Proceedings of the 4th annual Symposium on Cloud Computing最新文献

{"title":"Syndicate: democratizing cloud storage and caching through service composition","authors":"Jude C. Nelson, L. Peterson","doi":"10.1145/2523616.2525954","DOIUrl":"https://doi.org/10.1145/2523616.2525954","url":null,"abstract":"The cloud is changing the way we share data. We can keep data on local workstations and file servers for quick access, but face the challenge of sharing it with a large number of people. Alternatively, we can put our data into one or more cloud storage systems to share it with many other users, but then we cannot access it as quickly or as cheaply. Moreover, local copies of data can get out of sync with cloud copies, causing remote users to see old versions. Our solution is Syndicate, a virtual cloud storage system that composes local storage, cloud storage, and commodity CDNs and network caches to transparently give users the best of both worlds.","PeriodicalId":298547,"journal":{"name":"Proceedings of the 4th annual Symposium on Cloud Computing","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131758352","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}

{"title":"Session details: Data replication and consistency","authors":"Haryadi S. Gunawi","doi":"10.1145/3260254","DOIUrl":"https://doi.org/10.1145/3260254","url":null,"abstract":"","PeriodicalId":298547,"journal":{"name":"Proceedings of the 4th annual Symposium on Cloud Computing","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128405686","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}

{"title":"Recommending just enough memory for analytics","authors":"Charles Reiss, R. Katz","doi":"10.1145/2523616.2525957","DOIUrl":"https://doi.org/10.1145/2523616.2525957","url":null,"abstract":"MapReduce was designed by Google for large-scale data analysis on slow but cheap disk-based storage. Nevertheless, memory has declined in price to where cost-effective machines offer ever larger memory capacity. Furthermore, a more diverse data analyst community, with smaller datasets, has emerged. These trends motivate new parallel processing frameworks, like Spark [2], with better support for in-memory data analysis.","PeriodicalId":298547,"journal":{"name":"Proceedings of the 4th annual Symposium on Cloud Computing","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125378239","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}

{"title":"The wisdom of virtual crowds: mining datacenter telemetry to collaboratively debug performance","authors":"Dragos Ionescu, Rean Griffith","doi":"10.1145/2523616.2525941","DOIUrl":"https://doi.org/10.1145/2523616.2525941","url":null,"abstract":"Explaining the (mis)behavior of virtual machines in large-scale cloud environments presents a number of challenges with respect to both scale and making sense of torrents of datacenter telemetry emanating from multiple levels of the stack. In this paper we leverage VM-similarity to explain the behavior or performance of a VM using its cohort as a reference (or by contrasting it against groups of VMs outside of its cohort). The key insight is that virtual machines (VMs) running the same application (components or workloads), or VMs colocated within the same (logical) tier of a complex application exhibit similar telemetry patterns. The power of similarity relationships stems from the additional context that similarity provides. The quantitative or qualitative \"distance\" between a VM and its expected cohort could be used to explain or diagnose any discrepancy. Similarly, the distance between a VM and one in another cohort can be used to explain why the VMs are dissimilar. As an example we apply our data-mining techniques to debugging ViewPlanner performance. ViewPlanner is a tool used to emulate and evaluate large-scale deployments of virtual desktops. Using a ViewPlanner deployment of 175 VMs we collect ~ 300 metrics-per-VM, sampled at 20-second frequency over multiple 1 hour epochs, from the PerformanceManager [4] on ESX and automatically filter (using entropy measures [2]) and cluster them using K-means [1]. We use the median value of each metric within an epoch to summarize the VM's behavior during that epoch. We introduce spread/diffusion metrics to explain the difference between VMs. Spread metrics are those such that the expected value of the order statistic (in our case the median) of a metric, m, E[m] differs between two clusters, i.e., the expected value is conditioned on the cluster, E[mi|clusterA] ≠ E[mi|clusterB]. Within a cluster of VMs, differences in the distribution of a particular metric, mi, may be explained by conditioning mi on other metrics, {c0, ..., cn}, where E[mi] ≠ E[mi|c0, ..., cn]. We automatically find potentially interesting mi's using Silverman's test [3] for multi-modality and we use Mutual Information [2] to find associated ci's.","PeriodicalId":298547,"journal":{"name":"Proceedings of the 4th annual Symposium on Cloud Computing","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126957666","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}