Pub Date : 2020-10-01DOI: 10.1109/IISWC50251.2020.00013
Jinhong Li, Qiuping Wang, P. Lee, Chao Shi
Cloud block storage systems support diverse types of applications in modern cloud services. Characterizing their I/O activities is critical for guiding better system designs and optimizations. In this paper, we present an in-depth analysis of production cloud block storage workloads through the block-level I/O traces of billions of I/O requests collected from Alibaba Cloud. We study the characteristics of load intensity, spatial patterns, and temporal patterns. Also, we present a comparative study on our traces and the notable public block-level I/O traces from Microsoft Research Cambridge, and identify the commonalities and differences of the two sets of traces. Finally, we provide 15 findings and discuss their implications on load balancing, cache efficiency, and storage cluster management in a cloud block storage system. Our traces are now released for public use.
{"title":"An In-Depth Analysis of Cloud Block Storage Workloads in Large-Scale Production","authors":"Jinhong Li, Qiuping Wang, P. Lee, Chao Shi","doi":"10.1109/IISWC50251.2020.00013","DOIUrl":"https://doi.org/10.1109/IISWC50251.2020.00013","url":null,"abstract":"Cloud block storage systems support diverse types of applications in modern cloud services. Characterizing their I/O activities is critical for guiding better system designs and optimizations. In this paper, we present an in-depth analysis of production cloud block storage workloads through the block-level I/O traces of billions of I/O requests collected from Alibaba Cloud. We study the characteristics of load intensity, spatial patterns, and temporal patterns. Also, we present a comparative study on our traces and the notable public block-level I/O traces from Microsoft Research Cambridge, and identify the commonalities and differences of the two sets of traces. Finally, we provide 15 findings and discuss their implications on load balancing, cache efficiency, and storage cluster management in a cloud block storage system. Our traces are now released for public use.","PeriodicalId":365983,"journal":{"name":"2020 IEEE International Symposium on Workload Characterization (IISWC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121036626","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}
Pub Date : 2020-10-01DOI: 10.1109/IISWC50251.2020.00029
A. Azad, M. Aznaveh, S. Beamer, Mark P. Blanco, Jinhao Chen, Luke D'Alessandro, Roshan Dathathri, Tim Davis, Kevin Deweese, J. Firoz, H. Gabb, G. Gill, Bálint Hegyi, Scott P. Kolodziej, Tze Meng Low, A. Lumsdaine, Tugsbayasgalan Manlaibaatar, T. Mattson, Scott McMillan, R. Peri, K. Pingali, Upasana Sridhar, Gábor Szárnyas, Yunming Zhang, Yongzhe Zhang
Graphs play a key role in data analytics. Graphs and the software systems used to work with them are highly diverse. Algorithms interact with hardware in different ways and which graph solution works best on a given platform changes with the structure of the graph. This makes it difficult to decide which graph programming framework is the best for a given situation. In this paper, we try to make sense of this diverse landscape. We evaluate five different frameworks for graph analytics: SuiteS-parse GraphBLAS, Galois, the NWGraph library, the Graph Kernel Collection, and GraphIt. We use the GAP Benchmark Suite to evaluate each framework. GAP consists of 30 tests: six graph algorithms (breadth-first search, single-source shortest path, PageRank, betweenness centrality, connected components, and triangle counting) on five graphs. The GAP Benchmark Suite includes high-performance reference implementations to provide a performance baseline for comparison. Our results show the relative strengths of each framework, but also serve as a case study for the challenges of establishing objective measures for comparing graph frameworks.
{"title":"Evaluation of Graph Analytics Frameworks Using the GAP Benchmark Suite","authors":"A. Azad, M. Aznaveh, S. Beamer, Mark P. Blanco, Jinhao Chen, Luke D'Alessandro, Roshan Dathathri, Tim Davis, Kevin Deweese, J. Firoz, H. Gabb, G. Gill, Bálint Hegyi, Scott P. Kolodziej, Tze Meng Low, A. Lumsdaine, Tugsbayasgalan Manlaibaatar, T. Mattson, Scott McMillan, R. Peri, K. Pingali, Upasana Sridhar, Gábor Szárnyas, Yunming Zhang, Yongzhe Zhang","doi":"10.1109/IISWC50251.2020.00029","DOIUrl":"https://doi.org/10.1109/IISWC50251.2020.00029","url":null,"abstract":"Graphs play a key role in data analytics. Graphs and the software systems used to work with them are highly diverse. Algorithms interact with hardware in different ways and which graph solution works best on a given platform changes with the structure of the graph. This makes it difficult to decide which graph programming framework is the best for a given situation. In this paper, we try to make sense of this diverse landscape. We evaluate five different frameworks for graph analytics: SuiteS-parse GraphBLAS, Galois, the NWGraph library, the Graph Kernel Collection, and GraphIt. We use the GAP Benchmark Suite to evaluate each framework. GAP consists of 30 tests: six graph algorithms (breadth-first search, single-source shortest path, PageRank, betweenness centrality, connected components, and triangle counting) on five graphs. The GAP Benchmark Suite includes high-performance reference implementations to provide a performance baseline for comparison. Our results show the relative strengths of each framework, but also serve as a case study for the challenges of establishing objective measures for comparing graph frameworks.","PeriodicalId":365983,"journal":{"name":"2020 IEEE International Symposium on Workload Characterization (IISWC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127026675","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}
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