Identifying Workloads in Mixed Applications

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1 , pp Identfyng Workloads n Mxed Applcatons Jeong Seok Oh, Hyo Jung Bang, Yong Do Cho, Insttute of Gas Safety R&D, Korea Gas Safety Corporaton, Shghung-Sh, Gyoungg-Do, Korea {dbstar, bhj, Abstract. Database admnstrators should be aware of workload characterstcs for managng database systems. Workload characterstcs can be dfferent dependng on database applcaton. In partcular, dentfyng workloads n mxed database applcatons mght be qute dffcult. Therefore, a method s necessary for dentfyng workloads n the mxed database applcaton. Ths paper ams to dentfy workloads n the mxed database applcaton usng data mnng technologes. To construct the mxed database applcaton, we use the TPC-C and TPC-W benchmark. We dscrmnate between tran workloads (workloads usng the TPC-C or TPC-W benchmarks) and test workloads (mxed workloads of both benchmarks), and modfy the algorthm of k-nn (Nearest Neghbor) classfer n order to satsfy our objectves. The modfed k-nn algorthm measures how close the test workloads are to the tran workloads. The modfed k-nn algorthm s better than others for dentfyng workloads because ts results are lower than others n the oscllaton dependng on the k parameter and the error rate. Ths research contrbutes towards consderng flexble tunng methods usng workload dentfcaton nformaton Keywords: Database Workloads. Classfer algorthm. Introducton Database admnstrators should be aware of workload characterstcs for managng DBMSs. Database workloads can show dfferent workload characterstcs dependng on database applcaton. As database applcatons become more complex and varous, database admnstrators have more dffculty analyzng and dentfyng workloads. There are many publshed studes on database workloads rangng from workload characterstc analyss to workload classfcaton. As most studes have been researched n a smple database applcaton such as OLTP, DSS or Web-commerce, workloads of the mxed database applcaton are consderably nsuffcent [9]. The DBMS performance mght be degraded by usng the tunng method of a smple database applcaton n a mxed database applcaton. Some researches tred to solve ths ssue by classfyng workload type usng decson tree. Although ths study presented the necessty of workload dentfcaton, the methodology for dentfyng workloads s not consdered suffcently [4, 5]. ISSN: ASTL Copyrght 203 SERSC

2 Ths paper ams to dentfy workloads through comparng workloads of the mxed database applcaton wth workloads of the smple database applcaton usng modfed classfer algorthm. To mx workloads, we use the TPC-C and TPC-W benchmark [0, ]. Workload data are represented by 4 performance ndcators, and are classfed nto tran workload data and test workload data. Tran workload data are collected by performng each benchmark. Test workload data are constructed by dong both benchmarks at the same tme. We use k-nn classfer whch consders all data attrbutes. Furthermore, we modfy the classfer algorthm because prevous algorthms for classfcaton have lmts n order to dentfy workloads. The modfed classfer algorthm measures how close the test workloads are to the tran workloads. To compare the modfed classfer algorthm wth prevous algorthms, we test each algorthm s ablty at dentfyng workloads. The modfed classfer algorthm s better than other algorthms at dentfyng workloads because ts results are lower than others n the oscllaton dependng on the k parameter and the error rate. Our research contrbutes towards consderng flexble tunng method usng workload dentfcaton nformaton 2 The Modfed Classfer Algorthm In general, prevous k-nn algorthms have two lmts for dentfyng workloads. Frst, for the classfcaton, these algorthms assgn the class by force. Although the class membershp of the test data to neghbors s gven as the probablty, test data can belong to a partcular class set wth hgher class membershp by force. In other words, the class of test data s assgned by force f a certan class membershp value s objectvely rather low and s hgher other class membershp. The other lmt of these algorthms mght be the oscllaton of results dependng on the changng k parameter. The results of these algorthms can dffer dependng on the number of k and components of neghbors because only the relatonshp among neghbors s consdered n the phase of the ntal membershp. The nstablty of the results can degrade the accuracy and may cause confuson to the user. We keep the class membershp nformaton because of the forcble class assgnment. For nstance, suppose that n c matrx exsts, where n s the number of data, and c s the number of class. Furthermore, suppose the matrx for prevous k-nn classfer algorthm s seen as Fg.. Fg.. The method n prevous classfer Although the rate pertanng to Class s 80% and the rate to Class 2 s 20% n the frst data, ths data s assgned compulsorly to Class. The same stuaton can 396 Copyrght 203 SERSC

3 occur for the remanng data. Therefore, 60% of all data belongs to Class, and 40% of all data belongs to Class 2. Fg. 2. The method n the modfed classfer In the modfed classfer algorthm, the class membershp of test data does not belong to a certan class, and we keep t n order to avod the forcble class assgnment. Suppose that n c matrx exsts, where n s the number of data, and c s the number of class. Fg. 2 shows the matrx for our classfer algorthm. It can be concluded that 58% of all data s smlar to Class, and 42% of all data s smlar to Class 2. We consder the relatonshp between the test data and the centrod of class set n the ntal membershp n order to reduce the oscllaton of results dependng on the changng k parameter. The ntal membershp of our classfer algorthm s shown n Equaton. The ntal membershp s calculated by consderng f and when the class of the tran data s equal to the assumed class n the fnal membershp. c s the number of class, represents the class of the current tran data, and j represents the assumed class n the phase of the fnal ntal membershp ( ) 0.49, f j = 2 c x c m k = x ck ( y) = ( ) 0.49, f j 2 c x c m k = x ck µ () The fnal membershp s shown n Equaton 2, and s the same as the fnal membershp n [7]. The fnal membershp represents how test data x s smlar to class. k s the number of neghbors, j means the assumed class. µ ( x) j k = = k µ ( y)(/ x y = (/ x y 2 /( m ) 2 /( m ) ) ) (2) Copyrght 203 SERSC 397

4 3 Experment for Identfyng Mxed-workloads To test workload dentfcaton, we need to tran workload data and test workload data. All workload data conssts of 4 performance ndcators and a class. The 4 performance ndcators show the followng [3]: the data buffer ht rato; the shared memory ht rato; the system catalog ht rato; the latch contenton rato; the memory sort rato; the memory parsng rato; the data varance rato; the data buffer reads; the non-data buffer reads; the data buffer wrtes; the non-data buffer wrtes; the dsk wrtes wth checkponts; the dsk wrtes wthout checkponts; and the redo sze. The tran workload data are collected by performng the TPC-C and TPC-W benchmarks, respectvely. The class of tran workload data s TPC-C or TPC-W. The test workload data are mxed by executng the resource manager and adjustng the number of processes when both benchmarks are performng at the same tme. The resource manager that s offered by Oracle DBMS can dstrbute avalable processng resources by allocatng percentages of CUP tme to dfferent users and applcatons. Because the resource manager depends on the number of processes, we controlled the number of warehouses n the TPC-C and the number of EBs n the TPC-W for adjustng percentages of CUP tme. The class of test workload data s?. We set the mxed CPU rate at 80% and 20%, 50% and 50%, and 20% and 80% n the TPC-C and TPC-W, respectvely. The test workloads are collected fve tmes per the mxed CPU rate. Workload dentfcaton tests are executed 600 tmes by changng the k parameter from to 0 and by usng four classfer algorthms (the k-nn, the fuzzy I [6], the fuzzy II [8], the modfed classfer). The results of tests are analyzed by consderng the oscllaton of results dependng on the changng k parameter and the error rate. The oscllaton of results, dependng on the changng k parameter, s analyzed by the average standard devaton whch averages the standard devaton of the dentfcaton rate n fve workload sets wth the same mxed rate. The lower the standard devaton, the fewer the oscllaton of results. Table shows the average standard devaton n four classfer algorthms. Our fuzzy classfer algorthm s lower than others n all mxed rates. Table : The average standard devaton Algorthm 80%-20% 50%-50% 20%-80% k-nn Fuzzy I Fuzzy II Our The error rate represents the accuracy of the algorthm, and s analyzed by the average error rate that averages the dfference between the mxed rate and the dentfcaton rate n the fve workload sets wth the same mxed rate. Table 2 shows the average error rate n four k-nn algorthms. Our error rate of fuzzy classfer algorthm s also lower than others n all mxed rates. 398 Copyrght 203 SERSC

5 Table 2 : The average error rate Algorthm 80%-20% 50%-50% 20%-80% k-nn 2.5% 2.62% 7.08% Fuzzy I 5.3% 2.53% 6.% Fuzzy II.08% 28.8% 5.2% Our 2.94% 2.45%.69% 4 Conclusons As database applcatons become more complex and varous, analyzng workloads mght be dffcult. Furthermore, dentfyng workloads mght be more dffcult because of the mxed database applcatons. To manage database systems effectvely, the method for dentfyng workloads n the mxed database applcaton s necessary. Ths paper carred out dentfyng the mxed workloads between TPC-C and TPC- W benchmarks wth the modfed k-nn algorthm. Frst, we collected tran workload data and test workload data. The tran workload data are collected by performng each benchmark. The test workload data are mxed by performng both benchmarks at the same tme. The mxed rate of TPC-C and TPC workloads s 80% and 20%, 50% and 50%, and 20% and 80%. Next, we modfed the fuzzy k-nn algorthm n order to dentfy sutable workloads. Our algorthm kept the class membershp nformaton and dffered from processng the ntal membershp n order to reduce the oscllaton of dentfed results dependng on the changng k parameter and to mprove the accuracy of dentfed results. Fnally, we tested the ablty of four k-nn algorthms (the k-nn, the fuzzy k-nn I, the fuzzy k-nn II, our fuzzy k-nn), respectvely. We consdered the ablty of the algorthm as the low oscllaton and the low error rate. As a result, the modfed k- NN algorthm s more sutable than others for dentfyng workloads because t represents the lowest oscllaton and error rate. The result of ths research can ad the tunng method for the mxed database applcaton. For nstance, dentfed workload nformaton and tunng method of a smple workload type can be consdered together. For future work, we wll study database tunng methods wth dentfed workload nformaton References. R. Bayls, Database Admnstrator s Gude: Release 2 (9.2), Oracle Corporaton, T. M. Cover and P. E. Hart, Nearest Neghbor Pattern Classfcaton, IEEE Transactons on Informaton Theory, Vol. 3, No., pp 2-27, M. Cyran, Oracle 9: Database Performance Gude and Reference, Release 2(9.2), Oracle Corporaton, 200. Copyrght 203 SERSC 399

6 4. S. Elnaffar, A Methodology for Auto-Recognzng DBMS Workloads, Proceedngs of CASCON Conference, Toronto, Canada, S. Elnaffar, P. Martn, and R. Horman, Automatcally Classfyng Database Workloads, Proceedngs of th CKIM Conference, pp , McLean, USA, J. H. Han and Y. K. Km, A Fuzzy k-nn Algorthm usng Weghts from the Varance of Membershp Values, Proceedngs of IEEE CVPR Conference, pp , Fort Collns, USA, J. Han and M. Kamber, Data Mnng Concepts and Technques, Morgan Kaufmann Publshers, J. M. Keller, M. R. Gray, and J. A. Gvens, A Fuzzy k-nearest Neghbor Algorthms, IEEE Transacton on System, Man and Cybernetcs, Vol. 5, No. 4, pp , P. Martn, W. Powley, H. Y. L, and K. Romanufa, Managng Database Server Performance to Meet QoS Requrements n Electronc Commerce Systems, Internatonal Journal on Dgtal Lbrares, Vol. 3, No. 4, pp , TPC Benchmark C Specfcaton (Revson 5.0), 200, TPC Benchmark W (Web Commerce) Specfcaton (verson.8), 2002, 2. J. S. Oh and S. H. Lee, Database Workload Analyss: emprcal study, Journal of KISS, Vol. -D, No. 4, pp , Copyrght 203 SERSC

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