2015 IEEE International Conference on Cloud Engineering最新文献_第6页

Information Flow Control for Strong Protection with Flexible Sharing in PaaS PaaS中灵活共享的强保护信息流控制

2015 IEEE International Conference on Cloud Engineering Pub Date : 2015-03-09 DOI: 10.1109/IC2E.2015.64

Thomas Pasquier, Jatinder Singh, J. Bacon

引用次数: 17

I/O Performance Modeling for Big Data Applications over Cloud Infrastructures 基于云基础设施的大数据应用I/O性能建模

2015 IEEE International Conference on Cloud Engineering Pub Date : 2015-03-09 DOI: 10.1109/IC2E.2015.29

Ioannis Mytilinis, Dimitrios Tsoumakos, Verena Kantere, Anastassios Nanos, N. Koziris

{"title":"I/O Performance Modeling for Big Data Applications over Cloud Infrastructures","authors":"Ioannis Mytilinis, Dimitrios Tsoumakos, Verena Kantere, Anastassios Nanos, N. Koziris","doi":"10.1109/IC2E.2015.29","DOIUrl":"https://doi.org/10.1109/IC2E.2015.29","url":null,"abstract":"Big Data applications receive an ever-increasing amount of attention, thus becoming a dominant class of applications that are deployed over virtualized environments. Cloud environments entail a large amount of complexity relative to I/O performance. The use of Big Data increases the complexity of I/O management as well as its characterization and prediction: As I/O operations become growingly dominant in such applications, the intricacies of virtualization, different storage back ends and deployment setups significantly hinder our ability to analyze and correctly predict I/O performance. To that end, this work proposes an end-to-end modeling technique to predict performance of I/O--intensive Big Data applications running over cloud infrastructures. We develop a model tuned over application and infrastructure dimensions: Primitive I/O operations, data access patterns, storage back ends and deployment parameters. The trained model can be used to predict both I/O but also general task performance. Our evaluation results show that for jobs which are dominated by I/O operations, such as I/O-bound MapReduce jobs, our model is capable of predicting execution time with an accuracy close to 90% that decreases as application processing becomes more complex.","PeriodicalId":395715,"journal":{"name":"2015 IEEE International Conference on Cloud Engineering","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132571565","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

FIDDLE: Federated Infrastructure Discovery and Description Language 联邦基础设施发现和描述语言

2015 IEEE International Conference on Cloud Engineering Pub Date : 2015-03-09 DOI: 10.1109/IC2E.2015.77

A. Willner, R. Loughnane, T. Magedanz

{"title":"FIDDLE: Federated Infrastructure Discovery and Description Language","authors":"A. Willner, R. Loughnane, T. Magedanz","doi":"10.1109/IC2E.2015.77","DOIUrl":"https://doi.org/10.1109/IC2E.2015.77","url":null,"abstract":"Considerable efforts have been spent on designing architectures to manage heterogeneous resources across multiple administrative domains. Specific fields of application are federated cloud computing (Intercloud) approaches and distributed testbeds, among others. An important interoperability challenge that arises in this context is the exchange of information about the provided resources and their dependencies. Existing work usually rests upon schematic data models, which impede the discovery and management of heterogeneous resources between autonomous sites. One way of addressing this issue is to exchange semantic information models. In this paper, we exploit such approaches to formally define federations, including their infrastructures and the life-cycle of the offered resources and services. The requirements of this work have been derived from several research projects and the results are in process of being standardized by an international body. The main contribution of this work is a higher level (upper) ontology and initial integration concepts for it. These contributions form a basis for further work in the general context of distributed semantic resource management.","PeriodicalId":395715,"journal":{"name":"2015 IEEE International Conference on Cloud Engineering","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127384642","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

EAGER: Deployment-Time API Governance for Modern PaaS Clouds EAGER:面向现代PaaS云的部署时API治理

2015 IEEE International Conference on Cloud Engineering Pub Date : 2015-03-09 DOI: 10.1109/IC2E.2015.69

Hiranya Jayathilaka, C. Krintz, R. Wolski

引用次数: 6

A Bird's-Eye View on Modelling Malleable Multi-cloud Applications 可塑多云应用建模的鸟瞰图

2015 IEEE International Conference on Cloud Engineering Pub Date : 2015-03-09 DOI: 10.1109/IC2E.2015.94

Mohammad Hamdaqa

引用次数: 1

Efficient Prototyping of Fault Tolerant Map-Reduce Applications with Docker-Hadoop 基于Docker-Hadoop的容错Map-Reduce应用的高效原型

2015 IEEE International Conference on Cloud Engineering Pub Date : 2015-03-09 DOI: 10.1109/IC2E.2015.73

J. Rey, M. Cogorno, Sergio Nesmachnow, L. Steffenel

引用次数: 11

Automating Cloud Service Level Agreements Using Semantic Technologies 使用语义技术自动化云服务水平协议

2015 IEEE International Conference on Cloud Engineering Pub Date : 2015-03-09 DOI: 10.1109/IC2E.2015.63

K. Joshi, C. Pearce

引用次数: 19

Using Trustworthy Simulation to Engineer Cloud Schedulers 使用可信模拟来设计云调度器

2015 IEEE International Conference on Cloud Engineering Pub Date : 2015-03-09 DOI: 10.1109/IC2E.2015.14

A. Pucher, Emre Gul, R. Wolski, C. Krintz

{"title":"Using Trustworthy Simulation to Engineer Cloud Schedulers","authors":"A. Pucher, Emre Gul, R. Wolski, C. Krintz","doi":"10.1109/IC2E.2015.14","DOIUrl":"https://doi.org/10.1109/IC2E.2015.14","url":null,"abstract":"In recent years, researchers have contributed promising new techniques for allocating cloud resources in more robust, efficient, and ecologically sustainable ways. Unfortunately, the wide-spread use of these techniques in production systems has, to date, remained elusive. One reason for this is that the state of the art for investigating these innovations at scale often relies solely on model-driven simulation. Production-grade cloud software, however, demands certainty and precision for development and business planning that only comes from validating simulation against empirical observation. In this work, we take an alternative approach to facilitating cloud research and engineering in order to transition innovations to production deployment faster. In particular, we present a new methodology that complements existing model-driven simulation with platform-specific and statistically trustworthy results. We simulate systems at scales and on time frames that are testable, and then, based on the statistical validation of these simulations, investigate scenarios beyond those feasibly observable in practice. We demonstrate the approach by developing an energy-aware cloud scheduler and evaluating it using production and synthetic traces in faster than real time. Our results show that we can accurately simulate a production IaaS system, ease capacity planning, and expedite the reliable development of its components and extensions.","PeriodicalId":395715,"journal":{"name":"2015 IEEE International Conference on Cloud Engineering","volume":"120 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116373798","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: 20

Harp: Collective Communication on Hadoop Harp: Hadoop上的集体通信

2015 IEEE International Conference on Cloud Engineering Pub Date : 2015-03-09 DOI: 10.1109/IC2E.2015.35

Bingjing Zhang, Yang Ruan, J. Qiu

{"title":"Harp: Collective Communication on Hadoop","authors":"Bingjing Zhang, Yang Ruan, J. Qiu","doi":"10.1109/IC2E.2015.35","DOIUrl":"https://doi.org/10.1109/IC2E.2015.35","url":null,"abstract":"Big data processing tools have evolved rapidly in recent years. MapReduce has proven very successful but is not optimized for many important analytics, especially those involving iteration. In this regard, Iterative MapReduce frameworks improve performance of MapReduce job chains through caching. Further, Pregel, Giraph and Graph Lab abstract data as a graph and process it in iterations. But all these tools are designed with a fixed data abstraction and have limited collective communication support to synchronize application data and algorithm control states among parallel processes. In this paper, we introduce a collective communication abstraction layer which provides efficient collective communication operations on several common data abstractions such as arrays, key-values and graphs, and define a Map Collective programming model which serves the diverse collective communication demands in different parallel algorithms. We implement a library called Harp to provide the features above and plug it into Hadoop so that applications abstracted in Map Collective model can be easily developed on top of MapReduce framework and conveniently integrated with other tools in Apache Big Data Stack. With improved expressiveness in the abstraction and excellent performance on the implementation, we can simultaneously support various applications from HPC to Cloud systems together with high performance.","PeriodicalId":395715,"journal":{"name":"2015 IEEE International Conference on Cloud Engineering","volume":"206 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115739348","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: 38

Comparing Containers versus Virtual Machines for Achieving High Availability 比较容器和虚拟机实现高可用性

2015 IEEE International Conference on Cloud Engineering Pub Date : 2015-03-09 DOI: 10.1109/IC2E.2015.79

Wubin Li, A. Kanso

引用次数: 53