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Towards elastic transactional cloud storage with range query support
2010
Proceedings of the VLDB Endowment
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We also enhance the system with an effective load balancing scheme using a self-tuning replication technique that is specially designed for large-scale data. ...
In ec-Store, data objects are distributed and replicated in a cluster of commodity computer nodes located in the cloud. ...
C.2 Effect of Self-tuning Range Histogram We now study the effect of self-tuning range histogram in handling access patterns with flash crowd queries. ...
doi:10.14778/1920841.1920907
fatcat:igcu5btk35eino33rsa64dppy4
Estimating Query Result Sizes for Proxy Caching in Scientific Database Federations
2006
ACM/IEEE SC 2006 Conference (SC'06)
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CAROT estimates query result sizes by learning the distribution of query results, not by examining or sampling data, but from observing workload. ...
nature of database federations. ...
CXHist [16] builds workload-aware histograms for selectivity estimation on a broad class of XML string-based queries. ...
doi:10.1109/sc.2006.27
fatcat:yzq67pfs7za3ndj736tqdljapy
Data management and query---Estimating query result sizes for proxy caching in scientific database federations
2006
Proceedings of the 2006 ACM/IEEE conference on Supercomputing - SC '06
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CAROT estimates query result sizes by learning the distribution of query results, not by examining or sampling data, but from observing workload. ...
nature of database federations. ...
The authors thank Amitabh Chaudhary for giving us the idea of the multidimensional example. We thank Xiodan Wang for his help with the SDSS workload. ...
doi:10.1145/1188455.1188562
dblp:conf/sc/MalikBCS06
fatcat:6avilhqbkzh6flkugosxtic57u
Tsunami: A Learned Multi-dimensional Index for Correlated Data and Skewed Workloads
[article]
2020
arXiv
pre-print
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However, the performance of that work suffers in the presence of correlated data and skewed query workloads, both of which are common in real applications. ...
Filtering data based on predicates is one of the most fundamental operations for any modern data warehouse. ...
This research is supported by Google, Intel, and Microsoft as part of the MIT Data Systems and AI Lab (DSAIL) at MIT, NSF IIS 1900933, DARPA Award 16-43-D3M-FP040, and the MIT Air Force Artificial Intelligence ...
arXiv:2006.13282v1
fatcat:hkhmejy4w5dxtogv6gvrw2vabq
Research on Auto-Scaling of Web Applications in Cloud: Survey, Trends and Future Directions
2019
Scalable Computing : Practice and Experience
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Cloud computing emerging environment attracts many applications providers to deploy web applications on cloud data centers. ...
The primary area of attraction is elasticity, which allows to auto-scale the resources on-demand. However, web applications usually have dynamic workload and hard to predict. ...
It is further classified into three categories -Self-Tuning PID controller(SPID) -Self-tuning regulator(STR) -Gain scheduling(GS) The adaptive controller is also used in the literature. ...
doi:10.12694/scpe.v20i2.1537
fatcat:5zdylggvtjdslichn6mpoleese
Auto-scaling Web Applications in Clouds: A Taxonomy and Survey
[article]
2017
arXiv
pre-print
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Web application providers have been migrating their applications to cloud data centers, attracted by the emerging cloud computing paradigm. One of the appealing features of the cloud is elasticity. ...
acquire or release computing resources on-demand, which enables web application providers to automatically scale the resources provisioned to their applications without human intervention under a dynamic workload ...
Yaser Mansouri, Xunyun Liu, Minxian Xu, and Bowen Zhou for their valuable comments and suggestions in improving the quality of the paper. ...
arXiv:1609.09224v6
fatcat:dkk2ftpvpbcnvhcmc6lz2omwa4
Characterization of a Big Data Storage Workload in the Cloud
2019
Proceedings of the 2019 ACM/SPEC International Conference on Performance Engineering - ICPE '19
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Understanding the workloads of such systems facilitates tuning and could foster new designs. ...
of big data specific formats. ...
Projects Vidi Magna-Data and COMMIT/ co-support this work. The work of C. Abad is partially funded by a Google Faculty Research Award. ...
doi:10.1145/3297663.3310302
dblp:conf/wosp/TalluriLAI19
fatcat:qvh6hzr75zcszmsyog7u5cav3m
Advances in Large-Scale RDF Data Management
[chapter]
2014
Lecture Notes in Computer Science
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One of the prime goals of the LOD2 project is improving the performance and scalability of RDF storage solutions so that the increasing amount of Linked Open Data (LOD) can be efficiently managed. ...
Data (LOD). ...
As ontologies usually contain hierarchies, we create a histogram of type property values per CS that is aware of hierarchies. ...
doi:10.1007/978-3-319-09846-3_2
fatcat:e7ndc2gnyjherk6o45jk4kj5fe
A Tile-Based Framework with a Spatial-Aware Feature for Easy Access and Efficient Analysis of Marine Remote Sensing Data
2020
Remote Sensing
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The raw data are displayed and roamed on a virtual globe through the Internet as tiles, enhancing their spatial awareness, that can be intelligently used for visualization result tuning, data storage preloading ...
The SatANA framework is supported by a hybrid database storage ideal for the cloud storage of massive MRS data. ...
Figure 7 . 7 Comparison of rendering results for ocean Secchi Disk depth between (A) spatial-aware histogram equalization and (B) global histogram equalization. ...
doi:10.3390/rs12121932
fatcat:wxsi6iym3nc2dhrprnhozajw2a
Providing Scalable Database Services on the Cloud
[chapter]
2010
Lecture Notes in Computer Science
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In this paper, we present an overview of our current on-going work in developing epiC -an elastic and efficient power-aware data-intensive Cloud system. ...
The storage system and the processing engine are loosely coupled, and have been designed to handle two types of workload simultaneously, namely data-intensive analytical jobs and online transactions (commonly ...
for his valuable comments and the numerous discussions during the course of the implementation of epiC. ...
doi:10.1007/978-3-642-17616-6_1
fatcat:vxojjhaguzbn3bdiphoqnor5pi
Intelligent Similarity Joins for Big Data Integration
2013
2013 10th Web Information System and Application Conference
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We study how to apply diPs for complex query expressions and how the usefulness of diPs varies with the data statistics used to construct diPs and the data distributions. ...
Using a new (slightly larger) statistic, of the queries in the TPC-H, TPC-DS and JoinOrder benchmarks can skip at least of the query input. ...
A large area of related work improves data skipping using workload aware adaptations to data partitioning or indexing [ , , , , , , , , , ] ; they co-locate data that is accessed together or build correlated ...
doi:10.1109/wisa.2013.79
dblp:conf/IEEEwisa/WangNSKY13
fatcat:zpgecqejknhudaygsrergdqk7e
A Survey of Big Data Machine Learning Applications Optimization in Cloud Data Centers and Networks
[article]
2019
arXiv
pre-print
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Moreover, we provide a brief review of data centers topologies, routing protocols, and traffic characteristics, and emphasize the implications of big data on such cloud data centers and their supporting ...
In this survey, we present a summary of the characteristics of various big data programming models and applications and provide a review of cloud computing infrastructures, and related technologies such ...
All data are provided in full in the results section of this paper. ...
arXiv:1910.00731v1
fatcat:kvi3br4iwzg3bi7fifpgyly7m4
A Collective Knowledge workflow for collaborative research into multi-objective autotuning and machine learning techniques
[article]
2018
arXiv
pre-print
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We hope such approach will help teach students how to build upon each others' work to enable efficient and self-optimizing software/hardware/model stack for emerging workloads. ...
As the first practical step, we have implemented customizable compiler autotuning, crowdsourced optimization of diverse workloads across Raspberry Pi 3 devices, reduced the execution time and code size ...
Section 5 presents a snapshot of the latest optimization results from collaborative tuning of GCC flags for numerous shared workloads across Raspberry Pi3 devices. ...
arXiv:1801.08024v1
fatcat:k6ltuu6ihrgundwk2gfoe6kuhm
FlexIO: I/O Middleware for Location-Flexible Scientific Data Analytics
2013
2013 IEEE 27th International Symposium on Parallel and Distributed Processing
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Experimental results demonstrate that FlexIO can support a variety of simulation and analytics workloads at large scale through flexible placement options, efficient data movement, and dynamic deployment ...
of data manipulation functionalities. ...
This work was funded by Scientific Data Management Center, U.S. Department of Energy, and Center for Exascale Simulation of Combustion in Turbulence (ExaCT), U.S. Department of Energy. ...
doi:10.1109/ipdps.2013.46
dblp:conf/ipps/ZhengZESWDNCAKPY13
fatcat:uogj5f6yvfhbtcmvccrqjybhoe
A Framework for supporting DBMS-like indexes in the cloud
2011
Proceedings of the VLDB Endowment
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Each cluster node maintains a subset of the index data. ...
Further, the distribution of indexes is not straight forward, and there is therefore always the question of scalability, in terms of data volume, network size, and number of indexes. ...
This work is part of our cloud-based data management system, named epiC (elastic power-aware data-intensive Cloud) 1 , which is designed to support both analytical and OLTP workloads. ...
doi:10.14778/3402707.3402711
fatcat:gpry7ncnpzacbnzsbgh3dncq5y
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