2022 International Conference on Service Science (ICSS)最新文献

Overbooking-enabled Virtual Machine Deployment Approach in Mobile Edge Computing 移动边缘计算中启用超额预订的虚拟机部署方法

2022 International Conference on Service Science (ICSS) Pub Date : 2022-05-01 DOI: 10.1109/ICSS55994.2022.00041

Bingyi Hu, Jixun Gao, Quanzhen Huang, Huaichen Wang, Yanxin Hu, Jialei Liu, Yanmin Ge

{"title":"Overbooking-enabled Virtual Machine Deployment Approach in Mobile Edge Computing","authors":"Bingyi Hu, Jixun Gao, Quanzhen Huang, Huaichen Wang, Yanxin Hu, Jialei Liu, Yanmin Ge","doi":"10.1109/ICSS55994.2022.00041","DOIUrl":"https://doi.org/10.1109/ICSS55994.2022.00041","url":null,"abstract":"Mobile Edge Computing (MEC) integrates computing, storage and other resources on the edge of the network and constructs a unified user service platform. Then, according to the principle of nearest service, MEC responds to the task requests of the edge nodes in time and effectively processes them. In MEC, edge servers are virtualized into several slots so that resources can be shared among different mobile users. However, there are many unpredictable risks in MEC, these risks can cause edge servers to fail, the virtual machine deployed in the server slot fails and the task cannot be executed normally. The introduction of primary-backup virtual machines solves this problem well. However, when the primary virtual machine is working normally, its backup virtual machine is idle, this will result in a waste of resources. In order to improve the resource utilization of the system, this paper firstly overbooks the backup virtual machine reasonably, and then formulates the virtual machine deployment problem as a combinatorial optimization problem. Finally, Virtual Machine Deployment Algorithm (VMDA) is proposed based on genetic algorithm. With the increase of the number of algorithm iterations and the population size of the virtual machine deployment scheme, there may be more optimal virtual machine deployment scheme individuals in the population. Therefore, the algorithm can obtain the approximate optimal value of resource utilization within the risk range allowed by the system, and the algorithm is compared with two other typical bin packing algorithms. The results confirm that VMDA outperforms the other two algorithms.","PeriodicalId":327964,"journal":{"name":"2022 International Conference on Service Science (ICSS)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116448935","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

Generalizing STNU to Model Non-functional Constraints for Business Processes 将STNU泛化为业务流程的非功能约束建模

2022 International Conference on Service Science (ICSS) Pub Date : 2022-05-01 DOI: 10.1109/ICSS55994.2022.00024

Jun Peng, Jingwei Zhu, L. Zhang

引用次数: 2

Congestion Detection and Link Control via Feedback in RDMA Transmission RDMA传输中基于反馈的拥塞检测和链路控制

2022 International Conference on Service Science (ICSS) Pub Date : 2022-05-01 DOI: 10.1109/ICSS55994.2022.00010

Tianshi Wang, Hongwei Kan, Qibo Sun, Shan Xiao, Shangguang Wang

引用次数: 1

Automatic Scheduling Technology of Computing Power Network Driven by Knowledge Graph 知识图驱动的计算能力网络自动调度技术

2022 International Conference on Service Science (ICSS) Pub Date : 2022-05-01 DOI: 10.1109/ICSS55994.2022.00032

Yanheng Bi, Yingchi Long, Yanzheng Jin, Shengwen Zheng, Huaiyuan Liu, Hongzhi Wang

{"title":"Automatic Scheduling Technology of Computing Power Network Driven by Knowledge Graph","authors":"Yanheng Bi, Yingchi Long, Yanzheng Jin, Shengwen Zheng, Huaiyuan Liu, Hongzhi Wang","doi":"10.1109/ICSS55994.2022.00032","DOIUrl":"https://doi.org/10.1109/ICSS55994.2022.00032","url":null,"abstract":"In recent years, the demand for computing resources of AI industry is urgent because of the data explosion, which promoted the construction of computing power networks in the new era for operators. From the cloud network era to today's computing power network, stricter requirements are proposed to ensure the efficiency and security of computing services. Despite computing power scheduling technologies such as on-demand edge computing and efficient compute first network, knowledge graph techniques for graphs are less explored. As a new technology that can express the relationship between nodes in the graph extremely easily, knowledge graph has a natural advantage in expressing feature information of computing nodes in computing power network. Therefore, a novel knowledge graph representation for the architecture of computing power networks is proposed. And the knowledge graph of the computing power network is constructed by using the knowledge representation method. The scheduling tasks of computing power network is automatically executed by the proposed knowledge driven method based on the constructed knowledge graph. Different with the current scheduling technology of computing power network, the model will theoretically become more and more efficient and accurate with continuously addition of knowledge.","PeriodicalId":327964,"journal":{"name":"2022 International Conference on Service Science (ICSS)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130479958","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

MRNN-SA: A Multi-dimensional Time Series Fault Prediction Service for Power Equipment through Self-attention Network 基于自关注网络的电力设备多维时间序列故障预测服务

2022 International Conference on Service Science (ICSS) Pub Date : 2022-05-01 DOI: 10.1109/ICSS55994.2022.00039

Yongyan Yang, Lihong Yang, Mengda Xing

引用次数: 0

Distributed machine learning based link allocation strategy * 基于分布式机器学习的链路分配策略*

2022 International Conference on Service Science (ICSS) Pub Date : 2022-05-01 DOI: 10.1109/ICSS55994.2022.00044

Yi Yang, Mingkang Song, Jianming Zhou, Peng Dai, Tenghui Ke, Weidong Li, Zhengguan Wu, Xiayan Zheng, Xijin Li

{"title":"Distributed machine learning based link allocation strategy *","authors":"Yi Yang, Mingkang Song, Jianming Zhou, Peng Dai, Tenghui Ke, Weidong Li, Zhengguan Wu, Xiayan Zheng, Xijin Li","doi":"10.1109/ICSS55994.2022.00044","DOIUrl":"https://doi.org/10.1109/ICSS55994.2022.00044","url":null,"abstract":"In the field of machine learning, a machine learning system with multiple nodes is usually used, and each node is used to perform a machine learning distributed training process for a part of the data that is allocated to it and provide a server by performing the machine learning distributed training process. The obtained training result, its machine learning data needs to be transmitted through the network. This paper proposes a link allocation method for distributed machine learning. For machine learning computing nodes distributed across domains, due to inconsistencies in link distance, node performance, and link load, the traffic distribution between computing nodes is unbalanced. Aiming at the complex computing requirements of distributed machine learning, a link pre-allocation method is proposed, which establishes a central server-link-node topology map, integrates link resources, and determines the logical distance of nodes. For the synchronously distributed machine learning training set, preallocate transmission link resources and initiate transmission according to the remaining storage capacity of nodes. In order to improve the network utilization efficiency in the process of machine learning, it can break through the influence of large network transmission delay on the efficiency of distributed machine learning.","PeriodicalId":327964,"journal":{"name":"2022 International Conference on Service Science (ICSS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129717510","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

Probing the Mystery of Cryptocurrency Exchange: The Case Study Based on Mt.Gox 探究加密货币交易之谜:以Mt.Gox为例

2022 International Conference on Service Science (ICSS) Pub Date : 2022-05-01 DOI: 10.1109/ICSS55994.2022.00053

Yuanjun Ding, Weili Chen

引用次数: 1

IoTDM4BPMN: An IoT-Enhanced Decision Making Framework for BPMN 2.0 物联网增强的BPMN 2.0决策框架

2022 International Conference on Service Science (ICSS) Pub Date : 2022-05-01 DOI: 10.1109/ICSS55994.2022.00022

Yusuf Kirikkayis, Florian Gallik, M. Reichert

{"title":"IoTDM4BPMN: An IoT-Enhanced Decision Making Framework for BPMN 2.0","authors":"Yusuf Kirikkayis, Florian Gallik, M. Reichert","doi":"10.1109/ICSS55994.2022.00022","DOIUrl":"https://doi.org/10.1109/ICSS55994.2022.00022","url":null,"abstract":"The relevance of the Internet of Things (IoT) for Business Process Management (BPM) support is increasing. IoT devices enable the collection and exchange of data over the Internet, whereby each physical device is uniquely identifiable through its embedded computing system. BPM, in turn, is concerned with analyzing, discovering, modeling, executing, and monitoring (digitized) business processes. By enhancing BPM systems with IoT capabilities, real-world data can be gathered and considered during process execution to enhance process monitoring as well as IoT-driven decision making. In this context, the aggregation of low-level IoT data into high-level process-relevant data constitutes a fundamental step towards IoT-driven decisions in business processes. This paper presents IoT Decision Making for Business Process Model and Notation (IoTDM4BPMN) a web-based framework for modeling, executing, and monitoring IoT-driven decisions in real-time. We give insights into the design and implementation of IoTDM4BPMN and provide a case study as a first validation that applies IoTDM4BPMN to the modeling, executing, and monitoring of a real-world IoT-driven decision process.","PeriodicalId":327964,"journal":{"name":"2022 International Conference on Service Science (ICSS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130639301","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

HRET: Heterogeneous Information Network for Recommendation in testing and inspection HRET:面向检测推荐的异构信息网络

2022 International Conference on Service Science (ICSS) Pub Date : 2022-05-01 DOI: 10.1109/ICSS55994.2022.00038

Liwen Zhang, Weiping Li, Tong Mo, Weijie Chu

引用次数: 0

A Smart Contract-based Service Platform for Trustworthy Crowd Funding and Crowd Innovation 基于智能合约的可信赖众筹与众创服务平台

2022 International Conference on Service Science (ICSS) Pub Date : 2022-05-01 DOI: 10.1109/ICSS55994.2022.00048

Wenjie Teng, Hanchuan Xu, Zhe Huang, Yunwen Bai, Zhongjie Wang

引用次数: 1