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CRL: Efficient Concurrent Regeneration Codes with Local Reconstruction in Geo-Distributed Storage Systems
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作者 Quan-Qing Xu Wei-Ya Xi +1 位作者 Khai Leong Yong Chao Jin 《计算机科学技术学报:英文版》 SCIE EI CSCD 2018年第6期1140-1151,共12页
As a typical erasure coding choice, Reed-Solomon (RS) codes have such high repair cost that there is a penaltyfor high reliability and storage efficiency, thereby they are not suitable in geo-distributed storage sys... As a typical erasure coding choice, Reed-Solomon (RS) codes have such high repair cost that there is a penaltyfor high reliability and storage efficiency, thereby they are not suitable in geo-distributed storage systems. We present anovel family of concurrent regeneration codes with local reconstruction (CRL) in this paper. The CRL codes enjoy threebenefits. Firstly, they are able to minimize the network bandwidth for node repair. Secondly, they can reduce the numberof accessed nodes by calculating parities from a subset of data chunks and using an implied parity chunk. Thirdly, they arefaster than existing erasure codes for reconstruction in geo-distributed storage systems. In addition, we demonstrate howthe CRL codes overcome the limitations of the Reed-Solomon codes. We also illustrate analytically that they are excellent inthe trade-off between chunk locality and minimum distance. Furthermore, we present theoretical analysis including latencyanalysis and reliability analysis for the CRL codes. By using quantity comparisons, we prove that CRL(6, 2, 2) is only0.657x of Azure LRC(6, 2, 2), where there are six data chunks, two global parities, and two local parities, and CRL(10,4, 2) is only 0.656x of HDFS-Xorbas(10, 4, 2), where there are 10 data chunks, four local parities, and two global paritiesrespectively, in terms of data reconstruction times. Our experimental results show the performance of CRL by conductingperformance evaluations in both two kinds of environments: 1) it is at least 57.25% and 66.85% more than its competitorsin terms of encoding and decoding throughputs in memory, and 2) it has at least 1.46x and 1.21x higher encoding anddecoding throughputs than its competitors in JBOD (Just a Bunch Of Disks). We also illustrate that CRL is 28.79% and30.19% more than LRC on encoding and decoding throughputs in a geo-distributed environment. 展开更多
关键词 CONCURRENT REGENERATION code local RECONSTRUCTION geo-distributed storage system
雾计算的概念、相关研究与应用 预览 被引量:3
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作者 贾维嘉 周小杰 《通信学报》 CSCD 北大核心 2018年第5期153-165,共13页
首先,系统地分析和总结了雾计算的研究现状,重点介绍了雾计算出现的背景及其相对于云计算的优势,对雾计算及其他相似的计算模式进行比较,指出了雾计算相比于传统计算模式的优点。然后,总结了雾计算的体系结构与各层功能。同时,对于雾计... 首先,系统地分析和总结了雾计算的研究现状,重点介绍了雾计算出现的背景及其相对于云计算的优势,对雾计算及其他相似的计算模式进行比较,指出了雾计算相比于传统计算模式的优点。然后,总结了雾计算的体系结构与各层功能。同时,对于雾计算在网络管理和资源调度这2个方面的研究问题展开讨论,总结了前人提出的解决方法并指出了现有方法的不足。最后,对于雾计算的一些相关应用进行了阐述,并以智能驾驶、工业物联网这2个示范应用为例指出了当前雾计算在实际应用上仍需攻关的重要课题。 展开更多
关键词 雾计算 时延 地理分布 移动性
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Efficient Location-Aware Data Placement for Data-Intensive Applications in Geo-distributed Scientific Data Centers
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作者 Jinghui Zhang Jian Chen +1 位作者 Junzhou Luo Aibo Song 《清华大学学报:自然科学英文版》 EI CAS CSCD 2016年第5期471-481,共11页
Recent developments in cloud computing and big data have spurred the emergence of data-intensive applications for which massive scientific datasets are stored in globally distributed scientific data centers that have ... Recent developments in cloud computing and big data have spurred the emergence of data-intensive applications for which massive scientific datasets are stored in globally distributed scientific data centers that have a high frequency of data access by scientists worldwide. Multiple associated data items distributed in different scientific data centers may be requested for one data processing task, and data placement decisions must respect the storage capacity limits of the scientific data centers. Therefore, the optimization of data access cost in the placement of data items in globally distributed scientific data centers has become an increasingly important goal.Existing data placement approaches for geo-distributed data items are insufficient because they either cannot cope with the cost incurred by the associated data access, or they overlook storage capacity limitations, which are a very practical constraint of scientific data centers. In this paper, inspired by applications in the field of high energy physics, we propose an integer-programming-based data placement model that addresses the above challenges as a Non-deterministic Polynomial-time(NP)-hard problem. In addition we use a Lagrangian relaxation based heuristics algorithm to obtain ideal data placement solutions. Our simulation results demonstrate that our algorithm is effective and significantly reduces overall data access cost. 展开更多
分布式云的研究进展综述 预览 被引量:3
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作者 张晓丽 杨家海 +1 位作者 孙晓晴 吴建平 《软件学报》 CSCD 北大核心 2018年第7期2116-2132,共17页
云计算作为全新的计算模式,将数据中心的资源包括计算、存储等基础设施资源通过虚拟化技术以服务的形式交付给用户,使得用户可以通过互联网按需访问云内计算资源来运行应用.为面向用户提供更好的服务,分布式云跨区域联合多个云站点,创... 云计算作为全新的计算模式,将数据中心的资源包括计算、存储等基础设施资源通过虚拟化技术以服务的形式交付给用户,使得用户可以通过互联网按需访问云内计算资源来运行应用.为面向用户提供更好的服务,分布式云跨区域联合多个云站点,创建巨大的资源池,同时利用地理分布优势改善服务质量.近年来,分布式云的研究逐渐成为学术界和工业界的热点.围绕分布式云系统中研究的基本问题,介绍了国际、国内的研究现状,包括分布式云系统的架构设计、资源调度与性能优化策略和云安全方案等,并展望分布式云的发展趋势. 展开更多
关键词 云计算 分布式云 云架构 资源调度 云安全
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云环境下面向跨域作业的调度方法 预览
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作者 李焱 郑亚松 +2 位作者 李婧 朱春鸽 刘欣然 《电子学报》 CSCD 北大核心 2017年第10期2416-2424,共9页
云环境下,因数据局部性或是任务对资源的特殊偏好,一个作业所包含的任务往往需要在不同的数据中心局点上运行,此类作业称为跨域作业.跨域作业的完成时间取决于最慢任务的执行效率,即存在木桶效应.针对各域资源能力异构条件下不合理的调... 云环境下,因数据局部性或是任务对资源的特殊偏好,一个作业所包含的任务往往需要在不同的数据中心局点上运行,此类作业称为跨域作业.跨域作业的完成时间取决于最慢任务的执行效率,即存在木桶效应.针对各域资源能力异构条件下不合理的调度策略导致跨域作业执行时间跨度过长的问题,本文提出一种面向跨域作业的启发式调度方法 MIN-Max-Min,优先选择期望完成时间最短的作业执行.通过实验表明,与先来先服务的策略相比,该方法能将跨域作业平均执行时间跨度减少40%以上. 展开更多
关键词 云计算 跨域数据中心 跨域作业
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Wide Area Analytics for Geographically Distributed Datacenters
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作者 Siqi Ji Baochun Li 《清华大学学报:自然科学英文版》 EI CAS CSCD 2016年第2期125-135,共11页
Big data analytics,the process of organizing and analyzing data to get useful information,is one of the primary uses of cloud services today.Traditionally,collections of data are stored and processed in a single datac... Big data analytics,the process of organizing and analyzing data to get useful information,is one of the primary uses of cloud services today.Traditionally,collections of data are stored and processed in a single datacenter.As the volume of data grows at a tremendous rate,it is less efficient for only one datacenter to handle such large volumes of data from a performance point of view.Large cloud service providers are deploying datacenters geographically around the world for better performance and availability.A widely used approach for analytics of geo-distributed data is the centralized approach,which aggregates all the raw data from local datacenters to a central datacenter.However,it has been observed that this approach consumes a significant amount of bandwidth,leading to worse performance.A number of mechanisms have been proposed to achieve optimal performance when data analytics are performed over geo-distributed datacenters.In this paper,we present a survey on the representative mechanisms proposed in the literature for wide area analytics.We discuss basic ideas,present proposed architectures and mechanisms,and discuss several examples to illustrate existing work.We point out the limitations of these mechanisms,give comparisons,and conclude with our thoughts on future research directions. 展开更多
关键词 big data ANALYTICS geo-distributed datacenters
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