2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC)最新文献_第5页

Kubitect - a Solution for On-premise Cluster Deployment Kubitect——内部部署集群的解决方案

2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC) Pub Date : 2022-12-01 DOI: 10.1109/UCC56403.2022.00049

Din Music, C. Fortuna

引用次数: 0

Message from the CloudAM Workshop Chairs 来自CloudAM工作坊主席的信息

2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC) Pub Date : 2022-12-01 DOI: 10.1109/ucc56403.2022.00076

引用次数: 0

Understanding Microquanta Process Scheduling for Cloud Applications 理解云应用的微量子进程调度

2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC) Pub Date : 2022-12-01 DOI: 10.1109/UCC56403.2022.00050

Erfan Sharafzadeh, Alireza Sanaee, Peng Huang, G. Antichi, S. Ghorbani

引用次数: 0

Use of multilevel resource clustering for service placement in fog computing environments 在雾计算环境中使用多级资源集群进行服务放置

2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC) Pub Date : 2022-12-01 DOI: 10.1109/UCC56403.2022.00063

Helberth Borelli, F. Costa, Sérgio T. Carvalho

{"title":"Use of multilevel resource clustering for service placement in fog computing environments","authors":"Helberth Borelli, F. Costa, Sérgio T. Carvalho","doi":"10.1109/UCC56403.2022.00063","DOIUrl":"https://doi.org/10.1109/UCC56403.2022.00063","url":null,"abstract":"The fog is part of the infrastructure that makes up the Internet and can be considered an extension of the cloud. It is a layer between the cloud and the edge, and its topology comprises mostly heterogeneous and resource-limited computing nodes. Because of its proximity to edge devices, the fog is often mentioned as a solution to deploy services with stringent latency requirements. Moreover, the characteristics of the fog are appropriate for the deployment of services with lower computational demands, meaning that lean service models, such as microservices, are an appropriate match. Regarding the placement of services in the fog, an effective technique refers to the use of resource clustering, which organizes resources according to their properties. A common approach has been the use of the geographical location of resources to build clusters that favor the placement of services closer to clients. In this paper, we refine this approach by proposing multilevel clustering, adding to geolocation-based clustering the use of feature-based resource clustering. Thus, besides increasing proximity, we can also improve the matching between service requirements and the characteristics of resources, further improving the performance of applications. We evaluate the approach using iFogSim 2, and the results show performance improvements in excess of 20% in both application flow time and service placement time when compared to pure geolocation-based clustering.","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":"114771704","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

An Ensemble Neural Model for Classification of LADA Diabetes Case, Control and Variable Importance LADA糖尿病病例分类、控制和变量重要性的集成神经模型

2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC) Pub Date : 2022-12-01 DOI: 10.1109/UCC56403.2022.00041

A. Miller, John Panneerselvam, Lu Liu, N. Antonopoulos

{"title":"An Ensemble Neural Model for Classification of LADA Diabetes Case, Control and Variable Importance","authors":"A. Miller, John Panneerselvam, Lu Liu, N. Antonopoulos","doi":"10.1109/UCC56403.2022.00041","DOIUrl":"https://doi.org/10.1109/UCC56403.2022.00041","url":null,"abstract":"LADA Diabetes is a complex disease, but often dismissed as a potential individual disease within its own right. A comprehensive understanding of previously unknown aspects of LADA diabetes has the potential to not only ascertain a greater comprehension of LADA but also can assist the classification of Type 1 and Type 2 diabetes, as LADA characterises the attributes of both Type 1 and Type 2 diabetes. This paper proposes a novel heterogeneous ensemble model comprising of Neural network with Feature Extraction, Neural network alongside Multilayer Perceptron with Multiple Layers with the intention of classifying LADA diabetes along with weighting the importance of conventional variables including family history, age, gender, BMI, cholesterol level, and waist size in the classification. These conventional variables are analysed based on the aforementioned three-algorithm ensemble stack, and the entire architecture is tuned for optimal classification performance. The proposed novel ensemble stack delivers a reliable prediction accuracy in the identification of case, control, and variable importance. Performance evaluation of the proposed ensemble model based on statistics such as ROC/AUC curve, precision and recall demonstrated a higher predictive accuracy of 92.00%, sensitivity of 91.77%, and specificity of 92.23% alongside a precision of 92.23%, recall at 91.79% and an F1 score of 92.02%, ultimately outperforming well-known classical classification models. Further analysis has determined waist as an important and influential variable in the classification process, whereby a 100% association of LADA diabetes with waist is exhibited.","PeriodicalId":203244,"journal":{"name":"2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC)","volume":"360 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":"126695906","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

Arktos: A Hyperscale Cloud Infrastructure for Building Distributed Cloud Arktos:用于构建分布式云的超大规模云基础设施

2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC) Pub Date : 2022-12-01 DOI: 10.1109/UCC56403.2022.00022

Ying Huang, Yunwen Bai, FengE Li, Xiaoning Ding, Qian Chen, D. Vij, Peng Du, Ying Xiong

{"title":"Arktos: A Hyperscale Cloud Infrastructure for Building Distributed Cloud","authors":"Ying Huang, Yunwen Bai, FengE Li, Xiaoning Ding, Qian Chen, D. Vij, Peng Du, Ying Xiong","doi":"10.1109/UCC56403.2022.00022","DOIUrl":"https://doi.org/10.1109/UCC56403.2022.00022","url":null,"abstract":"Scalability and management cost in cloud computing are few of the top challenges for the cloud providers and large enterprises. In this paper, we present Arktos, a cloud infrastructure platform for managing large-scale compute clusters and running millions of application instances as containers and/or virtual machines (VM). Arktos is envisioned as a stepping-stone from current “ single-region” focused cloud infrastructure towards next generation distributed infrastructure in the public and/or private cloud environments. We present details related to the Arktos system architecture and features, important design decisions, and the results and analysis of the performance benchmark testing. Arktos achieves high scalability by partitioning its architecture into two independent components, the resource partition (RP) and the tenant workload partition (TP), with each component scaling independently. Our performance testing using a benchmark tool demonstrates that Arktos with just two RPs and two TPs system setting can already manage a cluster of 50K compute nodes and is able to run 1.5 million workload containers with 5 times system throughput (QPS)1 compared with an existing container management system. Three key characteristics differentiate Arktos from other open source cloud platforms such as OpenStack and Kubernetes. Firstly, Arktos architecture is a truly scalable architecture that supports a very large cluster by scaling to more RPs and TPs in the system, Secondly, it unifies the runtime infrastructure to run and manage both VM and container applications natively, therefore eliminating the cost of managing separate technology stacks for VMs and containers. Lastly, Arktos has a unique “ virtual cluster” style multi-tenancy design that provides both strong tenancy isolation, including network isolation and transparent resource view.","PeriodicalId":203244,"journal":{"name":"2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC)","volume":"40 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":"130902115","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

Microservices vs Serverless Deployment in AWS: A Case Study with an Image Processing Application 微服务与AWS中的无服务器部署:一个图像处理应用的案例研究

2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC) Pub Date : 2022-12-01 DOI: 10.1109/ucc56403.2022.00033

Raju Shrestha, Beebu Nisha

引用次数: 1

Towards Cognitive Self-Management of IoT-Edge-Cloud Continuum based on User Intents 基于用户意图的物联网-边缘-云连续体认知自我管理研究

2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC) Pub Date : 2022-12-01 DOI: 10.1109/UCC56403.2022.00055

Hui Song, A. Soylu, D. Roman

引用次数: 1

Robustness via Elasticity Accelerators for the IoT-Edge-Cloud Continuum 通过弹性加速器实现物联网边缘云连续体的鲁棒性

2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC) Pub Date : 2022-12-01 DOI: 10.1109/UCC56403.2022.00052

Hong Linh Truong, K. Magoutis

引用次数: 1

Exploiting the Potential of the Edge-Cloud Continuum with Self-distributing Systems 利用自分布系统开发边缘-云连续体的潜力

2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC) Pub Date : 2022-12-01 DOI: 10.1109/UCC56403.2022.00046

Roberto Rodrigues Filho, L. Bittencourt, F. Costa

{"title":"Exploiting the Potential of the Edge-Cloud Continuum with Self-distributing Systems","authors":"Roberto Rodrigues Filho, L. Bittencourt, F. Costa","doi":"10.1109/UCC56403.2022.00046","DOIUrl":"https://doi.org/10.1109/UCC56403.2022.00046","url":null,"abstract":"The Edge-Cloud Continuum offers a wide range of adaptive deployment settings for modern applications. However, in order to exploit the full potential of the edge-cloud infrastructure and platforms, applications have to be carefully crafted to be stateless and self-contained in small services or functions, i.e., the opposite of the classic stateful monolithic applications. In this paper, we explore an alternative approach that allows stateful single applications to also exploit the full potential of the edge-cloud continuum. We explore the concept of Self-distributing Systems(SDS) as a general approach for code offloading and as an elastic application-level mechanism for performance scale-out on the edge-cloud continuum. Our preliminary results indicate that SDS enables enough flexibility for applications to fully explore the edge-cloud resource mixture. Particularly, we describe our state management strategies for stateful code mobility; explore SDS as a general mechanism to exploit horizontal scaling on the cloud; and examine SDS as a general code offloading mechanism to move code from edge to cloud, showing the scenarios where our approach enables applications to positively exploit the edgecloud continuum for better performance.","PeriodicalId":203244,"journal":{"name":"2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC)","volume":"55 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":"121147790","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