2020 IEEE/ACM 13th International Conference on Utility and Cloud Computing (UCC)最新文献_第4页

Performance Analysis of Edge-Fog-Cloud Architectures in the Internet of Things 物联网中边缘-雾云架构的性能分析

2020 IEEE/ACM 13th International Conference on Utility and Cloud Computing (UCC) Pub Date : 2020-12-01 DOI: 10.1109/UCC48980.2020.00059

K. Geihs, Harun Baraki, A. D. Oliva

引用次数: 6

FIFE: an Infrastructure-as-Code Based Framework for Evaluating VM Instances from Multiple Clouds FIFE:一个基于基础架构即代码的框架，用于评估来自多云的VM实例

2020 IEEE/ACM 13th International Conference on Utility and Cloud Computing (UCC) Pub Date : 2020-12-01 DOI: 10.1109/UCC48980.2020.00028

Yuhui Lin, J. Briggs, A. Barker

引用次数: 1

Small is Beautiful: Distributed Orchestration of Spatial Deep Learning Workloads 小即是美:空间深度学习工作负载的分布式编排

2020 IEEE/ACM 13th International Conference on Utility and Cloud Computing (UCC) Pub Date : 2020-12-01 DOI: 10.1109/UCC48980.2020.00029

Daniel Rammer, Kevin Bruhwiler, Paahuni Khandelwal, Samuel Armstrong, S. Pallickara, S. Pallickara

{"title":"Small is Beautiful: Distributed Orchestration of Spatial Deep Learning Workloads","authors":"Daniel Rammer, Kevin Bruhwiler, Paahuni Khandelwal, Samuel Armstrong, S. Pallickara, S. Pallickara","doi":"10.1109/UCC48980.2020.00029","DOIUrl":"https://doi.org/10.1109/UCC48980.2020.00029","url":null,"abstract":"Several domains such as agriculture, urban sustainability, and meteorology entail processing satellite imagery for modeling and decision-making. In this study, we describe our novel methodology to train deep learning models over collections of satellite imagery. Deep learning models are computationally and resource expensive. As dataset sizes increase, there is a corresponding increase in the CPU, GPU, disk, and network I/O requirements to train models. Our methodology exploits spatial characteristics inherent in satellite data to partition, disperse, and orchestrate model training workloads. Rather than train a single, all-encompassing model we facilitate producing an ensemble of models - each tuned to a particular spatial extent. We support query-based retrieval of targeted portions of satellite imagery including those that satisfy properties relating to cloud occlusion, We validate the suitability of our methodology by supporting deep learning models for multiple spatial analyses. Our approach is agnostic of the underlying deep learning library. Our extensive empirical benchmark demonstrates the suitability of our methodology to not just preserve accuracy, but reduce completion times by 13.9x while reducing data movement costs by 4 orders of magnitude and ensuring frugal resource utilization.","PeriodicalId":125849,"journal":{"name":"2020 IEEE/ACM 13th International Conference on Utility and Cloud Computing (UCC)","volume":"155 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122764979","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: 2

Hearing loss classification via stationary wavelet entropy and genetic algorithm 基于平稳小波熵和遗传算法的听力损失分类

2020 IEEE/ACM 13th International Conference on Utility and Cloud Computing (UCC) Pub Date : 2020-12-01 DOI: 10.1109/UCC48980.2020.00050

Xujing Yao, Hei-Ran Cheong

引用次数: 2

VGGreNet: A Light-Weight VGGNet with Reused Convolutional Set 基于复用卷积集的轻量级VGGNet

2020 IEEE/ACM 13th International Conference on Utility and Cloud Computing (UCC) Pub Date : 2020-12-01 DOI: 10.1109/UCC48980.2020.00068

Ka‐Hou Chan, S. Im, W. Ke

引用次数: 8

Open-source Serverless Architectures: an Evaluation of Apache OpenWhisk 开源无服务器架构:Apache OpenWhisk的评估

2020 IEEE/ACM 13th International Conference on Utility and Cloud Computing (UCC) Pub Date : 2020-12-01 DOI: 10.1109/UCC48980.2020.00052

K. Djemame, Matthew O. Parker, Daniel Datsev

引用次数: 14

Dynamic Multi-objective Scheduling of Microservices in the Cloud 云环境下微服务的动态多目标调度

2020 IEEE/ACM 13th International Conference on Utility and Cloud Computing (UCC) Pub Date : 2020-12-01 DOI: 10.1109/UCC48980.2020.00061

H. M. Fard, R. Prodan, F. Wolf

{"title":"Dynamic Multi-objective Scheduling of Microservices in the Cloud","authors":"H. M. Fard, R. Prodan, F. Wolf","doi":"10.1109/UCC48980.2020.00061","DOIUrl":"https://doi.org/10.1109/UCC48980.2020.00061","url":null,"abstract":"For many applications, a microservices architecture promises better performance and flexibility compared to a conventional monolithic architecture. In spite of the advantages of a microservices architecture, deploying microservices poses various challenges for service developers and providers alike. One of these challenges is the efficient placement of microservices on the cluster nodes. Improper allocation of microservices can quickly waste resource capacities and cause low system throughput. In the last few years, new technologies in orchestration frameworks, such as the possibility of multiple schedulers for pods in Kubernetes, have improved scheduling solutions of microservices but using these technologies needs to involve both the service developer and the service provider in the behavior analysis of workloads. Using memory and CPU requests specified in the service manifest, we propose a general microservices scheduling mechanism that can operate efficiently in private clusters or enterprise clouds. We model the scheduling problem as a complex variant of the knapsack problem and solve it using a multi-objective optimization approach. Our experiments show that the proposed mechanism is highly scalable and simultaneously increases utilization of both memory and CPU, which in turn leads to better throughput when compared to the state-of-the-art.","PeriodicalId":125849,"journal":{"name":"2020 IEEE/ACM 13th International Conference on Utility and Cloud Computing (UCC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121325639","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: 5

Cloud Gaming: A QoE Study of Fast-paced Single-player and Multiplayer Gaming 云游戏:快节奏单人和多人游戏的QoE研究

2020 IEEE/ACM 13th International Conference on Utility and Cloud Computing (UCC) Pub Date : 2020-12-01 DOI: 10.1109/UCC48980.2020.00023

Sebastian Flinck Lindström, Markus Wetterberg, Niklas Carlsson

{"title":"Cloud Gaming: A QoE Study of Fast-paced Single-player and Multiplayer Gaming","authors":"Sebastian Flinck Lindström, Markus Wetterberg, Niklas Carlsson","doi":"10.1109/UCC48980.2020.00023","DOIUrl":"https://doi.org/10.1109/UCC48980.2020.00023","url":null,"abstract":"Cloud computing offers an attractive solution for modern computer games. By moving the increasingly demanding graphical calculations (e.g., generation of real-time video streams) to the cloud, consumers can play games using small, cheap devices. While cloud gaming has many advantages and is increasingly deployed, not much work has been done to understand the underlying factors impacting players’ user experience when moving the processing to the cloud. In this paper, we study the impact of the quality of service (QoS) factors most affecting the players’ quality of experience (QoE) and in-game performance. In particular, these relationships are studied from multiple perspectives using complementing analysis methods applied on the data collected via instrumented user tests. During the tests, we manipulated the players’ network conditions and collected low-level QoS metrics and in-game performance, and after each game, the users answered questions capturing their QoE. New insights are provided using different correlation/auto-correlation/cross-correlation statistics, regression models, and a thorough breakdown of the QoS metric most strongly correlated with the users’ QoE. We find that the frame age is the most important QoS metric for predicting in-game performance and QoE, and that spikes in the frame age caused by large frame transfers can have extended negative impact as they can cause processing backlogs. The study emphasizes the need to carefully consider and optimize the parts making up the frame age, including dependencies between the processing steps. By lowering the frame age, more enjoyable gaming experiences can be provided.","PeriodicalId":125849,"journal":{"name":"2020 IEEE/ACM 13th International Conference on Utility and Cloud Computing (UCC)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133867752","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: 10

Message from the Doctoral Symposium Chair 博士研讨会主席致辞

2020 IEEE/ACM 13th International Conference on Utility and Cloud Computing (UCC) Pub Date : 2020-12-01 DOI: 10.1109/ucc48980.2020.00016

Marcio Pereira de Sá

引用次数: 0

A Self-stabilizing Control Plane for Fog Ecosystems 雾生态系统的自稳定控制平面

2020 IEEE/ACM 13th International Conference on Utility and Cloud Computing (UCC) Pub Date : 2020-12-01 DOI: 10.1109/UCC48980.2020.00021

Z. Georgiou, Chryssis Georgiou, G. Pallis, E. Schiller, Demetris Trihinas

{"title":"A Self-stabilizing Control Plane for Fog Ecosystems","authors":"Z. Georgiou, Chryssis Georgiou, G. Pallis, E. Schiller, Demetris Trihinas","doi":"10.1109/UCC48980.2020.00021","DOIUrl":"https://doi.org/10.1109/UCC48980.2020.00021","url":null,"abstract":"Fog Computing is now emerging as the dominating paradigm bridging the compute and connectivity gap between sensing devices and latency-sensitive services. However, as fog deployments scale by accumulating numerous devices interconnected over highly dynamic and volatile network fabrics, the need for self-healing in the presence of failures is more evident. Using the prevailing methodology of self-stabilization, we propose a fault-tolerant framework for control planes that enables fog services to cope and recover from a very broad fault model. Specifically, our model considers network uncertainties, packet drops, node fail-stops and violations of the assumptions according to which the system was designed to operate (e.g., system state corruption). Our self-stabilizing algorithms guarantee automatic recovery within a constant number of communication rounds without the need for external (human) intervention. To showcase the framework’s effectiveness, the correctness proof of the self-stabilizing algorithmic process is accompanied by a comprehensive evaluation featuring an open and reproducible testbed utilizing real-world data from the smart vehicle domain. Results show that our framework ensures a fog system recovers from faults in constant time, analytics are computed correctly, while the control plane overhead scales linearly towards the IoT load.","PeriodicalId":125849,"journal":{"name":"2020 IEEE/ACM 13th International Conference on Utility and Cloud Computing (UCC)","volume":"113 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125194499","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