Proceedings of the Sixth ACM Symposium on Cloud Computing最新文献_第4页

Evaluating the impact of fine-scale burstiness on cloud elasticity 评估精细尺度爆炸对云弹性的影响

Proceedings of the Sixth ACM Symposium on Cloud Computing Pub Date : 2015-08-27 DOI: 10.1145/2806777.2806846

S. Islam, S. Venugopal, Anna Liu

引用次数: 19

FastLane: making short flows shorter with agile drop notification FastLane:通过灵活的掉落通知缩短短流程

Proceedings of the Sixth ACM Symposium on Cloud Computing Pub Date : 2015-08-27 DOI: 10.1145/2806777.2806852

David Zats, A. Iyer, G. Ananthanarayanan, R. Agarwal, R. Katz, I. Stoica, Amin Vahdat

{"title":"FastLane: making short flows shorter with agile drop notification","authors":"David Zats, A. Iyer, G. Ananthanarayanan, R. Agarwal, R. Katz, I. Stoica, Amin Vahdat","doi":"10.1145/2806777.2806852","DOIUrl":"https://doi.org/10.1145/2806777.2806852","url":null,"abstract":"The drive towards richer and more interactive web content places increasingly stringent requirements on datacenter network performance. Applications running atop these networks typically partition an incoming query into multiple subqueries, and generate the final result by aggregating the responses for these subqueries. As a result, a large fraction --- as high as 80% --- of the network flows in such workloads are short and latency-sensitive. The speed with which existing networks respond to packet drops limits their ability to meet high-percentile flow completion time SLOs. Indirect notifications indicating packet drops (e.g., duplicates in an end-to-end acknowledgement sequence) are an important limitation to the agility of response to packet drops. This paper proposes FastLane, an in-network drop notification mechanism. FastLane enhances switches to send high-priority drop notifications to sources, thus informing sources as quickly as possible. Consequently, sources can retransmit packets sooner and throttle transmission rates earlier, thus reducing high-percentile flow completion times. We demonstrate, through simulation and implementation, that FastLane reduces 99.9th percentile completion times of short flows by up to 81%. These benefits come at minimal cost --- safeguards ensure that FastLane consume no more than 1% of bandwidth and 2.5% of buffers.","PeriodicalId":275158,"journal":{"name":"Proceedings of the Sixth ACM Symposium on Cloud Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129109218","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: 25

Centiman: elastic, high performance optimistic concurrency control by watermarking 通过水印实现弹性、高性能、乐观的并发控制

Proceedings of the Sixth ACM Symposium on Cloud Computing Pub Date : 2015-08-27 DOI: 10.1145/2806777.2806837

B. Ding, Lucja Kot, A. Demers, J. Gehrke

引用次数: 28

Interactive data analytics: the new frontier 交互式数据分析:新前沿

Proceedings of the Sixth ACM Symposium on Cloud Computing Pub Date : 2015-08-27 DOI: 10.1145/2806777.2809956

S. Madden

引用次数: 3

Forecasting the cost of processing multi-join queries via hashing for main-memory databases 通过对主存数据库的散列预测处理多连接查询的成本

Proceedings of the Sixth ACM Symposium on Cloud Computing Pub Date : 2015-07-11 DOI: 10.1145/2806777.2806944

Feilong Liu, Spyros Blanas

{"title":"Forecasting the cost of processing multi-join queries via hashing for main-memory databases","authors":"Feilong Liu, Spyros Blanas","doi":"10.1145/2806777.2806944","DOIUrl":"https://doi.org/10.1145/2806777.2806944","url":null,"abstract":"Database management systems (DBMSs) carefully optimize complex multi-join queries to avoid expensive disk I/O. As servers today feature tens or hundreds of gigabytes of RAM, a significant fraction of many analytic databases becomes memory-resident. Even after careful tuning for an in-memory environment, a linear disk I/O model such as the one implemented in PostgreSQL may make query response time predictions that are up to 2× slower than the optimal multi-join query plan over memory-resident data. This paper introduces a memory I/O cost model to identify good evaluation strategies for complex query plans with multiple hash-based equi-joins over memory-resident data. The proposed cost model is carefully validated for accuracy using three different systems, including an Amazon EC2 instance, to control for hardware-specific differences. Prior work in parallel query evaluation has advocated right-deep and bushy trees for multi-join queries due to their greater parallelization and pipelining potential. A surprising finding is that the conventional wisdom from shared-nothing disk-based systems does not directly apply to the modern shared-everything memory hierarchy. As corroborated by our model, the performance gap between the optimal left-deep and right-deep query plan can grow to about 10× as the number of joins in the query increases.","PeriodicalId":275158,"journal":{"name":"Proceedings of the Sixth ACM Symposium on Cloud Computing","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126413671","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: 23

On data skewness, stragglers, and MapReduce progress indicators 关于数据偏度，掉队，MapReduce进度指标

Proceedings of the Sixth ACM Symposium on Cloud Computing Pub Date : 2015-03-31 DOI: 10.1145/2806777.2806843

Emilio Coppa, Irene Finocchi

{"title":"On data skewness, stragglers, and MapReduce progress indicators","authors":"Emilio Coppa, Irene Finocchi","doi":"10.1145/2806777.2806843","DOIUrl":"https://doi.org/10.1145/2806777.2806843","url":null,"abstract":"We tackle the problem of predicting the performance of MapReduce applications designing accurate progress indicators, which keep programmers informed on the percentage of completed computation time during the execution of a job. This is especially important in pay-as-you-go cloud environments, where slow jobs can be aborted in order to avoid excessive costs. Performance predictions can also serve as a building block for several profile-guided optimizations. By assuming that the running time depends linearly on the input size, state-of-the-art techniques can be seriously harmed by data skewness, load unbalancing, and straggling tasks. We thus design a novel profile-guided progress indicator, called NearestFit, that operates without the linear hypothesis assumption in a fully online way (i.e., without resorting to profile data collected from previous executions). NearestFit exploits a careful combination of nearest neighbor regression and statistical curve fitting techniques. Fine-grained profiles required by our theoretical progress model are approximated through space- and time-efficient data streaming algorithms. We implemented NearestFit on top of Hadoop 2.6.0. An extensive empirical assessment over the Amazon EC2 platform on a variety of benchmarks shows that its accuracy is very good, even when competitors incur non-negligible errors and wide prediction fluctuations.","PeriodicalId":275158,"journal":{"name":"Proceedings of the Sixth ACM Symposium on Cloud Computing","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131108807","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: 33