Replicated Static Allocation of Fragments in Distributed Database Design using Biogeographybased

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1 Proc. of Int. Conf. on Advances n Communcaton, Network, and Computng, CNC Replcated Statc Allocaton of Fragments n Dstrbuted Database Desgn usng Bogeographybased Optmzaton Arjan Sngh 1, Karanjeet Sngh Kahlon 2 and Rajnder Sngh Vrk 2 1 Punjab Unversty, Patala, Punjab, Inda. Emal: 2 Guru Nanak Dev Unversty, Amrtsar, Punjab, Inda Emal: Abstract Allocaton of data s one of the key desgn ssues of dstrbuted database. A major cost of query executon n a dstrbuted database system s the data transfer cost from one ste to another ste. The allocaton of fragments among the dfferent stes over the network plays an mportant role n performance of the dstrbuted database system. The man objectve of a data allocaton n dstrbuted database s to place the data fragments at dfferent stes n such a way, so that the total data transfer cost can be mnmzed whle executng a set of queres. As data allocaton n dstrbuted database s NP-hard problem and therefore optmal solutons cannot be found n reasonable amount of tme even for moderate sze problems. Therefore t requres meta-heurstcs algorthms to generate good soluton n quck tme. In ths paper, a new algorthm has been proposed for replcated fragment allocaton durng dstrbuted database desgn for statc envronment usng bogeography-based optmzaton (BBO). The man objectve of ths paper s to desgn a replcated fragments allocaton algorthm to mnmze the total data transmsson cost and storage cost of fragments. To show the performance of proposed algorthm, results of bogeography-based optmzaton algorthm for data allocaton are compared wth genetc algorthm. Index Terms Dstrbuted Databases, Replcated Allocaton, Statc Data Allocaton, Bogeography-Based Optmzaton (BBO), Genetc Algorthm (GA). I. INTRODUCTION Dstrbuted database technology s one of the most mportant developments of the past two decades n the feld of database systems. Dstrbuted database system technology s the merger of two separate branches of computer scence: database system and computer network [15]. Dstrbuted database technology has become an ntegral part of most of the busness organzaton due to ts decentralzed nature. Dstrbuted databases have elmnated many of the shortcomngs of the centralzed databases and ft more naturally n the decentralzed structures of many organzatons [7]. Dstrbuted database can be defned as a collecton of logcally nterrelated data dstrbuted over the stes of a computer network [15]. Dstrbuted database system has many advantages over centralzed database system [7,15]: Elsever, 2014

2 Reduced communcaton overhead Improved performance Relablty Avalablty Expandablty The desgn of centralzed database has two man ssues: desgnng the conceptual schema and desgnng the physcal database. But the desgn of dstrbuted databases adds two more ssues: desgnng the fragmentaton of global relatons and allocaton of fragments over network [7]. All these ssues complcate the desgn of dstrbuted database. The problem of fragmentng the database s dffcult one n tself and number of dfferent technques has been proposed for fragmentng the database by dfferent research. Ths study concentrates only on data/fragments allocaton problem. Fragment allocaton can further be dvded nto two dfferent categores: replcated/redundant and nonreplcated/non-redundant [7,15]. In a non-replcated/non-redundant allocaton exactly one copy of each fragment wll exst across all the stes, whle under a replcated/redundant allocaton, fragments are replcated over multple stes. Data n dstrbuted database system s allocated accordng to two dfferent types of access patterns: statc and dynamc [15]. In a statc envronment, the access probabltes of applcaton runnng on dfferent ste to fragments never change but n a dynamc envronment these probabltes change over tme. In ths paper, a new algorthm s ntroduced for replcated statc allocaton of fragments durng dstrbuted database desgn process usng bogeography-based optmzaton technque. To show the performance of the proposed algorthm, results are compared wth the genetc algorthm [2,9]. The new proposed algorthm s gvng qualty solutons wthn a shorter perod of tme. The rest of the paper s organzed as follows: Secton II provdes an overvew of the related work. Secton III descrbes the model for replcated data allocaton n statc dstrbuted database envronment. Secton IV contans the proposed new algorthm for replcated allocaton of data usng bogeography-based optmzaton. Secton V contans the results of the comparson of proposed new algorthm wth genetc algorthm based allocaton. Fnally, Secton VI summarzes the contrbuton of the study and pont out the future drecton of research. II. RELATED WORK Chu [8] was frst to develop a model to mnmze overall operatng costs under the constrants of response tme and storage capacty wth fxed number of copes each fle. Casey [5] further nvestgates the Chu s allocaton model and relaxes the assumpton of fxed number of copes. Casey [5] has gven stress on the dfference between updates and retreval. Eswaran [10] proved that Casey s formulaton was NP-Complete, so fndng optmal soluton s not computatonally feasble. Cer et al. [6] consdered the problem of fle allocaton for typcal dstrbuted database applcatons wth a smple model of transacton executon. Cer et al. [6] proposed a non-replcated allocaton of data and suggested that once the optmal non-replcated soluton has been found for non-replcated envronment then replcaton can be handled easly by applyng a greedy algorthm. Apers [3] proved that the fragment allocaton problem n dstrbuted database s all together dfferent from the fle allocaton problem. He also has shown that the data allocaton n dstrbuted database s NP-hard. Apers proposed a method for data allocaton so that total data transfer cost durng the executon of a set of transacton can be mnmzed. Sarathy et al. [16] have gven nonlnear nteger programmng formulaton for fragments allocaton. Tamhankar and Ram [19] had gven an ntegrated method of fragmentaton and allocaton together. Corcoran and Hale [9], and March and S. Rho [14] presented a genetc algorthm-based approach to allocate operatons to nodes. Ahmad et al. [2] compared genetc algorthm, a smulated evoluton algorthm, mean feld annealng algorthm and neghbourhood search algorthm for data allocaton n dstrbuted database desgn. Ahmad et al. [2] showed that when effcency and soluton qualty are equally mportant then genetc algorthm s an attractve soluton. Hababeh et al. [11] has gven a hgh-performance computng method for data allocaton n dstrbuted database system usng cluster based approach for network stes. In all of the above approaches, data allocaton has been proposed based on the statc data access patterns. Brunstroml et al. [4] proposed an optmzaton algorthm for non-replcated dynamc allocaton of fragments n dstrbuted database systems. T. Ulus and M. Uysal [20], Sngh and Kahlon [18] and Abdallaha et al. [1] have gven threshold algorthm, TTC algorthm and POE algorthm respectvely to further mprove the performance of optmzaton algorthm gven by Brunstroml et al. [4]. 463

3 III. THE DATA ALLOCATION MODEL A. The Data Allocaton Problem Assume a dstrbuted database system consstng of stes S = {S 1, S 2,.,S n } on whch a set of queres Q = {q 1,q 2,.,q q } s runnng. Each ste has ts own processng power, memory, and local database system and all the stes are connected by a communcaton lnk network. Let F = {F 1, F 2,.,F m } be the set of fragments after parttonng all global relatons durng fragmentaton phase of dstrbuted database desgn. The allocaton problem nvolves fndng the optmal placement of the fragments (F) to the stes (S). The optmalty can be defned wth respect to two measures mnmal cost and performance [15]. B. The Cost Model Table I. gves the descrpton of varous notatons used to draw the cost model of data allocaton. TABLE I. DESCRIPTION OF VARIOUS NOTATIONS Symbol S n S Q q q j F m F k FR j RF R UF U CC,' Sze(F k) USC RC UC SC Meanng The set of all Stes The number of stes n the network The th ste The set of all queres The number of dstrbuted database queres The j th query The set of all fragments The number of data fragments n the database system The k th fragment The executon frequency of the j th query at th ste The retreval frequency to the k th fragment by j th query The average percentage of k th fragment needed for retreval by j th query The update frequency to the k th fragment by j th query The average percentage of k th fragment needed to be updated by j th query The communcaton cost to transfer unt data from ste S to ste S ' Sze of the k th fragment The cost of storng unt data at ste S Retreval Cost Update Cost Storage Cost The cost of fragment allocaton ncludes can be dvded nto two types: Total data transmsson Cost and Storage cost [15]. Cost of Fragment Allocaton = Total Data Transmsson Cost (TC) + Storage Cost (SC) (1) Total data transmsson cost can further be dvded nto two parts. Frst part s the cost of retreval of fragments to process a query and the second part s the cost to update fragments to process a query [15]. The formula to calculate the total cost of processng a query s gven below: Total Data Transmsson Cost (TC) = Retreval Cost (RC) + Update Cost (UC) (2) The data transmsson overhead for retreval and update are dfferent from each others. In case of retreval, the communcaton s take place between the ste orgnatng the query and the ste havng the desred data fragment wth mnmum transmsson cost from the orgnatng ste. But n case of update queres, t s necessary to update the fragment at all the stes where replcas exsts so that consstency of the data can be preserved. RC UC n q m R FR j RF * mn( CC o ( ), ) * * Sze ( Fk ) 1 j 1 k n q m U FR j UF * ( CC o ( ), ) * * Sze ( Fk ) 1 j 1 k 1 S S 100 * (3) * (4) 464

4 CC o ), Where ( s the communcaton cost assocated between the ste (S o() ) orgnatng the query and the ste (S ) contanng the fragment F k. CC 0 o( ). S S Fk F o ( ), SC USC * Sze F (5) k C. The Cost Functon The objectve of fragments allocaton problem s to mnmze the total cost of data allocaton. The cost functon that has to be mnmzed s gven below: mn n j 1 j 1 k 1 n q q * FR j * UF 1 j 1 k 1 S S S S F F FR k * m USC m RF * mn( CC * Sze ( F k ) o ( ), ( CC R ) * 100 o ( ), * Sze ( F U ) * * Sze 100 ) ( Fk ) k (6) IV. THE PROPOSED ALGORITHM FOR FRAGMENTS ALLOCATION A. Bogeography-Based Optmzaton The bogeography-based optmzaton (BBO) s a newly developed populaton-based evolutonary technque. Bogeography-based optmzaton (BBO) s based on theory of bogeography. Bogeography s the study of geographcal dstrbuton of speces. Smon [17] developed the bogeography-based optmzaton (BBO). BBO s prmarly based on The Theory of Island Bogeography gven by MacArthur and Wlson [13]. MacArthur and Wlson [13] have gven a mathematcal model of bogeography. The mathematcal model descrbes that the rate of change n the number of speces on an sland hghly depends on the stablty between the mmgraton of new speces onto the sland and the emgraton of establshed speces [13]. The BBO algorthm works on a populaton called habtats (or slands). Each habtat represents a possble soluton to the problem n hand. Each soluton feature of a habtat s called a sutablty ndex varable (SIV) of that habtat. The ftness of each habtat s represented by ts habtat sutablty ndex (HSI). HSI s a metrc that determnes the goodness of a canddate soluton. Habtats wth a hgh HSI tend to have a large number of speces, whle those wth a low HSI have a small number of speces. Habtats wth a hgh HSI have many speces that emgrate to nearby habtats. Habtats wth a hgh HSI have a low speces mmgraton rate (λ) and a hgh speces emgraton rate (µ). Habtats wth a low HSI have a hgh speces mmgraton rate (λ) because of ther thn populatons. Ths mmgraton of new speces to low HSI habtats may rase the HSI of the habtat, because the sutablty of a habtat s proportonal to ts bologcal dversty. However f a habtat s HSI remans low, then the speces that resde there wll tend to be vanshed. Ths wll further open the way for addtonal mmgraton. So the low HSI habtats are more dynamc n ther speces dstrbuton than hgh HSI habtats. Fg. 1 shows the relatonshps between ftness of habtats (number of speces), mmgraton rate (λ) and emgraton rate (µ) [17]. The mmgraton rate (λ) and emgraton rate (µ) are functons of the number of speces n the habtat. They can be calculated as follows [17]: k k I1 (7) n 465

5 Fgure 1. Speces Model of a Sngle Habtat [13] k k E (8) n Where, I s the maxmum possble mmgraton rate; E s the maxmum possble emgraton rate; K s the number of speces of the k th ndvdual and n s the maxmum number of speces. B. BBO Algorthm In bogeography-based optmzaton, there are two operators: mgraton and mutaton [17]. A populaton of canddate soluton can be represented by dfferent desgn varables. Each desgn varable for a partcular populaton member s consdered as sutablty ndex (SIV). Mgraton s a probablstc operator that mproves the qualty of a habtat. The mmgraton and emgraton of each soluton are used to probablstcally share the nformaton between habtats. For each habtat H, ts mmgraton rate λ s used to probablstcally make a decson whether to mmgrate or not. If mmgraton s selected, then the emgratng habtat H e s selected probablstcally based on the emgraton rate µ e. Mgraton s represented as [17]: H (SIV) H e (SIV) Mutaton s a probablstc operator that randomly modfes a habtat s SIV. A randomly generated SIV replaces a selected SIV n the soluton H accordng to a mutaton probablty, whch s predefned. The man reason of mutaton s to ncrease dversty of the populaton. Mutaton s useful for both poor soluton and good soluton. For low HSI solutons, mutaton gves them an opportunty of enhancng the qualty of solutons, and for hgh HSI solutons, mutaton s capable to get better them [17]. The bogeography-based optmzaton algorthm s gven below [17,12]: Step1: Intalze Populaton Sze, Maxmum Number of Iteratons (NI), Maxmum Immgraton rate (I), Maxmum Emgraton rate (E), Mutaton rate, and Eltsm Parameter; Step2: Generate a random set of habtats based on the sze of the populaton. Each habtat corresponds to a potental soluton to the gven problem; Step3: Evaluate habtats and compute correspondng HSI value of each habtat; Step4: For =1 to NI Step5: Calculate the mmgraton rate (λ) and emgraton rate (µ) for each habtat accordng to HSI of each habtat; /* Start of Mgraton */ Step6: Select non-elte habtat H wth probablty λ for mmgraton; Step7: f H s selected then select H j wth probablty µ j for emgraton; Step8: f H j s selected then randomly select a SIV from H j ; Step9: H (SIV) H j (SIV); Step10: End f Step11: End f 466

6 /* End of Mgraton */ /* Start of Mutaton */ Step12: Select an SIV n H wth probablty based on the mutaton rate; Step13: f H (SIV) s selected then replace H (SIV) wth a randomly generated SIV; Step14: End f /* End of Mutaton */ Step15: Re-evaluate habtats and compute correspondng HSI value of each habtat; Step16: End for C. Encodng of Habtat In the proposed BBO algorthm for data allocaton, the allocaton of each fragment to dfferent stes over the communcaton network s encoded n a bnary representaton. Numbers of bt used to represent a SIV s the total numbers of stes n the communcaton network. For example n case of 5 stes, f a fragment s assgned to ste 2 and ste 4 then the value of related SIV wll be and f the fragment s assgned to ste 1, ste 3 and ste 4 then the value of related SIV wll be The assgnment values of all the data fragments are concatenated to form a bnary strng. Each bnary strng represents a potental soluton (habtat) to the data allocaton problem. Habtat sutablty ndex (HIS) s the total cost of data fragments allocaton that has to be mnmzed. V. RESULTS Dfferent set of experments are conducted wth number of fragments rangng from 4 to 10 and number of stes fxed 4, 5 and 6 to check the performance of the BBO algorthm. All the experments are done on 2.8 GHz Intel Core 5 processor wth 4 GB RAM and 64 bt Mcrosoft Wndows 7 as an operatng system. BBO and GA algorthms are mplemented n the MATLAB 2010 programmng envronment. The communcaton network topology, communcaton cost between stes, the sze of fragments, numbers of queres, executon frequency of each query at dfferent ste, retreval frequency of dfferent fragments and update frequency of dfferent fragments are randomly generated from unform dstrbutons for each experment [2,16]. Both algorthms are tested on the same data set for each experment and the values of other parameters are gven below: Maxmum Number of Iteratons = 500 Populaton sze = 20 Mutaton Rate = 0.15 Maxmum Immgraton rate (I) = 1 Maxmum Emgraton rate (E) = 1 Eltsm Parameter = 2 Table II and Table III summarze the expermental results obtaned from BBO and GA for 4 stes and number of fragments rangng from 4 to 10. Table IV and Table V show the expermental results obtaned from BBO and GA for 5 stes and number of fragments rangng from 4 to 10. Table VI and Table VII show the expermental results obtaned from BBO and GA for 6 stes and number of fragments rangng from 4 to 10. All these results are obtaned after runnng both the algorthms 20 tmes ndependently for each experment. TABLE II. MINIMUM AND AVERAGE COST ACHIEVED BY GA AND BBO FOR 4 SITES Mnmum Cost Average Cost Mnmum Cost Average Cost e e e e e e e e e e e e e e e e e e e e e e e e e e e e+6 467

7 (a) (b) Fgure 2. Convergence of GA and BBO for 4 Fragments and 4 Stes TABLE III. MINIMUM AND AVERAGE RUNNING TIME OF GA AND BBO FOR 4 SITES Mnmum Tme Average Tme Mnmum Tme Average Tme Fgure 3. Mnmum and Average Runnng Tme of GA and BBO for 4 Stes 468

8 TABLE IV. MINIMUM AND AVERAGE COST ACHIEVED BY GA AND BBO FOR 5 SITES Mnmum Cost Average Cost Mnmum Cost Average Cost e e e e e e e e e e e e e e e e e e e e e e e e e e e e+6 Fgure 4. Convergence of GA and BBO for 4 Fragments and 5 Stes TABLE V. MINIMUM AND AVERAGE RUNNING TIME OF GA AND BBO FOR 5 SITES Mnmum Tme Average Tme Mnmum Tme Average Tme From Table II, Table IV and Table VI, t s clearly evdent that the mnmum cost of allocaton acheved by proposed BBO algorthm for allcoaton of data fragments s less then the mnmum cost acheved by GA. In all the experments, the proposed BBO algorthm s provdng qualty solutons then that of GA. From Table III, Fg. 3, Table V, Fg. 5, Table VII and Fg. 7, t s also clear that BBO algorthm for fragment allocaton s faster than GA based fragment allocaton. But the average cost of fragment allocaton for BBO algorthm s more than GA n some cases as shown n Table II, Table IV and Table VI. Fg. 2, Fg. 4 and Fg. 6 show the convergence of GA and BBO. In most of the cases convergence rate of BBO s fast as compare to GA. In overall the proposed BBO algorthm for fragment allocaton s provdng qualty solutons n less tme. 469

9 (a) (b) Fgure 5. Mnmum and Average Runnng Tme of GA and BBO for 5 Stes TABLE VI. MINIMUM AND AVERAGE COST ACHIEVED BY GA AND BBO FOR 6 SITES Mnmum Cost Average Cost Mnmum Cost Average Cost e e e e e e e e e e e e e e e e e e e e e e e e e e e e+6 Fgure 6. Convergence of GA and BBO for 4 Fragments and 6 Stes 470

10 TABLE VII. MINIMUM AND AVERAGE RUNNING TIME OF GA AND BBO FOR 6 SITES Mnmum Tme Average Tme Mnmum Tme Average Tme (a) Fgure 7. Mnmum and Average Runnng Tme of GA and BBO for 6 Stes (b) VI. CONCLUSIONS Ths paper presents a new technque for replcated statc allocaton of data fragments durng the desgn of dstrbuted database usng bogeography-based optmzaton. To evaluate the performance of proposed algorthm, results are compared wth GA. From the results, t s clearly evdent that the proposed technque for data fragment allocaton s provdng qualty solutons n quck tme. The proposed algorthm sgnfcantly mnmze the data transfer cost durng the executon of a set of queres. It also mnmzes the storage cost of fragments. However n some cases the average cost of allocaton for BBO s more than GA, but for fast runnng tme and qualty soluton, BBO can be ntroduced as a capable algorthm for replcated statc allocaton of fragment durng dstrbuted database desgn. In future, the plan s to use hybrd approaches of bogeography-based optmzaton for data allocaton problem. REFERENCES [1] H.I. Abdallaha, A.A. Amer, H. Mathkour, Performance optmalty enhancement algorthm n DDBS (POEA), Computers n Human Behavor (2013), [2] I Ahmad, K. Karlapalem, Y.K. Kwok and S.K. So, Evolutonary algorthms for allocatng data n dstrbuted database systems, Dstrbuted Parallel Databases, 11, pp. 5 32, [3] P. Apers, Data Allocaton n Dstrbuted Databases, ACM Trans. Database Systems, vol. 13, no. 3, pp , Sept [4] A. Brunstroml, S.T. Leutenegger and R. Smhal, Expermental Evaluaton of Dynamc Data Allocaton Strateges n a Dstrbuted Database wth changng Workload, ACM Trans. Database Systems,

11 [5] R. G. Casey, Allocaton of Copes of a Fle n an Informaton Network, n Proc. AFIPC 1972 SJCC, Vol 40, 1972, pp [6] S. Cer, S. Navathe, and G. Wederhold, Dstrbuton Desgn of Logcal Database Schemas, IEEE Trans. Software Eng., vol. 9, pp , [7] S. Cer and G. Pelagatt, Dstrbuton Databases: Prncples Systems, McGraw-Hll Internatonal Edton, [8] W.W. Chu, Optmal Fle Allocaton n Multple Computer Systems, IEEE Transacton on Computers, Vol. C-18, No.10, [9] A. Corcoran and J. Hale, A Genetc Algorthm for Fragment Allocaton n a Dstrbuted Database System, Proc ACM Symp. Appled Computng, pp , [10] K.P. Eswaran, Placement of Records n a Fle and Fle Allocaton n a Computer Network, on Proc. IFIP Congr. North-Holland, [11] I.O. Hababeh, M. Ramachandran and N. Bowrng, A hgh-performance computng method for data allocaton n dstrbuted database systems, Sprnger, J Supercomput, 39:3-18, [12] H. Ma, An analyss of the equlbrum of mgraton models for bogeography-based optmzaton, Informaton Scences,180, pp , [13] R. MacArthur and E. Wlson, The Theory of Bogeography, Prnceton, NJ: Prnceton Unv. Press, [14] S. March and S. Rho, Allocatng Data and Operatons to Nodes n Dstrbuted Database Desgn, IEEE Trans. Knowledge and Data Eng., vol. 7, no. 2, pp , [15] M. Ozsu and P. Valdurez, Prncples of Dstrbuted Database Systems, Prentce Hall, Second Edton, [16] R. Sarathy, B. Shetty, and A. Sen, A Constraned Nonlnear 0-1 Program for Data Allocaton, European J. Operatonal Research, vol. 102, pp , [17] D. Smon, Bogeography-Based Optmzaton, IEEE Transacton on Evolutonary Computaton, Vol. 12, No. 6, pp , [18] A. Sngh and K.S. Kahlon, Non-replcated Dynamc Data Allocaton n Dstrbuted Database System, Internatonal Journal of Computer Scence and Network Securty, Vol. 9, No. 9, [19] A. Tamhankar and S. Ram, Database Fragmentaton and Allocaton: An Integrated Methodology and Case Study, IEEE Trans. Systems, Man and Cybernetcs Part A, vol. 28, no. 3, May [20] T. Ulus and M. Uysal, Heurstc Approach to Dynamc Data Allocaton n Dstrbuted Database Systems, Pakstan Journal of Informaton and Technology, 2(3): pp ,

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