2016 IEEE International Conference on Cloud Engineering (IC2E)最新文献_第2页

Phurti: Application and Network-Aware Flow Scheduling for Multi-tenant MapReduce Clusters Phurti:多租户MapReduce集群的应用和网络感知流调度

2016 IEEE International Conference on Cloud Engineering (IC2E) Pub Date : 2016-04-04 DOI: 10.1109/IC2E.2016.21

Chris X. Cai, Shayan Saeed, Indranil Gupta, R. Campbell, Franck Le

{"title":"Phurti: Application and Network-Aware Flow Scheduling for Multi-tenant MapReduce Clusters","authors":"Chris X. Cai, Shayan Saeed, Indranil Gupta, R. Campbell, Franck Le","doi":"10.1109/IC2E.2016.21","DOIUrl":"https://doi.org/10.1109/IC2E.2016.21","url":null,"abstract":"Traffic for a typical MapReduce job in a data center consists of multiple network flows. Traditionally, network resources have been allocated to optimize network-level metrics such as flow completion time or throughput. Some recent schemes propose using application-aware scheduling which can shorten the average job completion time. However, most of them treat the core network as a black box with sufficient capacity. Even if only one network link in the core network becomes a bottleneck, it can hurt application performance. We design and implement a centralized flow-scheduling framework called Phurti with the goal of improving the completion time for jobs in a cluster shared among multiple Hadoop jobs (multi-tenant). Phurti communicates both with the Hadoop framework to retrieve job-level network traffic information and the OpenFlow-based switches to learn about the network topology. Phurti implements a novel heuristic called Smallest Maximum Sequential-traffic First (SMSF) that uses collected application and network information to perform traffic scheduling for MapReduce jobs. Our evaluation with real Hadoop workloads shows that compared to application and network-agnostic scheduling strategies, Phurti improves job completion time for 95% of the jobs, decreases average job completion time by 20%, tail job completion time by 13% and scales well with the cluster size and number of jobs.","PeriodicalId":430893,"journal":{"name":"2016 IEEE International Conference on Cloud Engineering (IC2E)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128673490","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: 15

Attribute-Based Partial Geo-Replication System 基于属性的部分异地复制系统

2016 IEEE International Conference on Cloud Engineering (IC2E) Pub Date : 2016-04-04 DOI: 10.1109/IC2E.2016.29

Hobin Yoon, Ada Gavrilovska, K. Schwan

引用次数: 1

On IO Latency Prediction Accuracy and Automated Load Balancing in Consolidated VM Environments 整合虚拟机环境下的IO延迟预测精度和自动负载均衡

2016 IEEE International Conference on Cloud Engineering (IC2E) Pub Date : 2016-04-04 DOI: 10.1109/IC2E.2016.32

Jun Nemoto, G. Ganger

引用次数: 1

UIE: User-Centric Interference Estimation for Cloud Applications 以用户为中心的云应用干扰估计

2016 IEEE International Conference on Cloud Engineering (IC2E) Pub Date : 2016-04-04 DOI: 10.1109/IC2E.2016.13

S. A. Javadi, Sagar Mehra, Bharath Kumar Reddy Vangoor, Anshul Gandhi

引用次数: 8

Autoscaling for Hadoop Clusters Hadoop集群的自动伸缩

2016 IEEE International Conference on Cloud Engineering (IC2E) Pub Date : 2016-04-04 DOI: 10.1109/IC2E.2016.11

Anshul Gandhi, Sidhartha Thota, Parijat Dube, Andrzej Kochut, Li Zhang

{"title":"Autoscaling for Hadoop Clusters","authors":"Anshul Gandhi, Sidhartha Thota, Parijat Dube, Andrzej Kochut, Li Zhang","doi":"10.1109/IC2E.2016.11","DOIUrl":"https://doi.org/10.1109/IC2E.2016.11","url":null,"abstract":"Unforeseen events such as node failures and resource contention can have a severe impact on the performance of data processing frameworks, such as Hadoop, especially in cloud environments where such incidents are common. SLA compliance in the presence of such events requires the ability to quickly and dynamically resize infrastructure resources. Unfortunately, the distributed and stateful nature of data processing frameworks makes it challenging to accurately scale the system at run-time. In this paper, we present the design and implementation of a model-driven autoscaling solution for Hadoop clusters. We first develop novel gray-box performance models for Hadoop workloads that specifically relate job execution times to resource allocation and workload parameters. We then employ these models to dynamically determine the resources required to successfully complete the Hadoop jobs as per the user-specified SLA under various scenarios including node failures and multi-job executions. Our experimental results on three different Hadoop cloud clusters and across different workloads demonstrate the efficacy of our models and highlight their autoscaling capabilities.","PeriodicalId":430893,"journal":{"name":"2016 IEEE International Conference on Cloud Engineering (IC2E)","volume":"167 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133937593","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: 37

Enabling Enterprise-Class Workloads in the Cloud 在云中启用企业级工作负载

2016 IEEE International Conference on Cloud Engineering (IC2E) Pub Date : 2016-04-04 DOI: 10.1109/IC2E.2016.41

V. Salapura, R. Mahindru

引用次数: 2

A Genetic Algorithm for Dynamic Cloud Application Brokerage 动态云应用经纪的遗传算法

2016 IEEE International Conference on Cloud Engineering (IC2E) Pub Date : 2016-04-04 DOI: 10.1109/IC2E.2016.25

Lino Chamorro, Fabio Lopez Pires, B. Barán

引用次数: 5

The Discovery Cloud: Accelerating and Democratizing Research on a Global Scale 发现云:在全球范围内加速和民主化研究

2016 IEEE International Conference on Cloud Engineering (IC2E) Pub Date : 2016-04-04 DOI: 10.1109/IC2E.2016.46

Ian T Foster, K. Chard, S. Tuecke

{"title":"The Discovery Cloud: Accelerating and Democratizing Research on a Global Scale","authors":"Ian T Foster, K. Chard, S. Tuecke","doi":"10.1109/IC2E.2016.46","DOIUrl":"https://doi.org/10.1109/IC2E.2016.46","url":null,"abstract":"Modern science and engineering require increasingly sophisticated information technology (IT) for data analysis, simulation, and related tasks. Yet the small to medium laboratories (SMLs) in which the majority of research advances occur increasingly lack the human and financial capital needed to acquire and operate such IT. New methods are needed to provide all researchers with access to state-of-the-art scientific capabilities, regardless of their location and budget. Industry has demonstrated the value of cloud-hosted software-and platform-as-a-service approaches, small businesses that outsource their IT to third-party providers slash costs and accelerate innovation. However, few business cloud services are transferable to science. We thus propose the Discovery Cloud, an ecosystem of new, community-produced services to which SMLs can outsource common activities, from data management and analysis to collaboration and experiment automation. We explain the need for a Discovery Platform to streamline the creation and operation of new and interoperable services, and a Discovery Exchange to facilitate the use and sustainability of Discovery Cloud services. We report on our experiences building early elements of the Discovery Platform in the form of Globus services, and on the experiences of those who have applied those services in innovative applications.","PeriodicalId":430893,"journal":{"name":"2016 IEEE International Conference on Cloud Engineering (IC2E)","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130825951","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

Multiple Virtual Machines Live Migration Performance Modelling -- VMware vMotion Based Study 多虚拟机实时迁移性能建模——基于VMware vMotion的研究

2016 IEEE International Conference on Cloud Engineering (IC2E) Pub Date : 2016-04-04 DOI: 10.1109/IC2E.2016.9

M. E. Elsaid, C. Meinel

引用次数: 17

A Hot-Page Aware Hybrid-Copy Migration Method 一种热页感知混合拷贝迁移方法

2016 IEEE International Conference on Cloud Engineering (IC2E) Pub Date : 2016-04-04 DOI: 10.1109/IC2E.2016.27

Shuang Wu, Bei Wang, Ce Yang, Qinming He, Jianhai Chen

引用次数: 1