2016 IEEE/ACM 9th International Conference on Utility and Cloud Computing (UCC)最新文献

Latency-Based Benchmarking of Cloud Service Providers 基于延迟的云服务提供商基准测试

2016 IEEE/ACM 9th International Conference on Utility and Cloud Computing (UCC) Pub Date : 2016-12-06 DOI: 10.1145/2996890.3007870

Vojtech Uhlir, Ondrej Tomanek, L. Kencl

{"title":"Latency-Based Benchmarking of Cloud Service Providers","authors":"Vojtech Uhlir, Ondrej Tomanek, L. Kencl","doi":"10.1145/2996890.3007870","DOIUrl":"https://doi.org/10.1145/2996890.3007870","url":null,"abstract":"With the ever-increasing trend of migration of applications to the Cloud environment, there is a growing need to thoroughly evaluate quality of the Cloud service itself, before deciding upon a hosting provider. Benchmarking the Cloud services is difficult though, due to the complex nature of the Cloud Computing setup and the diversity of locations, of applications and of their specific service requirements. However, such comparison may be crucial for decision making and for troubleshooting of services offered by the intermediate businesses - the so-called Cloud tenants. Existing cross–sectional studies and benchmarking methodologies provide only a shallow comparison of Cloud services, whereas state-of-the-art tooling for specific comparisons of application-performance parameters, such as for example latency, is insufficient. In this work, we propose a novel methodology for benchmarking of Cloud-service providers, which is based on latency measurements collected via active probing, and can be tailored to specific application needs. Furthermore, we demonstrate its applicability on a practical longitudinal study of real measurements of two major Cloud-service providers – Amazon and Microsoft.","PeriodicalId":350701,"journal":{"name":"2016 IEEE/ACM 9th International Conference on Utility and Cloud Computing (UCC)","volume":"54 69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124706579","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: 8

High Performance in the Cloud with FPGA Groups 使用FPGA组实现云中的高性能

2016 IEEE/ACM 9th International Conference on Utility and Cloud Computing (UCC) Pub Date : 2016-12-06 DOI: 10.1145/2996890.2996895

Anca Iordache, G. Pierre, P. Sanders, J. Coutinho, Mark Stillwell

{"title":"High Performance in the Cloud with FPGA Groups","authors":"Anca Iordache, G. Pierre, P. Sanders, J. Coutinho, Mark Stillwell","doi":"10.1145/2996890.2996895","DOIUrl":"https://doi.org/10.1145/2996890.2996895","url":null,"abstract":"Field-programmable gate arrays (FPGAs) can offer invaluable computational performance for many compute-intensive algorithms. However, to justify their purchase and administration costs it is necessary to maximize resource utilization over their expected lifetime. Making FPGAs available in a cloud environment would make them attractive to new types of users and applications and help democratize this increasingly popular technology. However, there currently exists no satisfactory technique for offering FPGAs as cloud resources and sharing them between multiple tenants. We propose FPGA groups, which are seen by their clients as a single virtual FPGA, and which aggregate the computational power of multiple physical FPGAs. FPGA groups are elastic, and they may be shared among multiple tenants. We present an autoscaling algorithm to maximize FPGA groups' resource utilization and reduce user-perceived computation latencies. FPGA groups incur a low overhead in the order of 0.09ms per submitted task. When faced with a challenging workload, the autoscaling algorithm increases resource utilization from 52% to 61% compared to a static resource allocation, while reducing task execution latencies by 61%.","PeriodicalId":350701,"journal":{"name":"2016 IEEE/ACM 9th International Conference on Utility and Cloud Computing (UCC)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128359365","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: 29

A Methodology for Full-System Power Modeling in Heterogeneous Data Centers 异构数据中心全系统功率建模方法

2016 IEEE/ACM 9th International Conference on Utility and Cloud Computing (UCC) Pub Date : 2016-12-06 DOI: 10.1145/2996890.2996899

Mauro Canuto, Raimon Bosch, Mario Macías, Jordi Guitart

引用次数: 13

Experiences Creating a Framework for Smart Traffic Control Using AWS IOT 使用AWS物联网创建智能交通控制框架的经验

2016 IEEE/ACM 9th International Conference on Utility and Cloud Computing (UCC) Pub Date : 2016-12-06 DOI: 10.1145/2996890.2996911

William Tarneberg, V. Chandrasekaran, M. Humphrey

{"title":"Experiences Creating a Framework for Smart Traffic Control Using AWS IOT","authors":"William Tarneberg, V. Chandrasekaran, M. Humphrey","doi":"10.1145/2996890.2996911","DOIUrl":"https://doi.org/10.1145/2996890.2996911","url":null,"abstract":"Public clouds such as Amazon Web Services (AWS) and Microsoft's Azure provide excellent capabilities for scalable Web applications and Hadoop-based processing. Recent additions to public clouds to support connected devices and IoT have the potential to similarly disrupt emerging home-grown and/or proprietary approaches. While early public cloud IoT success stories have focused on smaller-scale scenarios such as connected houses, it is unclear to what extent these new public cloud mechanisms and abstractions are suitable and effective for larger-scale and/or scientific scenarios, which often have a different set of constraints or requirements. In this paper, the design and implementation of a representative cloud-based IoT infrastructure in a specific public cloud – AWS – is presented. The system created is for dynamic vehicle traffic control based on vehicle volumes/patterns and public transport punctuality. We find that constructing server-less, stateful, and data driven IoT applications in AWS that can operate in real-time is non-trivial. The primary challenges span application manageability and design, latency performance, asynchronicity, and scalability.","PeriodicalId":350701,"journal":{"name":"2016 IEEE/ACM 9th International Conference on Utility and Cloud Computing (UCC)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121523203","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: 48

FaaSeC: Enabling Forensics-as-a-Service for Cloud Computing Systems FaaSeC:为云计算系统启用取证即服务

2016 IEEE/ACM 9th International Conference on Utility and Cloud Computing (UCC) Pub Date : 2016-12-06 DOI: 10.1145/2996890.3009904

B. K. Raju, Bhupendra Moharil, G. Geethakumari

引用次数: 10

Performance-Efficient Service Deployment and Scheduling Methods for Composite Cloud Services 面向复合云服务的高性能服务部署和调度方法

2016 IEEE/ACM 9th International Conference on Utility and Cloud Computing (UCC) Pub Date : 2016-12-06 DOI: 10.1145/2996890.3007866

Kuo-Chan Huang, Yu-Chun Lu, Meng-Han Tsai, Ying-Jhih Wu, Hsi-Ya Chang

引用次数: 11

Cloud-Based Parametrized Publish/Subscribe System for Public Safety Applications in Smarter Cities 智慧城市公共安全应用的基于云的参数化发布/订阅系统

2016 IEEE/ACM 9th International Conference on Utility and Cloud Computing (UCC) Pub Date : 2016-12-06 DOI: 10.1145/2996890.3007862

Aljawharah Al-muaythir, M. Anwar Hossain

{"title":"Cloud-Based Parametrized Publish/Subscribe System for Public Safety Applications in Smarter Cities","authors":"Aljawharah Al-muaythir, M. Anwar Hossain","doi":"10.1145/2996890.3007862","DOIUrl":"https://doi.org/10.1145/2996890.3007862","url":null,"abstract":"Smart city is the future city that benefits from the emerging technologies to manage the complexity, and improve the quality of life in many aspects including public safety, healthcare, transportation, or energy. Public safety is an important issue, and in urban environment such as smart city, it is necessary to conduct much research to study the surveillance systems for public safety. It is important to know how to improve its performance especially with the utilization of cloud solutions in order to find the suspicious events in the near real time. For emergency and monitoring applications in distributed systems, Publish/Subscribe system is widely used and studied because of its advantages including the loosely coupled form of interaction, and the full decoupling in time, space, and synchronization between publishers and subscribers. So, combining its advantages with the offered benefits of cloud can be an optimal solution in order to use it in public safety surveillance applications in smarter cities. Subscriptions in a Publish/Subscribe system can be static or parametric. There is extensive work on static subscriptions where its constituent predicates stay fixed once defined. In contrast, there is little work on the emerging Publish/Subscribe parametric subscriptions despite of its proven efficiency in the existing few studies. In this work, we examine the use of the parametric subscriptions in the context of public safety scenario. We illustrate the use of it by showing some examples. Furthermore, we propose cloud-based Publish/Subscribe framework with some details that explain how system works.","PeriodicalId":350701,"journal":{"name":"2016 IEEE/ACM 9th International Conference on Utility and Cloud Computing (UCC)","volume":"130 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115893229","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: 3

The Research of MapReduce Load Balancing Based on Multiple Partition Algorithm 基于多分区算法的MapReduce负载均衡研究

2016 IEEE/ACM 9th International Conference on Utility and Cloud Computing (UCC) Pub Date : 2016-12-06 DOI: 10.1145/2996890.3007886

Suzhen Wang, Haowei Zhou

引用次数: 4

Quick Eviction of Virtual Machines through Proactive Live Snapshots 通过主动实时快照快速退出虚拟机

2016 IEEE/ACM 9th International Conference on Utility and Cloud Computing (UCC) Pub Date : 2016-12-06 DOI: 10.1145/2996890.2996891

Dinuni Fernando, Hardik Bagdi, Yaohui Hu, Ping Yang, Kartik Gopalan, C. Kamhoua, K. Kwiat

{"title":"Quick Eviction of Virtual Machines through Proactive Live Snapshots","authors":"Dinuni Fernando, Hardik Bagdi, Yaohui Hu, Ping Yang, Kartik Gopalan, C. Kamhoua, K. Kwiat","doi":"10.1145/2996890.2996891","DOIUrl":"https://doi.org/10.1145/2996890.2996891","url":null,"abstract":"Cloud computing platforms routinely use virtualization to improve service availability, resiliency, and flexibility. Live migration of Virtual Machines (VM) is a key technique to quickly migrate workloads in response to events such as impending failure or load changes. Despite extensive research, state-of-the-art live migration approaches take a long time to migrate a VM (in the order of tens of seconds for moderately sized VMs) which in turn negatively impacts the application performance during migration. We present Quick Eviction, a new approach to significantly speed up the eviction of a VM from the source host with low impact on VM's performance during migration. Our approach can also improve the effectiveness of VM resilience tools that back up a VM's state in anticipation of system failures. The insight behind Quick Eviction is that the majority of time to evict a VM during migration is due to the transfer of memory contents over the network, sometimes repeatedly. Before migration, Quick Eviction regularly snapshots the VM's memory to a destination or a failover node. During the actual migration, Quick Eviction has to transfer only a small amount of dirtied memory resulting in a very short time to completely evict the VM out of the source. The key challenge is to dynamically adapt the snapshot intervals so as to have minimal impact on application performance during migration. We show that our implementation of Quick Eviction in the KVM/QEMU platform reduces the eviction time of a VM by significant factors for both idle and write-intensive VMs compared to traditional pre-copy algorithm, while maintaining low overheads on application performance and network.","PeriodicalId":350701,"journal":{"name":"2016 IEEE/ACM 9th International Conference on Utility and Cloud Computing (UCC)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130195402","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: 3

Efficient Estimation and Control of WSANs for the Greenhouse Environment 温室环境无线传感器网络的有效估计与控制

2016 IEEE/ACM 9th International Conference on Utility and Cloud Computing (UCC) Pub Date : 2016-12-06 DOI: 10.1145/2996890.3007853

Xingzhen Bai, Maoyong Cao, Lu Liu, John Panneerselvam, Qiao‐Xi Sun

引用次数: 4