{"title":"Evaluating caching and storage options on the Amazon Web Services Cloud","authors":"David Chiu, G. Agrawal","doi":"10.1109/GRID.2010.5697949","DOIUrl":null,"url":null,"abstract":"With the promise on-demand compute/storage resources, many users are deploying data-intensive scientific applications onto Clouds. To accelerate these applications, the prospect of caching intermediate data using the elastic compute and storage framework has proved promising. To this end, we believe that an in-depth study of cache placement decisions over various Cloud storage options would be highly beneficial to a large class of users. While tangential analyses have been proposed, ours in contrast focuses on cost-performance tradeoffs of maintaining a data cache with various parameters of any Cloud application. We have compared several Amazon Web Service (AWS Cloud) resources as possible cache placements and found that application dependent attributes like unit-data size, total cache size, and persistence, have far reaching implications on the cost of cache sustenance. Moreover, while instance-based caches expectedly yield higher cost, the performance that they afford may outweigh lower cost options.","PeriodicalId":6372,"journal":{"name":"2010 11th IEEE/ACM International Conference on Grid Computing","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2010-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"35","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 11th IEEE/ACM International Conference on Grid Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/GRID.2010.5697949","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 35
Abstract
With the promise on-demand compute/storage resources, many users are deploying data-intensive scientific applications onto Clouds. To accelerate these applications, the prospect of caching intermediate data using the elastic compute and storage framework has proved promising. To this end, we believe that an in-depth study of cache placement decisions over various Cloud storage options would be highly beneficial to a large class of users. While tangential analyses have been proposed, ours in contrast focuses on cost-performance tradeoffs of maintaining a data cache with various parameters of any Cloud application. We have compared several Amazon Web Service (AWS Cloud) resources as possible cache placements and found that application dependent attributes like unit-data size, total cache size, and persistence, have far reaching implications on the cost of cache sustenance. Moreover, while instance-based caches expectedly yield higher cost, the performance that they afford may outweigh lower cost options.
随着按需计算/存储资源的承诺,许多用户正在将数据密集型科学应用程序部署到云上。为了加速这些应用程序,使用弹性计算和存储框架缓存中间数据的前景被证明是有希望的。为此,我们相信对各种云存储选项的缓存放置决策进行深入研究将对大量用户非常有益。虽然已经提出了相关的分析,但我们的对比侧重于使用任何云应用程序的各种参数维护数据缓存的成本-性能权衡。我们比较了几种Amazon Web Service (AWS云)资源作为可能的缓存位置,发现应用程序相关的属性,如单元数据大小、总缓存大小和持久性,对缓存维持的成本有深远的影响。此外,尽管基于实例的缓存预期会产生更高的成本,但它们提供的性能可能会超过成本更低的选项。