2020 IEEE International Conference on Web Services (ICWS)最新文献_第4页

Multivariate QoS Monitoring in Mobile Edge Computing based on Bayesian Classifier and Rough Set 基于贝叶斯分类器和粗糙集的移动边缘计算多变量QoS监控

2020 IEEE International Conference on Web Services (ICWS) Pub Date : 2020-10-01 DOI: 10.1109/ICWS49710.2020.00032

Pengcheng Zhang, Yaling Zhang, Hai Dong, Huiying Jin

{"title":"Multivariate QoS Monitoring in Mobile Edge Computing based on Bayesian Classifier and Rough Set","authors":"Pengcheng Zhang, Yaling Zhang, Hai Dong, Huiying Jin","doi":"10.1109/ICWS49710.2020.00032","DOIUrl":"https://doi.org/10.1109/ICWS49710.2020.00032","url":null,"abstract":"Mobile edge computing transfers computing and storage from traditional cloud servers to edge servers, presenting new challenges to quality assurance of edge services. Quality of Service (QoS) is considered as a defacto standard to evaluate similar services with different quality. Given the fact that QoS values are highly dynamic in complex edge environments, QoS monitoring is viewed as a promising technique to comprehensively and effectively understand QoS status of edge services. Due to the distributed storage of historical QoS data and the changeable edge environments, traditional QoS monitoring approaches cannot be directly applied into mobile edge computing. To address this problem, this paper proposes a novel multivariate QoS monitoring approach, called Rs-mBSRM (multivariate BayeSian Runtime Monitoring using Rough set), First, the weights of different QoS attributes are quantified and obtained according to the historical samples based on rough set theory. Second, a Bayesian classifier is constructed for each corresponding edge server during the training stage. Finally, during the monitoring stage, considering the distributed data storage, the classifier is dynamically switched and the attribute weights are also updated due to user mobility. Our experimental results on public data sets show that Rs-mBSRM is better than existing QoS monitoring approaches and is more suitable for mobile edge computing.","PeriodicalId":338833,"journal":{"name":"2020 IEEE International Conference on Web Services (ICWS)","volume":"450 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115729739","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

PrTaurus: An Availability-Enhanced EMR Service on Preemptible Cloud Instances PrTaurus:基于可抢占云实例的可用性增强EMR服务

2020 IEEE International Conference on Web Services (ICWS) Pub Date : 2020-10-01 DOI: 10.1109/ICWS49710.2020.00074

Junming Ma, Yan Li, Xiangqun Chen, Donggang Cao

{"title":"PrTaurus: An Availability-Enhanced EMR Service on Preemptible Cloud Instances","authors":"Junming Ma, Yan Li, Xiangqun Chen, Donggang Cao","doi":"10.1109/ICWS49710.2020.00074","DOIUrl":"https://doi.org/10.1109/ICWS49710.2020.00074","url":null,"abstract":"EMR (Elastic Map Reduce) is a service provided by mainstream cloud vendors for data processing users to directly obtain well-managed Hadoop YARN clusters on the cloud. Preemptible instance is a kind of cloud server that is cheap but is likely to be reclaimed by cloud vendors suddenly. Running EMR clusters on preemptible instances relies on YARN's own fault-tolerance, which is limited. In this paper, we present PrTaurus as an availability-enhanced EMR service on preemptible instances. PrTaurus integrates a system-level checkpoint capability based on Docker into YARN to further improve its fault-tolerance. In addition, PrTaurus's scheduling strategy takes advantage of Alibaba Cloud's one-hour protection policy. Furthermore, a new method that comprehensively considers cost-efficiency, preemption risk and overhead is proposed to select cluster instances. We evaluated PrTaurus through simulations on real-world workload and instance price traces. Experimental results show that compared with the existing EMR clusters running on preemptible instances, PrTaurus significantly reduces cost (13.0%-74.6%), instance preemptions (60.3%-88.9%), and task preemptions (86.0 % – 98.6 %).","PeriodicalId":338833,"journal":{"name":"2020 IEEE International Conference on Web Services (ICWS)","volume":"215 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122818589","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

PB-Worker: A Novel Participating Behavior-based Worker Ability Model for General Tasks on Crowdsourcing Platforms PB-Worker:一种新的基于参与行为的众包平台一般任务员工能力模型

2020 IEEE International Conference on Web Services (ICWS) Pub Date : 2020-10-01 DOI: 10.1109/ICWS49710.2020.00012

Qianli Xing, Weiliang Zhao, Jian Yang, Jia Wu, Qi Wang

{"title":"PB-Worker: A Novel Participating Behavior-based Worker Ability Model for General Tasks on Crowdsourcing Platforms","authors":"Qianli Xing, Weiliang Zhao, Jian Yang, Jia Wu, Qi Wang","doi":"10.1109/ICWS49710.2020.00012","DOIUrl":"https://doi.org/10.1109/ICWS49710.2020.00012","url":null,"abstract":"General tasks on crowdsourcing platforms attract more and more workers with different skills and experiences. Existing approaches only leverage the information from tasks with feedback to evaluate worker ability. However, there are millions of tasks without feedback on the platforms. The participating behavior of workers involved in these tasks has not been exploited. In this work, we propose a worker ability model PB-Worker to support general tasks on crowdsourcing platforms. We model the worker latent relation and task latent relation by exploiting the worker participating behavior. To the best of our knowledge, this is the first work to consider the worker participating behavior. Our model is a semi-supervised model that can cover tasks with feedback and tasks without feedback. We employ the ladder network to generate the representations of workers and employ the neural network to predict the worker ability scores. A set of experiments against the real-world dataset from the Zhubajie platform has been conducted. Experimental results show that the output quality of the proposed approach is better than the existing baseline methods.","PeriodicalId":338833,"journal":{"name":"2020 IEEE International Conference on Web Services (ICWS)","volume":"145 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122146035","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: 0

Joint Availability- and Traffic-aware Placement of Parallelized Service Chain in NFV-enabled Data Center 支持nfv的数据中心中并行服务链的联合可用性和流量感知布局

2020 IEEE International Conference on Web Services (ICWS) Pub Date : 2020-10-01 DOI: 10.1109/ICWS49710.2020.00035

Meng Wang, B. Cheng, Junliang Chen

引用次数: 2

Root-Cause Metric Location for Microservice Systems via Log Anomaly Detection 基于日志异常检测的微服务系统根本原因度量定位

2020 IEEE International Conference on Web Services (ICWS) Pub Date : 2020-10-01 DOI: 10.1109/ICWS49710.2020.00026

Lingzhi Wang, Nengwen Zhao, Junjie Chen, Pinnong Li, Wenchi Zhang, Kaixin Sui

{"title":"Root-Cause Metric Location for Microservice Systems via Log Anomaly Detection","authors":"Lingzhi Wang, Nengwen Zhao, Junjie Chen, Pinnong Li, Wenchi Zhang, Kaixin Sui","doi":"10.1109/ICWS49710.2020.00026","DOIUrl":"https://doi.org/10.1109/ICWS49710.2020.00026","url":null,"abstract":"Microservice systems are typically fragile and failures are inevitable in them due to their complexity and large scale. However, it is challenging to localize the root-cause metric due to its complicated dependencies and the huge number of various metrics. Existing methods are based on either correlation between metrics or correlation between metrics and failures. All of them ignore the key data source in microservice, i.e., logs. In this paper, we propose a novel root-cause metric localization approach by incorporating log anomaly detection. Our approach is based on a key observation, the value of root-cause metric should be changed along with the change of the log anomaly score of the system caused by the failure. Specifically, our approach includes two components, collecting anomaly scores by log anomaly detection algorithm and identifying root-cause metric by robust correlation analysis with data augmentation. Experiments on an open-source benchmark microservice system have demonstrated our approach can identify root-cause metrics more accurately than existing methods and only require a short localization time. Therefore, our approach can assist engineers to save much effort in diagnosing and mitigating failures as soon as possible.","PeriodicalId":338833,"journal":{"name":"2020 IEEE International Conference on Web Services (ICWS)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127412127","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: 35

A Novel Dual-Graph Convolutional Network based Web Service Classification Framework 一种新的基于双图卷积网络的Web服务分类框架

2020 IEEE International Conference on Web Services (ICWS) Pub Date : 2020-10-01 DOI: 10.1109/ICWS49710.2020.00043

Xin Wang, Jin Liu, Xiao Liu, Xiaohui Cui, Hao Wu

{"title":"A Novel Dual-Graph Convolutional Network based Web Service Classification Framework","authors":"Xin Wang, Jin Liu, Xiao Liu, Xiaohui Cui, Hao Wu","doi":"10.1109/ICWS49710.2020.00043","DOIUrl":"https://doi.org/10.1109/ICWS49710.2020.00043","url":null,"abstract":"Automated service classification is the foundation for service discovery and service composition. Currently, many existing methods extracting features from functional description documents suffer the problem of data sparsity. However, beside functional description documents, the Web API ecosystem has accumulated a wealth of information that can be used to improve the accuracy of Web service (API) classification. At the moment, there is an absence of a unified way to combine functional description documents with other sources of information (e.g., attributes, interactions and external knowledge) accumulated in the Web API ecosystem for API classification. To address this issue, we present a dual-GCN framework that can effectively suppress the noise propagation of textual contents by distinguishing functional description documents and other sources of information (specifically Mashup-API co-invocation patterns by default in this paper) for API classification. This framework is extensible with the ability to include different sources of information accumulated in the Web API ecosystem. Comprehensive experiments on a real-world public dataset demonstrate that our proposed method can outperform various representative methods for API classification.","PeriodicalId":338833,"journal":{"name":"2020 IEEE International Conference on Web Services (ICWS)","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127479650","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: 9

A Decentralized Collaborative Approach to Online Edge User Allocation in Edge Computing Environments 边缘计算环境中在线边缘用户分配的分散协作方法

2020 IEEE International Conference on Web Services (ICWS) Pub Date : 2020-10-01 DOI: 10.1109/ICWS49710.2020.00045

Qinglan Peng, Yunni Xia, Yan Wang, Chunrong Wu, Wanbo Zheng, Xin Luo, Shanchen Pang, Yong Ma, Chunxu Jiang

{"title":"A Decentralized Collaborative Approach to Online Edge User Allocation in Edge Computing Environments","authors":"Qinglan Peng, Yunni Xia, Yan Wang, Chunrong Wu, Wanbo Zheng, Xin Luo, Shanchen Pang, Yong Ma, Chunxu Jiang","doi":"10.1109/ICWS49710.2020.00045","DOIUrl":"https://doi.org/10.1109/ICWS49710.2020.00045","url":null,"abstract":"Edge computing is a promising paradigm that can boost the performance of novel mobile applications and energize the real-time governance of Internet-of-Things (IoT) big data. In edge computing, mobile application vendors are allowed to employ edge resources to speed up end-users' applications in an elastic and on-demand manner. However, due to the complex geographical distribution of edge servers and users, how to decide the most appropriate destination edge server to hire and how to decide the corresponding user-server allocation plan with as-low-as-possible monetary cost are the key problems for application vendors. Instead of assuming a simultaneous-batch-arrival pattern of incoming users and considering static optimization of the Edge User Allocation (EUA) problem by most existing studies, in this paper, we consider an online EUA problem where users' arrival and departure follow a general pattern. We take the long-term edge user allocation rate and edge server leasing cost as scheduling targets and propose a decentralized collaborative and fuzzy-control-based approach to yielding real-time user-edge-server allocation schedules. In this approach, edge users are allowed to independently make their own allocation decision only based on local information (i.e., the status of nearby edge servers). Experiments on real-world edge datasets demonstrate our approach outperforms state-of-the-art approaches in terms of long-term allocation rate and system cost.","PeriodicalId":338833,"journal":{"name":"2020 IEEE International Conference on Web Services (ICWS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127007928","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: 7

PhishTrim: Fast and adaptive phishing detection based on deep representation learning PhishTrim:基于深度表示学习的快速自适应网络钓鱼检测

2020 IEEE International Conference on Web Services (ICWS) Pub Date : 2020-10-01 DOI: 10.1109/ICWS49710.2020.00030

Lei Zhang, Peng Zhang

引用次数: 7

Leveraging Social Networks to Enhance Effective Coverage for Mobile Crowdsensing 利用社交网络增强移动众测的有效覆盖

2020 IEEE International Conference on Web Services (ICWS) Pub Date : 2020-10-01 DOI: 10.1109/ICWS49710.2020.00057

Wei Liu, Xiaofeng Gao

{"title":"Leveraging Social Networks to Enhance Effective Coverage for Mobile Crowdsensing","authors":"Wei Liu, Xiaofeng Gao","doi":"10.1109/ICWS49710.2020.00057","DOIUrl":"https://doi.org/10.1109/ICWS49710.2020.00057","url":null,"abstract":"With the development of the Internet of Things and smart city, the demand for mobile crowdsensing (MCS) is increasing. Most state-of-the-art studies in MCS assume that the participants are those who have registered with the MCS platform. In this paper, we propose to exploit social network for MCS worker recruitment instead of limiting participants to the platform. MCS platform can motivate more users to join in the task by leveraging the social influence of seed workers. Inspired by this, we first propose a social influence propagation model for MCS task. Considering the constraint of budget, our objective is to maximize the effective sensing coverage by selecting a limited number of seed workers, which is formulated as MESC problem. Based on the voting theory, a heuristic algorithm named as KT Voting is proposed to select seed workers. KT Voting algorithm allows users to vote for the most influential user to themselves and add a weight to their vote based on their sensing locations. After that, seed workers are selected based on the votes received. Extensive experiments based on two real-world data sets verify the effectiveness and efficiency of the proposed KT Voting algorithm.","PeriodicalId":338833,"journal":{"name":"2020 IEEE International Conference on Web Services (ICWS)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127168393","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

Title Page i 第1页

2020 IEEE International Conference on Web Services (ICWS) Pub Date : 2020-10-01 DOI: 10.1109/icws49710.2020.00001

引用次数: 0