Is it DSS or OLTP: automatically identifying DBMS workloads

被引:8
|
作者
Elnaffar, Said [1 ]
Martin, Pat [2 ]
Schiefer, Berni [3 ]
Lightstone, Sam [3 ]
机构
[1] UAE Univ, Coll It, Al Ain 17555, U Arab Emirates
[2] Queens Univ, Sch Comp, Kingston, ON K7L 3N6, Canada
[3] IBM Canada, IBM Software Lab, Markham, ON L6G 1C7, Canada
基金
加拿大自然科学与工程研究理事会;
关键词
autonomic systems; autonomic DBMS; data mining; classification; workload characterization; Decision Support System; On-line Transaction Processing; DSS; OLTP; self-managed DBMS; database management systems; performance tuning; applied artificial intelligence;
D O I
10.1007/s10844-006-0036-6
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The type of the workload on a database management system (DBMS) is a key consideration in tuning the system. Allocations for resources such as main memory can be very different depending on whether the workload type is Online Transaction Processing (OLTP) or Decision Support System (DSS). A DBMS also typically experiences changes in the type of workload it handles during its normal processing cycle. Database administrators must therefore recognize the significant shifts of workload type that demand reconfiguring the system in order to maintain acceptable levels of performance. We envision intelligent, autonomic DBMSs that have the capability to manage their own performance by automatically recognizing the workload type and then reconfiguring their resources accordingly. In this paper, we present an approach to automatically identifying a DBMS workload as either OLTP or DSS. Using data mining techniques, we build a classification model based on the most significant workload characteristics that differentiate OLTP from DSS and then use the model to identify any change in the workload type. We construct and compare classifiers built from two different sets of workloads, namely the TPC-C and TPC-H benchmarks and the Browsing and Ordering profiles from the TPC-W benchmark. We demonstrate the feasibility and success of these classifiers with TPC-genemted workloads and with industry-supplied workloads.
引用
收藏
页码:249 / 271
页数:23
相关论文
共 50 条
  • [1] Is it DSS or OLTP: automatically identifying DBMS workloads
    Said Elnaffar
    Pat Martin
    Berni Schiefer
    Sam Lightstone
    [J]. Journal of Intelligent Information Systems, 2008, 30 : 249 - 271
  • [2] System optimization for OLTP workloads
    Kunkel, S
    Armstrong, B
    Vitale, P
    [J]. IEEE MICRO, 1999, 19 (03) : 56 - 64
  • [3] Efficient Partitioner For Distributed OLTP DBMS
    Chen, Wenkang
    [J]. 2018 20TH INTERNATIONAL CONFERENCE ON ADVANCED COMMUNICATION TECHNOLOGY (ICACT), 2018, : 207 - 209
  • [4] OLTPShare: The Case for Sharing in OLTP Workloads
    Rehrmann, Robin
    Binnig, Carsten
    Boehm, Alexander
    Kim, Kihong
    Lehner, Wolfgang
    Rizk, Amr
    [J]. PROCEEDINGS OF THE VLDB ENDOWMENT, 2018, 11 (12): : 1769 - 1780
  • [5] GaccO - A GPU-accelerated OLTP DBMS
    Boeschen, Nils
    Binnig, Carsten
    [J]. PROCEEDINGS OF THE 2022 INTERNATIONAL CONFERENCE ON MANAGEMENT OF DATA (SIGMOD '22), 2022, : 1003 - 1016
  • [6] A Framework for Simulating Combined OLTP and OLAP Workloads
    Bog, Anja
    Domschke, Mathias
    Mueller, Juergen
    Zeier, Alexander
    [J]. 2009 IEEE 16TH INTERNATIONAL CONFERENCE ON INDUSTRIAL ENGINEERING AND ENGINEERING MANAGEMENT, VOLS 1 AND 2, PROCEEDINGS, 2009, : 1675 - 1678
  • [7] Performance Improvement of Database Compression for OLTP Workloads
    Lee, Ki-Hoon
    [J]. IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS, 2014, E97D (04): : 976 - 980
  • [8] Automatic Entity-Grouping for OLTP Workloads
    Liu, Bin
    Tatemura, Junichi
    Po, Oliver
    Hsiung, Wang-Pin
    Haciguemues, Hakan
    [J]. 2014 IEEE 30TH INTERNATIONAL CONFERENCE ON DATA ENGINEERING (ICDE), 2014, : 712 - 723
  • [9] GalOP - Towards a GPU-accelerated OLTP DBMS
    Boeschen, Nils
    Binnig, Carsten
    [J]. 17TH INTERNATIONAL WORKSHOP ON DATA MANAGEMENT ON NEW HARDWARE, DAMON 2021, 2021,
  • [10] OLTP versus DSS/OLAP/Data mining
    Kim, W
    [J]. JOURNAL OF OBJECT-ORIENTED PROGRAMMING, 1997, 10 (07): : 68 - &