2020 IEEE International Conference on Services Computing (SCC)最新文献

Diversified QoS-Centric Service Recommendation for Uncertain QoS Preferences 不确定QoS偏好下以QoS为中心的多样化业务推荐

2020 IEEE International Conference on Services Computing (SCC) Pub Date : 2020-11-01 DOI: 10.1109/SCC49832.2020.00045

Guosheng Kang, Jianxun Liu, Buqing Cao, Yong Xiao

{"title":"Diversified QoS-Centric Service Recommendation for Uncertain QoS Preferences","authors":"Guosheng Kang, Jianxun Liu, Buqing Cao, Yong Xiao","doi":"10.1109/SCC49832.2020.00045","DOIUrl":"https://doi.org/10.1109/SCC49832.2020.00045","url":null,"abstract":"With the wide adoption of SOA (Service Oriented Architecture), a massive amount of Web services emerge on the Internet. Finding the desired services becomes a challenge. Thus, service recommendation has become of paramount research to relieve users' difficulty in service selection. Existing Web service recommendation approaches employ utility functions or skyline techniques with the assumption that users can provide numerical QoS (Quality of Service) preferences. However, in practice, it is hard for users, even for professional users, to provide specific QoS preferences in their service requests. Thus, how to effectively recommend services to users when their QoS preferences are uncertain is a challenging issue. To solve the problem, this paper focuses on the characteristics of user requirements, and proposes a diversified QoS-centric service recommendation approach for uncertain QoS preferences, by which a list of services with desired QoS fulfillment and diversity are produced. Extensive experiments are conducted on a real-world dataset to demonstrate the effectiveness and efficiency of our approach.","PeriodicalId":274909,"journal":{"name":"2020 IEEE International Conference on Services Computing (SCC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124418650","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

Hybrid Architecture for Handwriting Perceptual Service based on Edge Computing 基于边缘计算的手写体感知服务混合体系结构

2020 IEEE International Conference on Services Computing (SCC) Pub Date : 2020-11-01 DOI: 10.1109/SCC49832.2020.00066

Tonghua Su, Huiyan Wen, Mingyue Zhang, Shuchen Liu, Zhongjie Wang, Xiaofei Xu

引用次数: 1

A Process Convergence Approach for Crossover Services based on Message Flow Partition and Merging 一种基于消息流划分和合并的跨界服务过程收敛方法

2020 IEEE International Conference on Services Computing (SCC) Pub Date : 2020-11-01 DOI: 10.1109/SCC49832.2020.00031

Yiwei Shan, Y. Qiao, Bing Li, Jian Wang

引用次数: 1

Detecting Artifact Anomalies in Microservice-Based Financial Applications 基于微服务的金融应用中的工件异常检测

2020 IEEE International Conference on Services Computing (SCC) Pub Date : 2020-11-01 DOI: 10.1109/SCC49832.2020.00061

F. Fahmi, Pei-Shu Huang, Feng-Jian Wang

引用次数: 1

A Survey of Modern Scientific Workflow Scheduling Algorithms and Systems in the Era of Big Data 大数据时代现代科学工作流调度算法与系统综述

2020 IEEE International Conference on Services Computing (SCC) Pub Date : 2020-11-01 DOI: 10.1109/SCC49832.2020.00026

Junwen Liu, Shiyong Lu, D. Che

{"title":"A Survey of Modern Scientific Workflow Scheduling Algorithms and Systems in the Era of Big Data","authors":"Junwen Liu, Shiyong Lu, D. Che","doi":"10.1109/SCC49832.2020.00026","DOIUrl":"https://doi.org/10.1109/SCC49832.2020.00026","url":null,"abstract":"This paper provides a survey of the state-of-the-art workflow scheduling algorithms with the assumption of cloud computing being used as the underlying compute infrastructure in support of large-scale scientific workflows involving big data. The survey also reviews a few selected representative scientific workflow systems in light of usability, performance, popularity, and other prominent features. In contrast to existing related surveys, which most try to be comprehensive in coverage and inevitably fall short in the depth of their coverage on workflow scheduling, this survey puts an emphasis on the two dominant factors in workflow scheduling, the makespan and the monetary cost of workflow execution, resulted in a useful taxonomy of workflow scheduling algorithms as an additional contribution. This survey tries to maintain a good balance between width and depth in its coverage – after a broad review, it spotlights on selected top ten representative scheduling algorithms and top five workflow management systems leveraging cloud infrastructure with an emphasis on support for big data scientific workflows.","PeriodicalId":274909,"journal":{"name":"2020 IEEE International Conference on Services Computing (SCC)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134444088","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: 5

Personality Traits Prediction Based on Users’ Digital Footprints in Social Networks via Attention RNN 基于注意RNN的社交网络用户数字足迹人格特征预测

2020 IEEE International Conference on Services Computing (SCC) Pub Date : 2020-11-01 DOI: 10.1109/SCC49832.2020.00015

Shipeng Wang, Li-zhen Cui, Lei Liu, Xudong Lu, Qingzhong Li

引用次数: 9

An Ethical Multi-Stakeholder Recommender System Based on Evolutionary Multi-Objective Optimization 基于进化多目标优化的伦理多利益相关者推荐系统

2020 IEEE International Conference on Services Computing (SCC) Pub Date : 2020-11-01 DOI: 10.1109/SCC49832.2020.00074

Naime Ranjbar Kermany, Weiliang Zhao, Jian Yang, Jia Wu, L. Pizzato

引用次数: 9

SEED: Confidential Big Data Workflow Scheduling with Intel SGX Under Deadline Constraints SEED:保密大数据工作流调度与英特尔SGX在截止日期限制

2020 IEEE International Conference on Services Computing (SCC) Pub Date : 2020-11-01 DOI: 10.1109/SCC49832.2020.00023

Ishtiaq Ahmed, S. Mofrad, Shiyong Lu, Changxin Bai, Fengwei Zhang, D. Che

{"title":"SEED: Confidential Big Data Workflow Scheduling with Intel SGX Under Deadline Constraints","authors":"Ishtiaq Ahmed, S. Mofrad, Shiyong Lu, Changxin Bai, Fengwei Zhang, D. Che","doi":"10.1109/SCC49832.2020.00023","DOIUrl":"https://doi.org/10.1109/SCC49832.2020.00023","url":null,"abstract":"Recently, cloud platforms play an essential role in large-scale big data analytics and especially running scientific workflows. In contrast to traditional on-premise computing environments, where the number of resources is bounded, cloud computing can provide practically unlimited resources to a workflow application based on a pay-as-you-go pricing model. One challenge of using cloud computing is the protection of the privacy of the confidential workflow’s tasks, whose proprietary algorithm implementations are intellectual properties of the respective stakeholders. Another one is the monetary cost optimization of executing workflows in the cloud while satisfying a user-defined deadline. In this paper, we use the Intel Software Guard eXtensions (SGX) as a Trusted Execution Environment (TEE) to support the confidentiality of individual workflow tasks. Based on this, we propose a deadline-constrained and SGX-aware workflow scheduling algorithm, called SEED (SGX, Efficient, Effective, Deadline Constrained), to address these two challenges. SEED features several heuristics, including exploiting the longest critical paths and reuse of extra times in existing virtual machine instances. Our experiments show that SEED outperforms the representative algorithm, IC-PCP, in most cases in monetary cost while satisfying the given user-defined deadline. To our best knowledge, this is the first workflow scheduling algorithm that considers protecting the confidentiality of workflow tasks in a public cloud computing environment.","PeriodicalId":274909,"journal":{"name":"2020 IEEE International Conference on Services Computing (SCC)","volume":"139 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122913094","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

A Blockchain-based Trustworthy Certification Process for Composite Services 基于区块链的组合服务可信认证流程

2020 IEEE International Conference on Services Computing (SCC) Pub Date : 2020-11-01 DOI: 10.1109/SCC49832.2020.00062

C. Ardagna, M. Anisetti, B. Carminati, E. Damiani, E. Ferrari, Christian Rondanini

引用次数: 4

MV4MS: A Spring Cloud based Framework for the Co-Deployment of Multi-Version Microservices MV4MS:一个基于Spring云的框架，用于协同部署多版本微服务

2020 IEEE International Conference on Services Computing (SCC) Pub Date : 2020-11-01 DOI: 10.1109/SCC49832.2020.00033

Lei Liu, Xiang He, Zhiying Tu, Zhongjie Wang

{"title":"MV4MS: A Spring Cloud based Framework for the Co-Deployment of Multi-Version Microservices","authors":"Lei Liu, Xiang He, Zhiying Tu, Zhongjie Wang","doi":"10.1109/SCC49832.2020.00033","DOIUrl":"https://doi.org/10.1109/SCC49832.2020.00033","url":null,"abstract":"Agile development and the loose coupling of microservices, make continuous delivery/deployment of large, complex service systems become much easier. The microservices are upgraded and released independently and have their own independent version trees. For compatibility, multiple versions of one microservice are to be deployed in the same system to offer slightly different functionalities to different users simultaneously. However, loosely-coupling does not mean multiple microservices keep absolutely independent but there are more or less dependencies among them, and such dependencies occur not only on functionalities but also on the version issue, too. Existing microservice frameworks have no enough capability for multi-version co-deployment and the corresponding version-oriented dependency management. In this paper, a Spring Cloud based framework called MV4MS is introduced for this challenge. It extracts version information from source codes of microservices, builds version dependencies, packs and deploys requisite versions of microservices, and routes user requests to desired versions at run-time. Architecture of MV4MS and detailed design of its components are elaborately introduced. Experiments are conducted in AWS cloud environment, and results show that our framework could reduce the complexity of multi-version microservice co-deployment and ensure the correctness of routing between multi-version microservice.","PeriodicalId":274909,"journal":{"name":"2020 IEEE International Conference on Services Computing (SCC)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125666496","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: 5