2022 IEEE World Congress on Services (SERVICES)最新文献

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FOCloud: Feature Model Guided Performance Prediction and Explanation for Deployment Configurable Cloud Applications FOCloud:基于特征模型的可配置云应用部署性能预测与解释
2022 IEEE World Congress on Services (SERVICES) Pub Date : 2022-07-01 DOI: 10.1109/SERVICES55459.2022.00032
I. Kumara, Mohamed Hameez Ariz, Mohan Baruwal Chhetri, Majid Mohammadi, Willem-Jan van den Heuvel, D. Tamburri
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引用次数: 7
Latency-driven Model Placement for Efficient Edge Intelligence Service 基于延迟驱动的高效边缘智能服务模型放置
2022 IEEE World Congress on Services (SERVICES) Pub Date : 2022-07-01 DOI: 10.1109/SERVICES55459.2022.00028
P. Lin, Zhichen Shi, Zheng Xiao, Cen Chen, KenLi Li
{"title":"Latency-driven Model Placement for Efficient Edge Intelligence Service","authors":"P. Lin, Zhichen Shi, Zheng Xiao, Cen Chen, KenLi Li","doi":"10.1109/SERVICES55459.2022.00028","DOIUrl":"https://doi.org/10.1109/SERVICES55459.2022.00028","url":null,"abstract":"Deep learning services are extensively required and have powerful expected effects in a wide range of applications, such as auto-self driving, voice assistant and so on.Traditionally, deep learning services are mainly provided based on cloud computing, referred to as cloud intelligence services, where the deep learning model is deployed in the cloud, and endusers need to upload data through wireless and core network when requesting training and inference services. However, the cloud computing-based deep learning services have deficiencies in latency, privacy, etc. For example, the deep learning service users do not want their private data to leak, and the privacy problem is difficult to solve when the data is uploading to the cloud. With the widespread use of the Internet of Things (IoT) and the rapid development of mobile devices such as smartphones and IoT sensors, large amounts of data need to be used for a variety of real-time deep learning services, such as target recognition and voice recognition for smart cities, smart medical care, and the Internet of Vehicles (IoVs). With a certain network bandwidth, a large amount of data uploaded to the cloud will cause network congestion and greatly increase the response time. To meet the requirements of low latency, researchers have begun to consider the deployment of deep learning services in edges, i.e., edge intelligence service.In edge intelligence services, the computation capability and memory of processors (or devices) are different from a large. At the same time, the requirement of memory size of deep neural network (DNN) models is increasing, such as the memory usage for Alexnet and Resnet are 2.12G and 16.20G separately. Also, in DNN model design, the branches are becoming common, which brings the parallelism. Deploying deep learning models on multiple processors can support the large-scale DNN models and the parallel implementation of DNN model, where the computation of a deep learning model can be conducted in parallel is a possible solution to improve the efficiency of edge intelligence services. The key point in edge intelligence services is how to partition and assign the implementation of the DNN model.In this paper, we propose a novel latency-driven deep learning model placement method for efficient edge intelligence service. Model placement contains two procedures: model partition and sub-models assignment. In our method, we first convert a DNN model into an execution graph, which is a directed acyclic graph (DAG), and propose a novel latency-driven multilevel graph partition for the model. Then the partitioned submodels are heuristically assigned to available processors. To the best of our knowledge, it is the first work that proposes latency-driven graph partition algorithms for model placement. Extensive experiments on several commonly used DNN models and synthetic datasets show that our method can achieve the lowest execution latency with low complexity compared with other state","PeriodicalId":429807,"journal":{"name":"2022 IEEE World Congress on Services (SERVICES)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121464968","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
Message from the TCSVC Chair 来自TCSVC主席的信息
2022 IEEE World Congress on Services (SERVICES) Pub Date : 2022-07-01 DOI: 10.1109/services55459.2022.00010
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引用次数: 0
Message from the J1C2 Track Chairs 来自J1C2轨道椅的信息
2022 IEEE World Congress on Services (SERVICES) Pub Date : 2022-07-01 DOI: 10.1109/services55459.2022.00011
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引用次数: 0
Message from the Steering Committee Chair-Elect 指导委员会候任主席的话
2022 IEEE World Congress on Services (SERVICES) Pub Date : 2022-07-01 DOI: 10.1109/services55459.2022.00006
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引用次数: 0
Integrated Exploration of Data-Intensive Business Processes [Extended Abstract] 数据密集型业务流程的集成探索[扩展摘要]
2022 IEEE World Congress on Services (SERVICES) Pub Date : 2022-07-01 DOI: 10.1109/SERVICES55459.2022.00038
Combi Carlo, Barbara Oliboni, Francesca Zerbato
{"title":"Integrated Exploration of Data-Intensive Business Processes [Extended Abstract]","authors":"Combi Carlo, Barbara Oliboni, Francesca Zerbato","doi":"10.1109/SERVICES55459.2022.00038","DOIUrl":"https://doi.org/10.1109/SERVICES55459.2022.00038","url":null,"abstract":"This is an extended abstract of our article 1 accepted for publication in the IEEE Transactions on Services Computing Journal proposing an approach for the conceptual modeling and design-time analysis of the data operations performed by a business process on a database. Our approach leverages a uniform formal representation that captures a process model, a conceptual database schema, and the data operations performed by the process on the database. We then show how such a formal model can be queried to obtain insights into the informational perspective of business process models and, in turn, support process modelers and analysts in reasoning about process (re) design and improvement. The approach is evaluated with a controlled experiment and a proof-of-concept implementation.1This extended abstract summarizes the approach and selected findings of the work referenced as [1] with doi:10.1109/TSC.2021.3134485","PeriodicalId":429807,"journal":{"name":"2022 IEEE World Congress on Services (SERVICES)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127258085","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
Keynote 2 - Dejan Milojicic - Technology Predictions 主题演讲2 - Dejan Milojicic -技术预测
2022 IEEE World Congress on Services (SERVICES) Pub Date : 2022-07-01 DOI: 10.1109/services55459.2022.00015
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引用次数: 0
A Novel Graph-based Computation Offloading Strategy for Workflow Applications in Mobile Edge Computing 一种新的基于图的移动边缘计算工作流卸载策略
2022 IEEE World Congress on Services (SERVICES) Pub Date : 2022-07-01 DOI: 10.1109/SERVICES55459.2022.00029
Xuejun Li, Tianxiang Chen, Dong Yuan, Jia Xu, Xiao Liu
{"title":"A Novel Graph-based Computation Offloading Strategy for Workflow Applications in Mobile Edge Computing","authors":"Xuejun Li, Tianxiang Chen, Dong Yuan, Jia Xu, Xiao Liu","doi":"10.1109/SERVICES55459.2022.00029","DOIUrl":"https://doi.org/10.1109/SERVICES55459.2022.00029","url":null,"abstract":"[J1C2 Presentation Abstract at IEEE SERVICES 2022 for IEEE Transactions on Services Computing DOI 10.1109/TSC.2022.3180067]","PeriodicalId":429807,"journal":{"name":"2022 IEEE World Congress on Services (SERVICES)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130581259","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
Social-Sensor Composition for Tapestry Scenes 挂毯场景的社会传感器组成
2022 IEEE World Congress on Services (SERVICES) Pub Date : 2022-07-01 DOI: 10.1109/SERVICES55459.2022.00035
Tooba Aamir, Hai Dong, A. Bouguettaya
{"title":"Social-Sensor Composition for Tapestry Scenes","authors":"Tooba Aamir, Hai Dong, A. Bouguettaya","doi":"10.1109/SERVICES55459.2022.00035","DOIUrl":"https://doi.org/10.1109/SERVICES55459.2022.00035","url":null,"abstract":"[J1C2 Presentation Abstract at IEEE SERVICES 2022 for IEEE Transactions on Services Computing DOI 10.1109/TSC.2020.2974741]","PeriodicalId":429807,"journal":{"name":"2022 IEEE World Congress on Services (SERVICES)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129978913","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
Keynote 1 - Allesandro Curioni - What's Next in Computing: The Era of Accelerated Discovery 主题演讲1 - Allesandro Curioni -计算的下一步:加速发现的时代
2022 IEEE World Congress on Services (SERVICES) Pub Date : 2022-07-01 DOI: 10.1109/services55459.2022.00014
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引用次数: 0
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