong Ding, Ji Liu

引用次数: 0

A Novel Method for Network Traffic Prediction Using Residual Mogrifier GRU 一种基于残差Mogrifier GRU的网络流量预测新方法

2021 IEEE World Congress on Services (SERVICES) Pub Date : 2021-09-01 DOI: 10.1109/services51467.2021.00035

Jinyu Tian, Jing Qin, Li-Ming Chen, Hui Fang, Zumin Wang

{"title":"A Novel Method for Network Traffic Prediction Using Residual Mogrifier GRU","authors":"Jinyu Tian, Jing Qin, Li-Ming Chen, Hui Fang, Zumin Wang","doi":"10.1109/services51467.2021.00035","DOIUrl":"https://doi.org/10.1109/services51467.2021.00035","url":null,"abstract":"Network traffic prediction is essential for network management and resource scheduling within Web information systems. However, existing prediction methods have difficulty fitting mutation values in traffic time-series data and are still inadequate in terms of precision. Here we describe a method for prediction using multimodal web traffic data. The method creates multi-dimensional time series on request traffic, response traffic, and abnormal code traffic, and uses the rich information contained in the different sequences in the preceding time window to make inferences about the traffic scale in subsequent time windows. In addition, we propose an improved algorithm based on the Gated Recurrent Unit (GRU) to reduce the prediction error. The algorithm introduces the residual structure into a stacked multi-layer recurrent network structure and uses the Mogrifier structure to interact the information before it is fed to the gating unit. The experimental results show that the improved method leads to a further reduction in the error between the predicted and true values, providing high usability in the field of network traffic prediction.","PeriodicalId":210534,"journal":{"name":"2021 IEEE World Congress on Services (SERVICES)","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":"134045704","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: 2

Privacy-preserving Diverse Keyword Search and Online Pre-diagnosis in Cloud Computing 云计算中保护隐私的多关键字搜索与在线预诊断

2021 IEEE World Congress on Services (SERVICES) Pub Date : 2021-09-01 DOI: 10.1109/services51467.2021.00029

Xiangyu Wang, Jianfeng Ma, Yinbin Miao, Ximeng Liu, Ruikang Yang

{"title":"Privacy-preserving Diverse Keyword Search and Online Pre-diagnosis in Cloud Computing","authors":"Xiangyu Wang, Jianfeng Ma, Yinbin Miao, Ximeng Liu, Ruikang Yang","doi":"10.1109/services51467.2021.00029","DOIUrl":"https://doi.org/10.1109/services51467.2021.00029","url":null,"abstract":"With the development of the Mobile Healthcare Monitoring Network (MHMN), patients’ data collected by body sensors not only allows patients to monitor their health or make online pre-diagnosis but also enables clinicians to make proper decisions by utilizing data mining techniques. In MHMN, patients’ personal data are collected by sensors per second and uploaded to the cloud server as multi-dimension vectors, cloud server stores the personal data as well as sends monitoring information to the hospital when the real-time data is abnormal. Hospital users (i.e., doctors, etc.) may query some samples which contain certain textual keywords or digital keywords in certain ranges for disease diagnosis or medical research. For example, a certain hospital user may query all samples with textual keywords ‘cancer; diabetes’ and digital vectors ‘age’ ∈ [30,50], ‘blood sugar’ ∈ [4,8], ‘heart rhythm’ ∈ [70,80]. Besides, the potential value of massive medical data has attracted considerable interests recently, for example, valuable results in diagnosis model can be yield with large-scale aggregation analysis of personal medical data. The cloud server can build a diagnosis model using data mining technology over massive data, so that hospital users or pre-diagnosis users upload medical data (i.e., age, blood pressure, blood sugar, etc.) to the cloud for diagnosis.","PeriodicalId":210534,"journal":{"name":"2021 IEEE World Congress on Services (SERVICES)","volume":"70 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":"128602563","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: 4