FluteDB: An efficient and scalable in-memory time series database for sensor-cloud
Recently, with the widespread use of large-scale sensor network, time series data is vastly generated and requires to be processed. However, those traditional databases show their limitations on storage when handling such a large stream data in cloud, and even their actual reliability and availabili...
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| Vydáno v: | Journal of parallel and distributed computing Ročník 122; s. 95 - 108 |
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| Jazyk: | angličtina |
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Elsevier Inc
01.12.2018
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| ISSN: | 0743-7315, 1096-0848 |
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| Abstract | Recently, with the widespread use of large-scale sensor network, time series data is vastly generated and requires to be processed. However, those traditional databases show their limitations on storage when handling such a large stream data in cloud, and even their actual reliability and availability are also difficult to be guaranteed. To deal with the problem, this paper proposes FluteDB, an efficient and scalable in-memory time series database for sensor-cloud. We adequately analyze the unique characteristics of time series data and its relevant operations to strike the right balance among efficiency, scalability, resources consumption, reliability and availability. Specifically, on basis of the aggregate analysis of root cause for ongoing time series problems, FluteDB targeted optimizes the strategies for key operations in memory and physical storage, at the expense of partial acceptable data precision and consistency. FluteDB’s enhanced strategies are primarily comprised of Triggered Time Series Merge Tree (TTSM Tree), time series enhanced cache management and corresponding compression algorithms for different data types. The validations of all sub-modules have demonstrated that our improved strategies outperform existing methods in real time series environment significantly. Global experimental results also show that the integrated FluteDB reduces query latency by 17x, improves write rate by 98x and saves about 47% storage resources. The average available service time and recovery rate and degree of FluteDB are competitive with the state-of-the-art reliability and availability strategy in real and simulated faults, which demonstrates FluteDB can provide highly stable large-scale data cloud services.
•FluteDB is an efficient and scalable time series database for sensor-cloud.•The index in FluteDB equips flexible storage tricks for time series data.•FluteDB improves efficiency by adjusting disk accesses according to data temperature.•FluteDB optimizes its data encapsulation and fault tolerant strategies. |
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| AbstractList | Recently, with the widespread use of large-scale sensor network, time series data is vastly generated and requires to be processed. However, those traditional databases show their limitations on storage when handling such a large stream data in cloud, and even their actual reliability and availability are also difficult to be guaranteed. To deal with the problem, this paper proposes FluteDB, an efficient and scalable in-memory time series database for sensor-cloud. We adequately analyze the unique characteristics of time series data and its relevant operations to strike the right balance among efficiency, scalability, resources consumption, reliability and availability. Specifically, on basis of the aggregate analysis of root cause for ongoing time series problems, FluteDB targeted optimizes the strategies for key operations in memory and physical storage, at the expense of partial acceptable data precision and consistency. FluteDB’s enhanced strategies are primarily comprised of Triggered Time Series Merge Tree (TTSM Tree), time series enhanced cache management and corresponding compression algorithms for different data types. The validations of all sub-modules have demonstrated that our improved strategies outperform existing methods in real time series environment significantly. Global experimental results also show that the integrated FluteDB reduces query latency by 17x, improves write rate by 98x and saves about 47% storage resources. The average available service time and recovery rate and degree of FluteDB are competitive with the state-of-the-art reliability and availability strategy in real and simulated faults, which demonstrates FluteDB can provide highly stable large-scale data cloud services.
•FluteDB is an efficient and scalable time series database for sensor-cloud.•The index in FluteDB equips flexible storage tricks for time series data.•FluteDB improves efficiency by adjusting disk accesses according to data temperature.•FluteDB optimizes its data encapsulation and fault tolerant strategies. |
| Author | Li, Chen Li, Jianxin Li, Bo Wei, Guanyu Wang, Lihong Si, Jinghui Bhuiyan, Md Zakirul Alam |
| Author_xml | – sequence: 1 givenname: Chen surname: Li fullname: Li, Chen email: lichen@act.buaa.edu.cn organization: Beijing Advanced Innovation Center for Big Data and Brain Computing, Beihang University, Beijing, China – sequence: 2 givenname: Bo surname: Li fullname: Li, Bo email: libo@act.buaa.edu.cn organization: Beijing Advanced Innovation Center for Big Data and Brain Computing, Beihang University, Beijing, China – sequence: 3 givenname: Md Zakirul Alam surname: Bhuiyan fullname: Bhuiyan, Md Zakirul Alam email: mbhuiyan3@fordham.edu organization: Department of Computer and Information Sciences, Fordham University, Bronx, NY, USA – sequence: 4 givenname: Lihong surname: Wang fullname: Wang, Lihong email: wlh@isc.org.cn organization: National Internet Emergency Center, Beijing, China – sequence: 5 givenname: Jinghui surname: Si fullname: Si, Jinghui email: sijh@act.buaa.edu.cn organization: Beijing Advanced Innovation Center for Big Data and Brain Computing, Beihang University, Beijing, China – sequence: 6 givenname: Guanyu surname: Wei fullname: Wei, Guanyu email: weigy@act.buaa.edu.cn organization: Beijing Advanced Innovation Center for Big Data and Brain Computing, Beihang University, Beijing, China – sequence: 7 givenname: Jianxin surname: Li fullname: Li, Jianxin email: lijx@act.buaa.edu.cn organization: Beijing Advanced Innovation Center for Big Data and Brain Computing, Beihang University, Beijing, China |
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| Cites_doi | 10.1109/TPDS.2016.2552174 10.1145/2783258.2783348 10.1007/s00778-005-0171-7 10.1145/146941.146943 10.1109/TNET.2002.803864 10.1007/s11047-015-9536-z 10.1007/s002360050048 10.1109/TR.2013.2270415 10.1145/3035918.3056102 10.1145/954339.954341 10.1145/2213836.2213862 10.1007/978-3-319-72395-2_41 10.1145/568518.568520 10.1109/TIT.1977.1055714 |
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| Title | FluteDB: An efficient and scalable in-memory time series database for sensor-cloud |
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