Pub Date : 2022-07-01DOI: 10.1109/SERVICES55459.2022.00031
Huiying Jin, Pengcheng Zhang, Hai Dong, Yuelong Zhu, A. Bouguettaya
We propose a novel privacy-aware Quality of Service (QoS) forecasting approach in the mobile edge environment – Edge-PMAM (Edge QoS forecasting with Public Model and Attention Mechanism). Edge-PMAM can make realtime, accurate and personalized QoS forecasting on the premise of user privacy preservation. Edge-PMAM comprises a public model for privacy-aware QoS forecasting in an edge region and a private model for personalized QoS forecasting for an individual user. An attention mechanism atop Long Short-Term Memory and an automated edge region division solution are devised to enhance the prediction accuracy of the public and private models. We conduct a series of experiments based on public and self-collected data sets. The results based on public and self-collected data sets demonstrate that our approach can effectively improve forecasting performance and protect user privacy.
{"title":"Privacy-Aware Forecasting of Quality of Service in Mobile Edge Computing","authors":"Huiying Jin, Pengcheng Zhang, Hai Dong, Yuelong Zhu, A. Bouguettaya","doi":"10.1109/SERVICES55459.2022.00031","DOIUrl":"https://doi.org/10.1109/SERVICES55459.2022.00031","url":null,"abstract":"We propose a novel privacy-aware Quality of Service (QoS) forecasting approach in the mobile edge environment – Edge-PMAM (Edge QoS forecasting with Public Model and Attention Mechanism). Edge-PMAM can make realtime, accurate and personalized QoS forecasting on the premise of user privacy preservation. Edge-PMAM comprises a public model for privacy-aware QoS forecasting in an edge region and a private model for personalized QoS forecasting for an individual user. An attention mechanism atop Long Short-Term Memory and an automated edge region division solution are devised to enhance the prediction accuracy of the public and private models. We conduct a series of experiments based on public and self-collected data sets. The results based on public and self-collected data sets demonstrate that our approach can effectively improve forecasting performance and protect user privacy.","PeriodicalId":429807,"journal":{"name":"2022 IEEE World Congress on Services (SERVICES)","volume":"23 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":"123411497","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 : 2022-07-01DOI: 10.1109/SERVICES55459.2022.00023
Carl K. Chang, Zhongjie Wang
Industry observers with keen eyes have found that services computing and software engineering are becoming more and more intertwined. That is, services computing professionals and software engineers have been finding ways to intersect theories and practices that used to be more of a concern for the other camp. This emerging interdisciplinary field of study is hereby termed Software Services Engineering (SSE) [1].
{"title":"Software Services Engineering Manifesto – Revisited","authors":"Carl K. Chang, Zhongjie Wang","doi":"10.1109/SERVICES55459.2022.00023","DOIUrl":"https://doi.org/10.1109/SERVICES55459.2022.00023","url":null,"abstract":"Industry observers with keen eyes have found that services computing and software engineering are becoming more and more intertwined. That is, services computing professionals and software engineers have been finding ways to intersect theories and practices that used to be more of a concern for the other camp. This emerging interdisciplinary field of study is hereby termed Software Services Engineering (SSE) [1].","PeriodicalId":429807,"journal":{"name":"2022 IEEE World Congress on Services (SERVICES)","volume":"9 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":"132007145","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 : 2022-07-01DOI: 10.1109/SERVICES55459.2022.00025
M. Anisetti, Filippo Berto, M. Banzi
Nowadays there is an increasing trend in the volume and velocity of data, typically consumed by data-intensive AI/ML-based services, requiring a larger diffusion of more effective Edge computing approaches. In addition, we are experiencing an increment of critical applications using an increasing volume of sensitive data and requiring advanced security and privacy protections. 5G Edge technology can foster a more diffused Edge computing adoption but several challenges in terms of interoperability. Handling data-intensive pipelines on the 5Genabled Edge continuum, considering specific QoS requirements including security and privacy, is still in its infancy. In this paper, we propose an initial solution for deploying a data-intensive pipeline in a 5G-enabled Edge continuum satisfying specific QoS requirements. Our approach is based on a QoS-aware meta orchestration modeling of a given pipeline and an orchestration builder generating deployable Edge-specific orchestrations. In this paper, we also present an initial walkthrough scenario in the context of a wet lab analysis pipeline to be deployed on the 5G-enabled Edge continuum.
{"title":"Orchestration of data-intensive pipeline in 5G-enabled Edge Continuum","authors":"M. Anisetti, Filippo Berto, M. Banzi","doi":"10.1109/SERVICES55459.2022.00025","DOIUrl":"https://doi.org/10.1109/SERVICES55459.2022.00025","url":null,"abstract":"Nowadays there is an increasing trend in the volume and velocity of data, typically consumed by data-intensive AI/ML-based services, requiring a larger diffusion of more effective Edge computing approaches. In addition, we are experiencing an increment of critical applications using an increasing volume of sensitive data and requiring advanced security and privacy protections. 5G Edge technology can foster a more diffused Edge computing adoption but several challenges in terms of interoperability. Handling data-intensive pipelines on the 5Genabled Edge continuum, considering specific QoS requirements including security and privacy, is still in its infancy. In this paper, we propose an initial solution for deploying a data-intensive pipeline in a 5G-enabled Edge continuum satisfying specific QoS requirements. Our approach is based on a QoS-aware meta orchestration modeling of a given pipeline and an orchestration builder generating deployable Edge-specific orchestrations. In this paper, we also present an initial walkthrough scenario in the context of a wet lab analysis pipeline to be deployed on the 5G-enabled Edge continuum.","PeriodicalId":429807,"journal":{"name":"2022 IEEE World Congress on Services (SERVICES)","volume":"159 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":"115195921","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 : 2022-07-01DOI: 10.1109/SERVICES55459.2022.00045
S. Ristov, Stefan Pedratscher, T. Fahringer
[J1C2 Presentation Abstract at IEEE SERVICES 2021 for IEEE Transactions on Services Computing DOI 10.1109/TSC.2021.3128137]
[J1C2在IEEE SERVICES 2021 for IEEE Transactions on SERVICES Computing DOI 10.1109/TSC.2021.3128137]
{"title":"xAFCL: Run Scalable Function Choreographies Across Multiple FaaS Systems","authors":"S. Ristov, Stefan Pedratscher, T. Fahringer","doi":"10.1109/SERVICES55459.2022.00045","DOIUrl":"https://doi.org/10.1109/SERVICES55459.2022.00045","url":null,"abstract":"[J1C2 Presentation Abstract at IEEE SERVICES 2021 for IEEE Transactions on Services Computing DOI 10.1109/TSC.2021.3128137]","PeriodicalId":429807,"journal":{"name":"2022 IEEE World Congress on Services (SERVICES)","volume":"13 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":"115467077","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 : 2022-07-01DOI: 10.1109/services55459.2022.00021
{"title":"Plenary Panel 4 - The Role of Services in the 5G/6G Arena","authors":"","doi":"10.1109/services55459.2022.00021","DOIUrl":"https://doi.org/10.1109/services55459.2022.00021","url":null,"abstract":"","PeriodicalId":429807,"journal":{"name":"2022 IEEE World Congress on Services (SERVICES)","volume":"124 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":"115884625","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}
[J1C2 Presentation Abstract at IEEE SERVICES 2022 for IEEE Transactions on Services Computing DOI 10.1109/TSC.2022.3142265]
[J1C2 IEEE SERVICES 2022 for IEEE Transactions on SERVICES Computing DOI 10.1109/TSC.2022.3142265]
{"title":"DisCOV: Distributed COVID-19 Detection on X-Ray Images with Edge-Cloud Collaboration","authors":"Xiaolong Xu, Hao Tian, Xuyun Zhang, Lianyong Qi, Qiang He, Wanchun Dou","doi":"10.1109/SERVICES55459.2022.00036","DOIUrl":"https://doi.org/10.1109/SERVICES55459.2022.00036","url":null,"abstract":"[J1C2 Presentation Abstract at IEEE SERVICES 2022 for IEEE Transactions on Services Computing DOI 10.1109/TSC.2022.3142265]","PeriodicalId":429807,"journal":{"name":"2022 IEEE World Congress on Services (SERVICES)","volume":"34 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":"127265600","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 : 2022-07-01DOI: 10.1109/services55459.2022.00022
{"title":"Plenary Panel 5 - Software Services Engineering","authors":"","doi":"10.1109/services55459.2022.00022","DOIUrl":"https://doi.org/10.1109/services55459.2022.00022","url":null,"abstract":"","PeriodicalId":429807,"journal":{"name":"2022 IEEE World Congress on Services (SERVICES)","volume":"61 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":"115841211","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 : 2022-07-01DOI: 10.1109/SERVICES55459.2022.00027
Lei Xu, Xingliang Yuan, Zhengxiang Zhou, Cong Wang, Chungen Xu
[J1C2 Presentation Abstract at IEEE SERVICES 2021 for IEEE Transactions on Services Computing DOI 10.1109/TSC.2021.3111208]
[J1C2 IEEE SERVICES 2021 for IEEE Transactions on SERVICES Computing DOI 10.1109/TSC.2021.3111208]
{"title":"Towards Efficient Cryptographic Data Validation Service in Edge Computing","authors":"Lei Xu, Xingliang Yuan, Zhengxiang Zhou, Cong Wang, Chungen Xu","doi":"10.1109/SERVICES55459.2022.00027","DOIUrl":"https://doi.org/10.1109/SERVICES55459.2022.00027","url":null,"abstract":"[J1C2 Presentation Abstract at IEEE SERVICES 2021 for IEEE Transactions on Services Computing DOI 10.1109/TSC.2021.3111208]","PeriodicalId":429807,"journal":{"name":"2022 IEEE World Congress on Services (SERVICES)","volume":"21 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":"125550544","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 : 2022-07-01DOI: 10.1109/SERVICES55459.2022.00030
Phu Lai, Qiang He, Xiaoyu Xia, Feifei Chen, Mohamed Abdelrazek, J. Grundy, J. Hosking, Yun Yang
Mobile edge computing (MEC) is a new distributed computing paradigm where edge servers are deployed at, or near cellular base stations in close proximity to end-users. This offers computing resources at the edge of the network, facilitating a highly accessible platform for real-time, latency-sensitive services. A typical MEC environment is highly stochastic with random user arrivals and departures over time. Here, we address the user allocation problem from a service provider’s perspective, who needs to allocate its users to the cloud or edge servers in a specific area. A user, who has a multi-dimensional resource requirement, can be allocated to either the remote cloud, which incurs a high latency, or an edge server, which results in a low latency but might require the user to wait in a queue. This study aims to achieve a controllable trade-off between performance (throughput) and several associated costs such as queuing delay and latency costs. We model this problem as a stochastic optimization problem, propose SUAC (Stochastic User AlloCation) – an online Lyapunov optimization-based algorithm, and prove its performance bounds. The experimental results demonstrate that SUAC outperforms existing approaches, effectively allocating users with a desired trade-off while keeping the system strongly stable.
移动边缘计算(MEC)是一种新的分布式计算范式，其中边缘服务器部署在靠近最终用户的蜂窝基站或附近。这在网络边缘提供了计算资源，为实时、延迟敏感的服务提供了一个高度可访问的平台。典型的MEC环境是高度随机的，随着时间的推移，用户的到达和离开是随机的。这里，我们从服务提供商的角度解决用户分配问题，服务提供商需要将其用户分配到特定区域的云或边缘服务器。具有多维资源需求的用户可以分配给远程云，这会导致高延迟，也可以分配给边缘服务器，这会导致低延迟，但可能需要用户在队列中等待。本研究旨在实现性能(吞吐量)和一些相关成本(如排队延迟和延迟成本)之间的可控权衡。我们将此问题建模为一个随机优化问题，提出了一种基于Lyapunov优化的SUAC (stochastic User AlloCation)算法，并证明了其性能界限。实验结果表明，该方法优于现有的方法，在保持系统强稳定性的同时，有效地分配用户。
{"title":"Dynamic User Allocation in Stochastic Mobile Edge Computing Systems*","authors":"Phu Lai, Qiang He, Xiaoyu Xia, Feifei Chen, Mohamed Abdelrazek, J. Grundy, J. Hosking, Yun Yang","doi":"10.1109/SERVICES55459.2022.00030","DOIUrl":"https://doi.org/10.1109/SERVICES55459.2022.00030","url":null,"abstract":"Mobile edge computing (MEC) is a new distributed computing paradigm where edge servers are deployed at, or near cellular base stations in close proximity to end-users. This offers computing resources at the edge of the network, facilitating a highly accessible platform for real-time, latency-sensitive services. A typical MEC environment is highly stochastic with random user arrivals and departures over time. Here, we address the user allocation problem from a service provider’s perspective, who needs to allocate its users to the cloud or edge servers in a specific area. A user, who has a multi-dimensional resource requirement, can be allocated to either the remote cloud, which incurs a high latency, or an edge server, which results in a low latency but might require the user to wait in a queue. This study aims to achieve a controllable trade-off between performance (throughput) and several associated costs such as queuing delay and latency costs. We model this problem as a stochastic optimization problem, propose SUAC (Stochastic User AlloCation) – an online Lyapunov optimization-based algorithm, and prove its performance bounds. The experimental results demonstrate that SUAC outperforms existing approaches, effectively allocating users with a desired trade-off while keeping the system strongly stable.","PeriodicalId":429807,"journal":{"name":"2022 IEEE World Congress on Services (SERVICES)","volume":"18 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":"132703461","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}
{"title":"An Extended Abstract of “Dynamic Random Testing of Web Services: A Methodology and Evaluation”","authors":"Chang-ai Sun, Hepeng Dai, Guan Wang, D. Towey, T. Chen, K. Cai","doi":"10.1109/SERVICES55459.2022.00043","DOIUrl":"https://doi.org/10.1109/SERVICES55459.2022.00043","url":null,"abstract":"[J1C2 Presentation Abstract at IEEE SERVICES 2022 for IEEE Transactions on Services Computing, 2022, 15(2):736-751. DOI: 10.1109/TSC.2019.2960496].","PeriodicalId":429807,"journal":{"name":"2022 IEEE World Congress on Services (SERVICES)","volume":"9 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":"123930302","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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