Pub Date : 2021-09-01DOI: 10.1109/services51467.2021.00037
Ruyu Yan, Yushun Fan
The number of available online services increases sharply with the development of the Internet. These services typically belong to varying service domains. To address the data-sparse issue, cross-domain recommendation techniques are proposed to transfer the information in relevant service domains to improve the recommendation effects. In this paper, we presented a novel end-to-end cross-domain service recommendation learning framework, named EATN, short for End-to-end Attention Transfer Network, which is different from most existing cross-domain step-by-step learning frameworks. To realize this end-to-end framework, we design a workflow to achieve user preferences cross-domain matching procedure. We capture fine-grained and multi-faceted user preferences by using multiple Multi-Layer Perceptron layers. To reasonably integrate multi-faceted transfer preferences, we design a service-level attention module, which learns weight based on the relevance to services. Finally, it can improve the recommendation effect of cold-start users in the target domain. Extensive experiments on the real-world Amazon dataset show the significant improvement of our proposed EATN framework.
随着互联网的发展，可用的在线服务数量急剧增加。这些服务通常属于不同的服务域。针对数据稀疏问题，提出了跨域推荐技术，在相关服务域中传递信息，提高推荐效果。本文提出了一种新的端到端跨域服务推荐学习框架，即端到端注意力转移网络(end-to-end Attention Transfer Network，简称EATN)，它不同于现有的跨域分步学习框架。为了实现这个端到端框架，我们设计了一个工作流来实现用户偏好的跨域匹配过程。我们通过使用多个多层感知器层来捕获细粒度和多方面的用户偏好。为了合理整合多方面的转移偏好，我们设计了一个服务级关注模块，该模块根据与服务的相关性来学习权重。最后，可以提高目标域冷启动用户的推荐效果。在真实的Amazon数据集上进行的大量实验表明，我们提出的eattn框架有了显著的改进。
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Pub Date : 2021-09-01DOI: 10.1109/services51467.2021.00017
Ashutosh Kumar Singh, Nitin Auluck, Omer F. Rana, S. Nepal
Fog computing extends the capability of cloud services to support latency sensitive applications. Adding fog computing nodes in proximity to a data generation/ actuation source can support data analysis tasks that have stringent deadline constraints. We introduce a real time, security-aware scheduling algorithm that can execute over a fog environment [1 , 2] . The applications we consider comprise of: (i) interactive applications which are less compute intensive, but require faster response time; (ii) computationally intensive batch applications which can tolerate some delay in execution. From a security perspective, applications are divided into three categories: public, private and semi-private which must be hosted over trusted, semi-trusted and untrusted resources. We propose the architecture and implementation of a distributed orchestrator for fog computing, able to combine task requirements (both performance and security) and resource properties.
{"title":"Scheduling Real Tim Security Aware Tasks in Fog Networks","authors":"Ashutosh Kumar Singh, Nitin Auluck, Omer F. Rana, S. Nepal","doi":"10.1109/services51467.2021.00017","DOIUrl":"https://doi.org/10.1109/services51467.2021.00017","url":null,"abstract":"Fog computing extends the capability of cloud services to support latency sensitive applications. Adding fog computing nodes in proximity to a data generation/ actuation source can support data analysis tasks that have stringent deadline constraints. We introduce a real time, security-aware scheduling algorithm that can execute over a fog environment [1 , 2] . The applications we consider comprise of: (i) interactive applications which are less compute intensive, but require faster response time; (ii) computationally intensive batch applications which can tolerate some delay in execution. From a security perspective, applications are divided into three categories: public, private and semi-private which must be hosted over trusted, semi-trusted and untrusted resources. We propose the architecture and implementation of a distributed orchestrator for fog computing, able to combine task requirements (both performance and security) and resource properties.","PeriodicalId":210534,"journal":{"name":"2021 IEEE World Congress on Services (SERVICES)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125441109","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}
Pub Date : 2021-09-01DOI: 10.1109/services51467.2021.00049
Zhaokun Qiu, Long Chen, Xiaoping Li
Task scheduling with multi-dimensional configuration requirements is widely used in cloud platforms such as OpenStack and Kubernetes. In this paper, we consider the problem of scheduling tasks with multi-dimensional configuration to hybrid resources. An energy-aware scheduling algorithm on tasks with multi-dimensional configuration requirements (ESMCR in short) is presented. ESMCR is combined with a decomposition-based multi-objective evolutionary algorithm to minimize energy consumption and provide sufficient capacity for the data center. An entropy-based performance index is modeled to measure the QoS. The experimental results indicate that the proposed algorithms outperform the compared algorithms significantly.
{"title":"Hybrid Cloud Resource Scheduling With Multi-dimensional Configuration Requirements","authors":"Zhaokun Qiu, Long Chen, Xiaoping Li","doi":"10.1109/services51467.2021.00049","DOIUrl":"https://doi.org/10.1109/services51467.2021.00049","url":null,"abstract":"Task scheduling with multi-dimensional configuration requirements is widely used in cloud platforms such as OpenStack and Kubernetes. In this paper, we consider the problem of scheduling tasks with multi-dimensional configuration to hybrid resources. An energy-aware scheduling algorithm on tasks with multi-dimensional configuration requirements (ESMCR in short) is presented. ESMCR is combined with a decomposition-based multi-objective evolutionary algorithm to minimize energy consumption and provide sufficient capacity for the data center. An entropy-based performance index is modeled to measure the QoS. The experimental results indicate that the proposed algorithms outperform the compared algorithms significantly.","PeriodicalId":210534,"journal":{"name":"2021 IEEE World Congress on Services (SERVICES)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117112715","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}
Pub Date : 2021-09-01DOI: 10.1109/services51467.2021.00058
{"title":"Plenary Panel 1 - Cloud HPC: Exploring the Growing Synergy between Cloud and High Performance Computing","authors":"","doi":"10.1109/services51467.2021.00058","DOIUrl":"https://doi.org/10.1109/services51467.2021.00058","url":null,"abstract":"","PeriodicalId":210534,"journal":{"name":"2021 IEEE World Congress on Services (SERVICES)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133615977","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}
Pub Date : 2021-09-01DOI: 10.1109/services51467.2021.00020
Zhuo Ma, Jianfeng Ma, Yinbin Miao, Ximeng Liu, K. Choo, Ruikang Yang, Xiangyu Wang
In the era of machine learning, mobile users are able to submit their symptoms to doctors at any time, anywhere for personal diagnosis. It is prevalent to exploit edge computing for real-time diagnosis services in order to reduce transmission latency. Although data-driven machine learning is powerful, it inevitably compromises privacy by relying on vast amounts of medical data to build a diagnostic model. Therefore, it is necessary to protect data privacy without accessing local data. However, the blossom has also been accompanied by various problems, i.e., the limitation of training data, vulnerabilities, and privacy concern. As a solution to these above challenges, in this paper, we design a lightweight privacy-preserving medical diagnosis mechanism on edge. Our method redesigns the extreme gradient boosting (XGBoost) model based on the edge-cloud model, which adopts encrypted model parameters instead of local data to reduce amounts of ciphertext computation to plaintext computation, thus realizing lightweight privacy preservation on resource-limited edges. Additionally, the proposed scheme is able to provide a secure diagnosis on edge while maintaining privacy to ensure an accurate and timely diagnosis. The proposed system with secure computation could securely construct the XGBoost model with lightweight overhead, and efficiently provide a medical diagnosis without privacy leakage. Our security analysis and experimental evaluation indicate the security, effectiveness, and efficiency of the proposed system.
{"title":"Lightweight Privacy-preserving Medical Diagnosis in Edge Computing","authors":"Zhuo Ma, Jianfeng Ma, Yinbin Miao, Ximeng Liu, K. Choo, Ruikang Yang, Xiangyu Wang","doi":"10.1109/services51467.2021.00020","DOIUrl":"https://doi.org/10.1109/services51467.2021.00020","url":null,"abstract":"In the era of machine learning, mobile users are able to submit their symptoms to doctors at any time, anywhere for personal diagnosis. It is prevalent to exploit edge computing for real-time diagnosis services in order to reduce transmission latency. Although data-driven machine learning is powerful, it inevitably compromises privacy by relying on vast amounts of medical data to build a diagnostic model. Therefore, it is necessary to protect data privacy without accessing local data. However, the blossom has also been accompanied by various problems, i.e., the limitation of training data, vulnerabilities, and privacy concern. As a solution to these above challenges, in this paper, we design a lightweight privacy-preserving medical diagnosis mechanism on edge. Our method redesigns the extreme gradient boosting (XGBoost) model based on the edge-cloud model, which adopts encrypted model parameters instead of local data to reduce amounts of ciphertext computation to plaintext computation, thus realizing lightweight privacy preservation on resource-limited edges. Additionally, the proposed scheme is able to provide a secure diagnosis on edge while maintaining privacy to ensure an accurate and timely diagnosis. The proposed system with secure computation could securely construct the XGBoost model with lightweight overhead, and efficiently provide a medical diagnosis without privacy leakage. Our security analysis and experimental evaluation indicate the security, effectiveness, and efficiency of the proposed system.","PeriodicalId":210534,"journal":{"name":"2021 IEEE World Congress on Services (SERVICES)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127654442","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}
Pub Date : 2021-09-01DOI: 10.1109/services51467.2021.00025
Shangguang Wang, Ao Zhou, Ruo Bao, Chou Wu
With the popularization of the cloud, an increasing number of users and developers need to combine multiple different services to satisfy their requirements in the cloud. Consequently, the question of how to combine these services that are published by different service providers in the cloud has become an active focus of research in service-oriented cloud systems. Service composition is the core technology of service-oriented cloud systems.
{"title":"Towards Green Service Composition Approach in the Cloud","authors":"Shangguang Wang, Ao Zhou, Ruo Bao, Chou Wu","doi":"10.1109/services51467.2021.00025","DOIUrl":"https://doi.org/10.1109/services51467.2021.00025","url":null,"abstract":"With the popularization of the cloud, an increasing number of users and developers need to combine multiple different services to satisfy their requirements in the cloud. Consequently, the question of how to combine these services that are published by different service providers in the cloud has become an active focus of research in service-oriented cloud systems. Service composition is the core technology of service-oriented cloud systems.","PeriodicalId":210534,"journal":{"name":"2021 IEEE World Congress on Services (SERVICES)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125595169","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}
Pub Date : 2021-09-01DOI: 10.1109/services51467.2021.00062
{"title":"Plenary Panel 5 - Future Trends of Strategic Advances of Services Computing Using AI Technologies","authors":"","doi":"10.1109/services51467.2021.00062","DOIUrl":"https://doi.org/10.1109/services51467.2021.00062","url":null,"abstract":"","PeriodicalId":210534,"journal":{"name":"2021 IEEE World Congress on Services (SERVICES)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127094075","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}
Pub Date : 2021-09-01DOI: 10.1109/services51467.2021.00028
Maithilee P. Joshi, K. Joshi, Timothy W. Finin
Medical organizations find it challenging to adopt cloud-based Electronic Health Records (EHR) services due to the risk of data breaches and the resulting compromise of patient data. Existing authorization models follow a patient-centric approach for EHR management, where the responsibility of authorizing data access is handled at the patients’ end. This creates significant overhead for the patient, who must authorize every access of their health record. It is also not practical given that multiple personnel are typically involved in providing care and that the patient may not always be in a state to provide this authorization.
{"title":"Delegated Authorization Framework for EHR Services using Attribute Based Encryption","authors":"Maithilee P. Joshi, K. Joshi, Timothy W. Finin","doi":"10.1109/services51467.2021.00028","DOIUrl":"https://doi.org/10.1109/services51467.2021.00028","url":null,"abstract":"Medical organizations find it challenging to adopt cloud-based Electronic Health Records (EHR) services due to the risk of data breaches and the resulting compromise of patient data. Existing authorization models follow a patient-centric approach for EHR management, where the responsibility of authorizing data access is handled at the patients’ end. This creates significant overhead for the patient, who must authorize every access of their health record. It is also not practical given that multiple personnel are typically involved in providing care and that the patient may not always be in a state to provide this authorization.","PeriodicalId":210534,"journal":{"name":"2021 IEEE World Congress on Services (SERVICES)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127580385","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}
Pub Date : 2021-09-01DOI: 10.1109/services51467.2021.00033
Zaiwen Feng, W. Mayer, M. Stumptner, G. Grossmann, Selasi Kwashie, Da Ning, K. He
Traditionally the integration of data from multiple sources is done on an ad-hoc basis for each analysis scenario and application. This is an approach that is inflexible, incurs high costs, and leads to “silos” that prevent sharing data across different agencies or tasks. A standard approach to tackling this problem is to design a common ontology and to construct source descriptions which specify mappings between the sources and the ontology. Modeling the semantics of data manually requires huge human cost and expertise, making an automatic method of semantic modeling desired. Automatic semantic model has been gaining attention in data integration [5], federated data query [14] and knowledge graph construction [6]. This paper proposes an service-oriented architecture to create a correct semantic model, including annotating training data, training the machine learning model, and predict an accurate semantic model for new data source. Moreover, a holistic process for automatic semantic modeling is presented. By the usage of ASMaaS, historical semantic annotations for training machine learning model used in automatic semantic modeling can be shared, reducing costs of human resources from users. By specifying a well defined interface, users are able to have access to automatic semantic modeling process at any time, from anywhere. In addition, users must not be concerned with machine learning technologies and pipeline used in automatic semantic modeling, focusing mainly on the business itself.
{"title":"ASMaaS: Automatic Semantic Modeling as a Service","authors":"Zaiwen Feng, W. Mayer, M. Stumptner, G. Grossmann, Selasi Kwashie, Da Ning, K. He","doi":"10.1109/services51467.2021.00033","DOIUrl":"https://doi.org/10.1109/services51467.2021.00033","url":null,"abstract":"Traditionally the integration of data from multiple sources is done on an ad-hoc basis for each analysis scenario and application. This is an approach that is inflexible, incurs high costs, and leads to “silos” that prevent sharing data across different agencies or tasks. A standard approach to tackling this problem is to design a common ontology and to construct source descriptions which specify mappings between the sources and the ontology. Modeling the semantics of data manually requires huge human cost and expertise, making an automatic method of semantic modeling desired. Automatic semantic model has been gaining attention in data integration [5], federated data query [14] and knowledge graph construction [6]. This paper proposes an service-oriented architecture to create a correct semantic model, including annotating training data, training the machine learning model, and predict an accurate semantic model for new data source. Moreover, a holistic process for automatic semantic modeling is presented. By the usage of ASMaaS, historical semantic annotations for training machine learning model used in automatic semantic modeling can be shared, reducing costs of human resources from users. By specifying a well defined interface, users are able to have access to automatic semantic modeling process at any time, from anywhere. In addition, users must not be concerned with machine learning technologies and pipeline used in automatic semantic modeling, focusing mainly on the business itself.","PeriodicalId":210534,"journal":{"name":"2021 IEEE World Congress on Services (SERVICES)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129070495","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}
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