2019 19th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID)最新文献_第5页

Welcome Message from the CCGRID 2019 Workshop/Tutorial Chairs CCGRID 2019研讨会/讲座主席欢迎辞

2019 19th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID) Pub Date : 2019-05-01 DOI: 10.1109/ccgrid.2019.00007

G. Pallis, A. Toosi, B. Sotomayor

引用次数: 0

[Publisher's information] (发布者的信息)

2019 19th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID) Pub Date : 2019-05-01 DOI: 10.1109/ccgrid.2019.00091

引用次数: 0

Proximity-Aware Traffic Routing in Distributed Fog Computing Platforms 分布式雾计算平台中邻近感知的流量路由

2019 19th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID) Pub Date : 2019-05-01 DOI: 10.1109/CCGRID.2019.00062

Alice Fahs, G. Pierre

引用次数: 28

Miracle: An Agile Colocation Platform for Enabling XaaS Cloud Architecture 奇迹:一个支持XaaS云架构的敏捷托管平台

2019 19th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID) Pub Date : 2019-05-01 DOI: 10.1109/CCGRID.2019.00078

Mukhtiar Bano, Umar Ahmad Qureshi, R. N. B. Rais, M. Tufail, A. Qayyum

{"title":"Miracle: An Agile Colocation Platform for Enabling XaaS Cloud Architecture","authors":"Mukhtiar Bano, Umar Ahmad Qureshi, R. N. B. Rais, M. Tufail, A. Qayyum","doi":"10.1109/CCGRID.2019.00078","DOIUrl":"https://doi.org/10.1109/CCGRID.2019.00078","url":null,"abstract":"Cloud Service Providers (CSPs) offer services to large scale business organizations using different cloud frameworks including Platform as a Service (PaaS), Software as a Service (SaaS), Infrastructure as a Service (IaaS) and follow various business models to realize profitability while addressing the challenges regarding scalability, guaranteed connectivity and security. In order to address such issues, Colocation service providers (third-party organizations) lease physical infrastructure services in geographically distributed locations known as colocation points (Co-Lo). Organization eventually request services available within a given colocation facility. Such a solution is challenging in a way that not all service offerings are available on a given Co-Lo provider. The proposed platform 'Miracle' addresses aforementioned challenges by providing agility and cost reduction to organizations aiming to leverage colocation services. Our solution leverages Software Defined Networking along with service and performance awareness technology to virtualize the organization's presence in a Co-Lo. Hence, organization can be associated or re-associated on the fly to required Co-Lo according to its service connectivity needs at a given time. Our solution may benefit cloud services industry and their customers while adopting the open-source computing technologies such as virtualization, dynamic provisioning environment, and multi-tenancy resulting in offering increased efficiency in a profitable and cost effective manner.","PeriodicalId":234571,"journal":{"name":"2019 19th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130683982","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

A Performance Driven Micro Services-Based Architecture/System for Analyzing Noisy IoT Data 基于性能驱动的微服务架构/系统分析物联网噪声数据

2019 19th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID) Pub Date : 2019-05-01 DOI: 10.1109/CCGRID.2019.00031

M. Bolic, S. Majumdar

{"title":"A Performance Driven Micro Services-Based Architecture/System for Analyzing Noisy IoT Data","authors":"M. Bolic, S. Majumdar","doi":"10.1109/CCGRID.2019.00031","DOIUrl":"https://doi.org/10.1109/CCGRID.2019.00031","url":null,"abstract":"The Internet of Things (IoT) technology presents a complex and challenging paradigm where a huge amount of noisy raw sensor data is collected in order to observe and detect critical events occurring on the system, and generate alarms when required. The biggest challenge of the IoT systems is that the systems collect a massive amount of uncertain data from diverse IoT devices connected through the network. In addition, some events are inferred from other events and uncertainty is propagated from parent events to the inferred events, which additionally contributes to overall system uncertainty. The observed complex events are a complex relationship of primitive events that are produced by IoT devices and collected in IoT systems. A survey performed on existing prior arts on quantifying uncertainty for complex events concluded that proposed existing solutions are unable to scale under heavy loads of incoming data. This paper presents a micro-service based notification methodology that uses complex event recognition (both complex event processing and probabilistic programming) to handle IoT systems uncertainty. In addition, the paper analyzes and recommends existing big data platforms for processing complex events in IoT systems. The current focus of our work includes research and development of the optimized deadline-based and cost-effective resource allocation algorithm in Apache Spark for Uncertain IoT Notification systems.","PeriodicalId":234571,"journal":{"name":"2019 19th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID)","volume":" 24","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113952839","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: 4

Scalable Video Transcoding in Public Clouds 公共云中可扩展的视频转码

2019 19th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID) Pub Date : 2019-05-01 DOI: 10.1109/CCGRID.2019.00017

Qingye Jiang, Young Choon Lee, Albert Y. Zomaya

引用次数: 5

Theoretical Scalability Analysis of Distributed Deep Convolutional Neural Networks 分布式深度卷积神经网络的理论可扩展性分析

2019 19th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID) Pub Date : 2019-05-01 DOI: 10.1109/CCGRID.2019.00068

Adrián Castelló, M. F. Dolz, E. S. Quintana‐Ortí, J. Duato

引用次数: 12

Scalability of the NewMadeleine Communication Library for Large Numbers of MPI Point-to-Point Requests 针对大量MPI点对点请求的NewMadeleine通信库的可扩展性

2019 19th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID) Pub Date : 2019-05-01 DOI: 10.1109/CCGRID.2019.00051

Alexandre Denis

引用次数: 6

Application-Level Differential Checkpointing for HPC Applications with Dynamic Datasets 具有动态数据集的HPC应用程序的应用级差分检查点

2019 19th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID) Pub Date : 2019-05-01 DOI: 10.1109/CCGRID.2019.00015

Kai Keller, L. Bautista-Gomez

{"title":"Application-Level Differential Checkpointing for HPC Applications with Dynamic Datasets","authors":"Kai Keller, L. Bautista-Gomez","doi":"10.1109/CCGRID.2019.00015","DOIUrl":"https://doi.org/10.1109/CCGRID.2019.00015","url":null,"abstract":"High-performance computing (HPC) requires resilience techniques such as checkpointing in order to tolerate failures in supercomputers. As the number of nodes and memory in supercomputers keeps on increasing, the size of checkpoint data also increases dramatically, sometimes causing an I/O bottleneck. Differential checkpointing (dCP) aims to minimize the checkpointing overhead by only writing data differences. This is typically implemented at the memory page level, sometimes complemented with hashing algorithms. However, such a technique is unable to cope with dynamic-size datasets. In this work, we present a novel dCP implementation with a new file format that allows fragmentation of protected datasets in order to support dynamic sizes. We identify dirty data blocks using hash algorithms. In order to evaluate the dCP performance, we ported the HPC applications xPic, LULESH 2.0 and Heat2D and analyze them regarding their potential of reducing I/O with dCP and how this data reduction influences the checkpoint performance. In our experiments, we achieve reductions of up to 62% of the checkpoint time.","PeriodicalId":234571,"journal":{"name":"2019 19th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133529663","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: 7

Welcome from the General Chairs 欢迎各位主席

2019 19th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID) Pub Date : 2019-05-01 DOI: 10.1109/ccgrid.2019.00005

引用次数: 0