2020 IEEE International Conference on Services Computing (SCC)最新文献_第5页

Summarization of Multidimensional Process Traces for Analysis under Edit-distance Constraints 编辑距离约束下多维过程轨迹分析的总结

2020 IEEE International Conference on Services Computing (SCC) Pub Date : 2020-11-01 DOI: 10.1109/SCC49832.2020.00070

Phuong Nguyen, Vatche Isahagian, Vinod Muthusamy, Aleksander Slominski

引用次数: 1

JTang Dubhe: a Service Pattern Modeling and Analysis System 一个服务模式建模与分析系统

2020 IEEE International Conference on Services Computing (SCC) Pub Date : 2020-11-01 DOI: 10.1109/SCC49832.2020.00064

Jianwei Yin, Siwei Tan, Meng Xi, Jintao Chen, Yongna Wei, Shuiguang Deng

{"title":"JTang Dubhe: a Service Pattern Modeling and Analysis System","authors":"Jianwei Yin, Siwei Tan, Meng Xi, Jintao Chen, Yongna Wei, Shuiguang Deng","doi":"10.1109/SCC49832.2020.00064","DOIUrl":"https://doi.org/10.1109/SCC49832.2020.00064","url":null,"abstract":"Business models now become essential parts of an enterprise’s strategy. Many newly-rising crossover business models require multi-industry companies involved. The communication and cooperation process between the enterprises shows the characteristics of complexity and high uncertainty, which is detrimental to business value analysis. However, existing methods can not quantify the value demands of their stakeholders. Service pattern models business from the perspective of enterprise application. It is executable and suitable for quantitative analysis. In this paper, we cooperate with management experts and propose a new ontology-based modeling and multi-agent-based simulation method that supports the quantitative analysis of the stakeholder’s value demands at the business process level. With the method proposed, a corresponding simulation system is developed, Dubhe. It is a user-friendly modeling, simulation, and visualization tool for corporate decision-makers. Finally, the paper uses a case study to prove that the method and system can evaluate the value of a business process and discover the bottlenecks.","PeriodicalId":274909,"journal":{"name":"2020 IEEE International Conference on Services Computing (SCC)","volume":"48 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":"126663147","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

A Service Computing Framework for Proteomics Analysis and Collaboration of Pathogenic Mechanism Studies 蛋白质组学分析与致病机制协同研究的服务计算框架

2020 IEEE International Conference on Services Computing (SCC) Pub Date : 2020-11-01 DOI: 10.1109/SCC49832.2020.00069

Huaming Chen, Fucun Li, G. Sun, Xuyun Zhang, Xianjun Dong, Lei Wang, Kewen Liao, Haifeng Shen, Jun Shen

{"title":"A Service Computing Framework for Proteomics Analysis and Collaboration of Pathogenic Mechanism Studies","authors":"Huaming Chen, Fucun Li, G. Sun, Xuyun Zhang, Xianjun Dong, Lei Wang, Kewen Liao, Haifeng Shen, Jun Shen","doi":"10.1109/SCC49832.2020.00069","DOIUrl":"https://doi.org/10.1109/SCC49832.2020.00069","url":null,"abstract":"The booming of proteomics data has positioned multiple disciplines and research areas in a more complicated and challenging place. Moreover, the proteomics data of any defined research interests, such as for pathogenic mechanism studies of infectious diseases, have presented unstructured and heterogeneous characteristics. Thus, a service computing framework for proteomics analysis is desired to bring biologists and computer scientists into this area seamlessly and efficiently. With this regard, this work is dedicated to detail the proteomics analysis and collaboration process of pathogenic mechanism studies. We articulate this framework to serve the requirements and ease the task design by broadly reviewing the state-of-the- art research and development efforts and collectively designing different informative stages. Thus, the framework has a focus of distilling different aspects, including data curation, resources distribution, standard construction and computational tasks identification, into the proteomics analysis. The framework is designed as Proteomics Analysis as a Service to deepen the understanding of the interdisciplinary research.","PeriodicalId":274909,"journal":{"name":"2020 IEEE International Conference on Services Computing (SCC)","volume":"14 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":"130224207","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

An Empirical Study of an Open Ecosystem Model for Inclusive Financial Services 普惠金融服务开放生态系统模型的实证研究

2020 IEEE International Conference on Services Computing (SCC) Pub Date : 2020-11-01 DOI: 10.1109/SCC49832.2020.00060

Ko-Yang Wang, G. Lin, Kevin Kuo, Han-Chao Lee, Brick Tsai, Webster Peng

引用次数: 0

Adaptive Request Scheduling for Device Cloud 设备云的自适应请求调度

2020 IEEE International Conference on Services Computing (SCC) Pub Date : 2020-11-01 DOI: 10.1109/SCC49832.2020.00058

Han Dong, Enze Xu, Xiang Jing, Huaqian Cai, Gang Huang

引用次数: 1

Dynamic Task Allocation for Cost-Efficient Edge Cloud Computing 经济高效的边缘云计算动态任务分配

2020 IEEE International Conference on Services Computing (SCC) Pub Date : 2020-11-01 DOI: 10.1109/SCC49832.2020.00036

Shiyao Ding, Donghui Lin

{"title":"Dynamic Task Allocation for Cost-Efficient Edge Cloud Computing","authors":"Shiyao Ding, Donghui Lin","doi":"10.1109/SCC49832.2020.00036","DOIUrl":"https://doi.org/10.1109/SCC49832.2020.00036","url":null,"abstract":"Edge cloud computing systems are widely used to supply various computation services in Internet of Things (IoT). An essential problem is how to efficiently allocate task requests to various edge and cloud servers given task requirements (e.g., response time and required memory space), in order to minimize various costs generated in edge cloud computing. Existing studies on task allocation usually consider the viewpoint of provider cost such as offloading cost, uploading cost and deployment cost. However, the viewpoint of user cost (e.g., server fee) is rarely considered which is becoming an important issue in the deployment of edge cloud computing systems, especially for cost sensitive users like venture companies. In this paper, we study a dynamic task allocation problem in edge cloud computing where both servers’ status and arriving tasks would change along with time; the goal is to search the task allocation policy that can minimize user cost. Specifically, we consider a parallel processing case where a task’s workload can be infinitely divided among the various servers; this causes a huge solution space and makes the problem hard to solve. Thus, we consider an approximate method from the perspective of server coalitions rather than a single server, and propose a dynamic coalition formation algorithm called coalitional R-learning (CR-learning) to guide several edge servers in forming a coalition dynamically. Simulations verify that our algorithm can significantly reduce user cost comparing with some other existing algorithms while shrinking the solution space.","PeriodicalId":274909,"journal":{"name":"2020 IEEE International Conference on Services Computing (SCC)","volume":"49 40","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120888990","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

Temporal-spatial-domanial features oriented modeling framework for Transboundary Service 面向时空特征的跨界服务建模框架

2020 IEEE International Conference on Services Computing (SCC) Pub Date : 2020-11-01 DOI: 10.1109/SCC49832.2020.00063

Min Li, Zhiying Tu, Hanchuan Xu, Zhongjie Wang

{"title":"Temporal-spatial-domanial features oriented modeling framework for Transboundary Service","authors":"Min Li, Zhiying Tu, Hanchuan Xu, Zhongjie Wang","doi":"10.1109/SCC49832.2020.00063","DOIUrl":"https://doi.org/10.1109/SCC49832.2020.00063","url":null,"abstract":"Service model is an important form to describe service functions and non-functional attributes. Many scholars have given detailed model specifications and modeling languages for various aspects such as business processes, service value delivery, service ontology description, service decision making, and case management. The state of the art of the above models and their associations shows that temporal-spatial-domanial features have received little attention, however, which have great significance on service execution and evaluation, especially in the background of Transboundary Service. There-fore, this paper proposes a Transboundary Service modeling framework oriented to temporal-spatial-domanial features. It defines the representation of service domains, and analyzes the relationship between domain and service functional/non-functional model. In addition, a tool is developed to support visual modeling and annotation work based on this framework. Finally, an actual case of Freshhema from Alibaba is used to verify this framework. Compared with the existing modeling framework (e.g. BPMN, VDML), this framework pays special attention to the description and analysis of temporal-spatial-domanial features, and clarifies the dependency relationship between it and service function/non-function attributes, pro-vides the necessary model extensions to provide more detailed support for subsequent model application and optimization.","PeriodicalId":274909,"journal":{"name":"2020 IEEE International Conference on Services Computing (SCC)","volume":"190 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":"133806962","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

Seeding-Based Multi-Objective Evolutionary Algorithms for Multi-Cloud Composite Applications Deployment 基于种子的多目标进化算法在多云组合应用部署中的应用

2020 IEEE International Conference on Services Computing (SCC) Pub Date : 2020-11-01 DOI: 10.1109/SCC49832.2020.00039

Tao Shi, Hui Ma, Gang Chen

{"title":"Seeding-Based Multi-Objective Evolutionary Algorithms for Multi-Cloud Composite Applications Deployment","authors":"Tao Shi, Hui Ma, Gang Chen","doi":"10.1109/SCC49832.2020.00039","DOIUrl":"https://doi.org/10.1109/SCC49832.2020.00039","url":null,"abstract":"There are an increasing number of enterprises deploying their application services to multi-cloud to benefit the advantages brought by cloud computing. The multi-cloud composite applications deployment problem (MCADP) aims to select proper cloud resources from multiple cloud providers at different locations to deploy applications with shared constituent services so as to optimize application performance and deployment cost. Multi-objective evolutionary algorithms (MOEAs) can be utilized to find a set of trade-off solutions for MCADP. During population initialization of MOEAs, seeding strategies can considerably improve the algorithms’ performance. For example, the seeding-based MOEAs, AO-Seed and SO-Seed, introduce a pre-optimization phase to search for solutions to be embedded into the initial population of MOEAs. With the extra optimization overhead, however, the two seeding-based MOEAs can only identify one or a limited few solutions to MCADP utilized by MOEAs. To solve MCADP effectively and efficiently, we propose new seeding-based MOEAs in this paper. The approach can construct application-specific seeds according to problem domain knowledge and build a group of diverse and high-quality solutions for the initial population of MOEAs. Extensive experiments have been conducted on a real-world dataset. The results demonstrate that the proposed seeding-based MOEAs outperform SO-Seed and AO-Seed with less computation cost for MCADP.","PeriodicalId":274909,"journal":{"name":"2020 IEEE International Conference on Services Computing (SCC)","volume":"385 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":"133273079","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 Scenario-based Modeling Method for Crossover Services 基于场景的跨界服务建模方法

2020 IEEE International Conference on Services Computing (SCC) Pub Date : 2020-11-01 DOI: 10.1109/SCC49832.2020.00010

Meng Xi, Jianwei Yin, Yongna Wei, Maolin Zhang, Shuiguang Deng, Ying Li

{"title":"A Scenario-based Modeling Method for Crossover Services","authors":"Meng Xi, Jianwei Yin, Yongna Wei, Maolin Zhang, Shuiguang Deng, Ying Li","doi":"10.1109/SCC49832.2020.00010","DOIUrl":"https://doi.org/10.1109/SCC49832.2020.00010","url":null,"abstract":"With the continuous emergence of large companies, businesses in different domains have been amalgamated, which leads to a prevalence of crossover services. Different from service choreographies and collaborations, the crossover services involve multiple domains and are highly integrated in terms of processes, entities, abilities, and resources. In a crossover service, the elements of independent service are disrupted and reorganized, which leads to the increase of the complexity of traditional modeling methods. This new form of service brings challenges to business analysis and modeling, such as semantic ambiguity, business coupling, and test explosion. In this work, a crossover service-oriented modeling method, namely CoSM, is proposed to help with the construction, reuse, and analysis of crossover services. CoSM is designed based on scenarios, which can be used to slice the processes and route to each other through the triggers. Besides, a prototype platform is built and concepts and processes can be constructed through that. We verify CoSM in a real case and design experiments to compare with pertinent models. Experimental results show that our model can express the same process with fewer connectors.","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":"129000870","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

FDA-VeD: A Future-Demand-Aware Vehicle Dispatching Service FDA-VeD:未来需求感知的车辆调度服务

2020 IEEE International Conference on Services Computing (SCC) Pub Date : 2020-11-01 DOI: 10.1109/SCC49832.2020.00052

Yang Guo, Weiliang Zhao, Jian Yang, Zizhu Zhang, Jia Wu, Tarique Anwar

引用次数: 1