2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC)最新文献_第3页

Performance Evaluation of WLAN Access Points Selection Metrics for Fingerprinting based Localization 基于指纹定位的无线局域网接入点选择指标的性能评估

2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC) Pub Date : 2022-12-01 DOI: 10.1109/UCC56403.2022.00070

Sohaib Bin Altaf Khattak, Moustafa M. Nasralla, M. Esmail, M. Marey, Nikumani Choudhury

{"title":"Performance Evaluation of WLAN Access Points Selection Metrics for Fingerprinting based Localization","authors":"Sohaib Bin Altaf Khattak, Moustafa M. Nasralla, M. Esmail, M. Marey, Nikumani Choudhury","doi":"10.1109/UCC56403.2022.00070","DOIUrl":"https://doi.org/10.1109/UCC56403.2022.00070","url":null,"abstract":"abstract Reliability and cost are essential elements to consider in all engineering network designs. In RF-localization systems, reliability can be defined as seamless coverage with precise and accurate localization. Cost-efficiency aims to reduce the infrastructure while simultaneously maintaining high accuracy. Both the cost and reliability can be associated with Access Points (APs) deployment. Therefore, it is paramount to study how to optimize the APs placement in RF-localization systems. To select the optimal AP configuration, different selection metrics are proposed. This paper investigates different AP placement and optimization strategies for WLAN fingerprinting indoor localization systems. Performance of selection metrics are evaluated experimentally in a realistic indoor environment. A fingerprinting database is developed by a grid spacing of 2m using 7 WLAN APs, by collecting Received Signal Strength (RSS) values using an Android smartphone app. The experimental results show, the AP configuration obtained by the metric combining the fingerprint difference metric with the geometric dilution of precision, results in high localization accuracy.","PeriodicalId":203244,"journal":{"name":"2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123302818","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

Scaling Data Analysis Services in an Edge-based Federated Learning Environment 在基于边缘的联邦学习环境中扩展数据分析服务

2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC) Pub Date : 2022-12-01 DOI: 10.1109/UCC56403.2022.00030

Alessio Catalfamo, Lorenzo Carnevale, A. Galletta, Francesco Martella, A. Celesti, M. Fazio, M. Villari

引用次数: 1

Message from the BlockCPS Workshop Chairs 来自BlockCPS工作坊主席的消息

2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC) Pub Date : 2022-12-01 DOI: 10.1109/ucc56403.2022.00075

引用次数: 0

Cloud Auto-scaling Auditing Approach using Blockchain 使用区块链的云自动缩放审计方法

2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC) Pub Date : 2022-12-01 DOI: 10.1109/UCC56403.2022.00068

Ahmad Alsharidah, M. Barati, Giacomo Bergami, R. Ranjan

引用次数: 0

Mobile-Kube: Mobility-aware and Energy-efficient Service Orchestration on Kubernetes Edge Servers Mobile-Kube: Kubernetes边缘服务器上的移动感知和节能服务编排

2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC) Pub Date : 2022-12-01 DOI: 10.1109/UCC56403.2022.00019

Saeid Ghafouri, Alireza Karami, D. B. Bakhtiarvand, Ali Akbar Saleh-Bigdeli, S. S. Gill, Joseph Doyle

{"title":"Mobile-Kube: Mobility-aware and Energy-efficient Service Orchestration on Kubernetes Edge Servers","authors":"Saeid Ghafouri, Alireza Karami, D. B. Bakhtiarvand, Ali Akbar Saleh-Bigdeli, S. S. Gill, Joseph Doyle","doi":"10.1109/UCC56403.2022.00019","DOIUrl":"https://doi.org/10.1109/UCC56403.2022.00019","url":null,"abstract":"In recent years Kubernetes has become the de facto standard in the realm of service orchestration. Despite its great benefits, there are still numerous challenges to make it compatible with decentralised cloud computing platforms. One of the challenges of mobile edge computing is that the location of the users is changing over time. This mobility will constantly alter the proximity of the users to their connected services. One solution to this problem is to regularly move services to computing nodes near the users. However, distributing the services in edge nodes only subject to user movements will result in the fragmentation of active nodes. This leads to having active nodes that do not use their full capacity. We have proposed a method called MobileKube to reduce the latency of Kubernetes applications on mobile edge computing devices while maintaining energy consumption at a reasonable level. An experimental framework is designed on top of real-world Kubernetes clusters and real-world traces of mobile users’ movements have been used to simulate the users’ mobility. Experimental results show that Mobile-Kube can achieve similar energy consumption performance to a heuristic approach that focuses on reducing energy consumption only while reducing the latency of services by 43%.","PeriodicalId":203244,"journal":{"name":"2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130646995","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: 6

Applying Federated Learning in the detection of Freezing of Gait in Parkinson’s disease 应用联邦学习检测帕金森病步态冻结

2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC) Pub Date : 2022-12-01 DOI: 10.1109/UCC56403.2022.00037

J. Jorge, P. H. Barros, R. S. Yokoyama, D. Guidoni, Heitor S. Ramos, Nelson Luis Saldanha da Fonseca, L. Villas

引用次数: 2

NSDF-Catalog: Lightweight Indexing Service for Democratizing Data Delivery 民主化数据传递的轻量级索引服务

2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC) Pub Date : 2022-12-01 DOI: 10.1109/UCC56403.2022.00011

Jakob Luettgau, Christine R. Kirkpatrick, G. Scorzelli, Valerio Pascucci, G. Tarcea

{"title":"NSDF-Catalog: Lightweight Indexing Service for Democratizing Data Delivery","authors":"Jakob Luettgau, Christine R. Kirkpatrick, G. Scorzelli, Valerio Pascucci, G. Tarcea","doi":"10.1109/UCC56403.2022.00011","DOIUrl":"https://doi.org/10.1109/UCC56403.2022.00011","url":null,"abstract":"Across domains massive amounts of scientific data are generated. Because of the large volume of information, data discoverability is a challenge, especially for scientists who have not generated the data or are from other domains. As part of the NSF-funded National Science Data Fabric (NSDF) initiative, we developed a testbed to demonstrate that these boundaries to data discoverability can be overcome. In support of this effort, we identify the need for indexing large-amounts of scientific data across scientific domains. We propose NSDF-Catalog, a lightweight indexing service with minimal metadata that complements existing domain-specific and rich-metadata col-lections. NSDF-Catalog is designed to facilitate multiple related objectives within a flexible microservice to: (i) coordinate data movements and replication of data from origin repositories within the NSDF federation; (ii) build an inventory of existing scientific data to inform the design of next-generation cyberinfrastructure; and (iii) provide a suite of tools for discovery of datasets for cross-disciplinary research. Our service indexes scientific data at a fine-granularity at the file or object level to inform data distribution strategies and to improve the experience for users from the consumer perspective, with the goal of allowing end-to-end dataflow optimizations.","PeriodicalId":203244,"journal":{"name":"2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130246554","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

Message from the Intel4EC Workshop Chairs 来自Intel4EC研讨会主席的消息

2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC) Pub Date : 2022-12-01 DOI: 10.1109/ucc56403.2022.00078

引用次数: 0

VECFlex: Reconfigurability and Scalability for Trustworthy Volunteer Edge-Cloud supporting Data-intensive Scientific Computing VECFlex:可重构性和可扩展性，为可信赖的志愿者边缘云支持数据密集型科学计算

2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC) Pub Date : 2022-12-01 DOI: 10.1109/UCC56403.2022.00027

Mauro Lemus Alarcon, Minh Nguyen, Ashish Pandey, S. Debroy, P. Calyam

{"title":"VECFlex: Reconfigurability and Scalability for Trustworthy Volunteer Edge-Cloud supporting Data-intensive Scientific Computing","authors":"Mauro Lemus Alarcon, Minh Nguyen, Ashish Pandey, S. Debroy, P. Calyam","doi":"10.1109/UCC56403.2022.00027","DOIUrl":"https://doi.org/10.1109/UCC56403.2022.00027","url":null,"abstract":"Although ‘‘volunteer edge-cloud’’ (VEC) computing has emerged as a new paradigm for scientific computing in recent times, wider adoption is being hindered due to the abundance of heterogeneous volunteer resources and lack of trust in their ability to satisfy workflows’ performance and security requirements. In this paper, we propose the VECFlex, a flexible resource management framework that can reconFigure resource security policies to meet workflow requirements and efficiently allocate resources from a large pool with disparate configurations. It employs Reinforcement Learning (RL)-driven resource behavioral modeling to rank the trustworthiness of resources in terms of their availability, flexibility of applying security policies, and consistency of task execution times. VECFlex also applies a security-based modified Particle Swarm optimization (PSO) scheduler to allocate optimal resources to workflow tasks from a large pool of disparate resources. We evaluate the performance of VECFlex using an AWS testbed that demonstrates VECFlex’s ability to fully satisfy workflows’ security requirements and $sim$2x improvement in workflow execution latency.","PeriodicalId":203244,"journal":{"name":"2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129190625","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

Computation Offloading From Edge to Equipment for Smart Manufacturing 智能制造中从边缘到设备的计算卸载

2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC) Pub Date : 2022-12-01 DOI: 10.1109/UCC56403.2022.00039

H. H. Nguyen, Yi Zhou, K. Kushagra, Xiao Qin

{"title":"Computation Offloading From Edge to Equipment for Smart Manufacturing","authors":"H. H. Nguyen, Yi Zhou, K. Kushagra, Xiao Qin","doi":"10.1109/UCC56403.2022.00039","DOIUrl":"https://doi.org/10.1109/UCC56403.2022.00039","url":null,"abstract":"In smart manufacturing, data management systems are built with a multi-layer architecture, in which the most significant layers are the edge and the cloud. The edge layer renders support to data analysis that genuinely demands low latency. Cloud platforms store vast amounts of data while performing extensive computations such as machine learning and big data analysis. This type of data management system has a limitation rooted in the fact that all data needs to be transferred from the equipment layer to the edge layer in order to perform all data analyses. Even worse, data transferring adds delays to computation processes in smart manufacturing. We investigate an offloading strategy to shift a selection of computation tasks towards the equipment layer. Our computation offloading mechanism opts for smart manufacturing tasks that are not only light weight but also have no need to save data at the edge/cloud end. In our empirical study, we demonstrate that the edge layer can judiciously offload computing tasks to the equipment layer, which curtails computing latency and slashes the amount of transferred data during smart manufacturing process. Our experimental results confirm that our offloading strategy offers the capability for data analysis computing in real-time at the equipment level- an array of smart devices is slated to speed up the data analysis process in semiconductor manufacturing.","PeriodicalId":203244,"journal":{"name":"2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129194512","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