Research of Dynamic Access to Cloud Database Based on Improved Pheromone Algorithm

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1 , pp Research of Dynamc Access to Cloud Database Based on Improved Pheromone Algorthm Yongqang L 1 and Jn Pan 2 1 (Software Technology Vocatonal College, North Chna Unversty of Water Resources and Electrc Power, Zhengzhou , Chna) @qq.com 2 (Henan Procuratoral Vocatonal College Zhengzhou , Chna) @qq.com Abstract In cloud computng envronments, the database s dynamc and real-tme features, and n data access t has been a Gordan not of cloud computng research; because n the ant colony algorthm that ants fndng food shares smlar correlaton wth cloud computng node fndng the access to the database. Therefore, the ntroducton of ant colony algorthm nto cloud computng database, the ntroducton of the chaos functon n the pheromone update, both wll mae the mproved pheromone avod the possblty of gettng nto local convergence, and hence mprove the effcency of database access n the cloud computng, reduce the load n the cloud computng. The smulaton results show that the algorthm n ths paper has been sgnfcantly mproved n terms of cloud computng networ queres and database consumpton, and greatly mproved the effcency of cloud computng. Keywords: cloud computng data; chaos; pheromone; ant colony algorthm 1. Introducton As the concept of cloud computng emerges, the networ model, combnng dstrbuted concept wth grd technology, has been wdely appled, and the emergence of cloud computng can help cloud users use varous resources under the cloud envronment accordng to the demands [1]. How to best mae users have access to data n the cloud database has always been a focus of the study. Owng to the dynamc and dstrbutve natures of cloud computng, the database under cloud computng envronment dffers sgnfcantly from the tradtonal database. Besdes, the emergence of cloud computng database has broen the tradtonal centralzed, statc and non-extendable storage method, and has won more and more attenton. Lterature [2] proposed a CCRP algorthm. Based on the characterstcs of data storage n the cloud computng database, ths algorthm frst appled contnuous readng feature to date n query schedulng, solved the dle system resources n the cloud computng compared to other algorthms, and the smulaton experments proved that CCRP algorthm was superor to other algorthms n system utlzaton rate and system performance. Lterature [3] proposed to combne ant colony algorthm wth the cloud database, rapdly and reasonably fnd the database requred to access from the cloud database through pheromone evaporaton, and mproved the effcency of cloud computng. Lterature [4] proposed a new cloud relatonal database platform, descrbed the vertcal mechansm and horzontal mechansm of data, and the expermental results showed that the cloud relatonal database platform had good relablty, extendblty and usablty. Lterature [4] proposed a tree-based cloud database. The database organzed and ISSN: IJDTA Copyrght c 2016 SERSC

2 retreved data n the form of tree, reorganzed the exstng data storage method n the form of tree, and satsfed the requrements over data storage and retreval. However, t had the weanesses of hgher data retreval costs, so that t was mpossble to mplement cloud computng database retreval under bg data. Lterature [6] proposed the best practce and the actual operatng techncal ndcators for database cloud platform n telecommuncatons companes. In foregn countes, Google uses the computer cluster composed of cheap PCs, and acheved the GFS dstrbuted, BgTable dstrbuted and MapReduce [7-9] envronments, n order to handle bg data n the cloud database as many as possble. Lterature [10] proposed a performance functon based on Sesson and content ratngs, too full account of the customer prorty, and mproved the database access effcency. The tradtonal ant colony algorthm s a dscrete optmzaton to solve NP problems, whch s a good soluton to the tradtonal statc combnaton optmzaton problems. Based on the tradtonal ant colony algorthm, ths paper mproves the ant colony algorthm, and the mproved ant colony algorthm has good advantages n solvng the access of cloud computng database. The smulaton results show that the presented algorthm can effectvely reduce the access tme of cloud computng database and enhance the access effcency of cloud computng networ. 2. Relevant Knowledge 2.1 Ant Colony Algorthm Ant colony algorthm s a swarm ntellgence and bonc optmzaton algorthm, whch s manly used to solve complex combnaton optmzaton problems. It s characterzed by ntellgent search, global optmzaton and dstrbuted computng. Ant colony algorthm means that ants leave a secreton called pheromone on the path of seeng food, and other ants wll choose a path accordng to the pheromone secreton. As a result, many ants travel down a certan path wthn a short tme, formng a postve feedbac mechansm and thus producng the shortest path from the colony to a food source. 2.2 Descrpton of Cloud Database Problems Cloud computng database conssts of mass databases dstrbuted n varous parts. However, the actual cloud database has the followng problems. On the one hand, a part of databases are stored n the fxed cloud server, whereas the other parts are stored n the moble ntermedate servce provders. These moble servce provders contnuously nteract wth cloud computng wth the oned or qut cloud nodes. On the other hand, could computng has cloud nodes or ln falures at any tme and new cloud nodes and lns on n, whch brng bg dffcultes to the access to resources n the cloud database. Therefore, how to acheve data access n ths dynamc envronment has become a ey to solvng problems. The user query operaton process of cloud database s as follows: (1) Cloud users send the contents to be quered to the database server through the networ; (2) Cloud server processes SQL commands sent by the users and dstrbute data sub-commands to each storage cloud nodes through pre-processng. (3) Each storage cloud node nteracts between the dfferent nodes after recevng SQL commands. (4) Upload the query results of each cloud node to the cloud server for summary. (5) Return to users and complete the query after server mang summary. In the process the user query, there are many factors nvolvng the query effcency, such as tme for users transmttng to server, server resolved tme, tme for server sendng to nodes, query tme between nodes and tme for nodes feedbac to server. In order to best 198 Copyrght c 2016 SERSC

3 guarantee the effcency of the user access to the database, the ey queston s how do nodes fnd the requred resources from the database. The process of resources seeng s to qucly and safely see an optmal path from the cloud startng pont to the end pont, whch s very smlar to the food seeng process of ant colony algorthm n realty. In ant colony algorthm, after ants seeng foods, the process from source node to the orgnal node s equvalent to cloud users sendng requests to the database, and the process of recordng returnng from the database s obtaned. In ths process, dfferent nodes obtaned from dfferent postons s equvalent to the database nodes n cloud computng. Snce the cloud computng adopts the column storage to manage data, t s very mportant to mae query schedulng for the database n cloud computng. The data query process n cloud computng s combned wth the flow dstrbuton, congeston control and route selecton processes n cloud computng. Compared wth tradtonal statc databases, the cloud computng database gves more consderaton to dynamc and real-tme storage. The routng table records the nformaton requred for the routng algorthm, whch can ensure that the scale of the cloud computng database can see the optmal soluton under certan condtons. However, f the cloud computng database has a relatvely large scale, t may be mpossble to see the optmal soluton. Obvously, t s an nevtable trend for the ncreased scale of cloud computng database, whch cannot reach the accuracy of routng algorthm to some extent. Therefore, the access to cloud computng database s the combnaton optmzaton problem n plannng. 3 Applcaton of the Algorthm n Cloud Computng Database 3.1 State Transton Combned wth the features of ant colony algorthm, assumng that m represents the database number n cloud computng, represents the dstance between the cloud database and the cloud database d s the amount of pheromone between request at t n the cloud database, 1/ d stands for the heurstc nformaton,, and p () t s the possblty of to query the cloud database, then [ ( t)] [ ( t)], allowed p [ s( t)] [ s( t)] (1) sallowed 0, otherwse In the equaton, the log sheet tabu ( 1,2,... m, 1,2... n) s used to represent the record havng access to the th database, beng used for the current travelng database set of database record, and allowed { C tabu } represents the record can have access to the next database. and respectvely represent the n computng. If =0, the database nearest to the access request weghts of and may be frst accessed. If =0, when the pheromone n ant colony algorthm wors, t becomes a heurstc search algorthm. When all access records have searched all databases, the pheromone smlar to the ant colony algorthm wll be updated. 3.2 Updatng Rules for Tradtonal Pheromone m 1 ( t 1) (1 ) ( t) ( t) (2) In Equaton (2), represents the factor of pheromone evaporaton, and () t th Copyrght c 2016 SERSC 199

4 represents the tral (pheromone) that the travelng through the path, at. Where, database Q L, where, t th record has access to the database and Q () t L (3) 0 represents the rato of length between node access database L and the represents the number of database accessed by the th record. In the prevous ant colony algorthm, an optmal soluton can be obtaned through the postve feedbac mechansm. However, as the path ( nodes n cloud computng) ncreases, t s easy to cause relatvely long searchng tme. If the recorded node access trals (pheromones) are centralzed to the optmal path, the stagnaton problem wll be emerged easly and the local optmal soluton s obtaned. The optmal query to the database access records n the cloud database may not be necessarly acheved, and thus lose the query to the optmal soluton. 3.3 Improved Pheromone Rules Snce the ant colony algorthm convergence s slow, the ants cannot be guded to the optmal soluton to a certan extent, whch s largely nfluenced by pheromones. The pheromone updatng cannot gude the generaton of optmal soluton from a global perspectve, cannot gude the cloud nodes correctly access the cloud database from the perspectve of cloud computng database, or t has the problems of low access neffcency. The pheromone updatng n the ant colony algorthm cannot accurately reflect the path, t cannot reflect the recorded accurate database access n the cloud computng database, and thus the optmal soluton s affected. On ths bass, ths paper changes the pheromone equaton, namely, ntroduces chaos algorthm to the pheromone updatng. The dea of chaos algorthm s to compare n the traversal process, save the relatvely good soluton n the comparson process and see the optmal soluton n the teraton search process. Ideas are as follows: 3.4 Algorthm Descrpton In the ant colony algorthm, the process of ants seeng food s the process of cloud nodes access to the data n server. Ths part combnes the database query wth the ant colony algorthm, and the algorthm steps are as follows: Step 1: Intalzaton: Place the ants of smulaton node access on a cloud node, lst the end pont server node nformaton to the log sheet tabu, set the maxmum frequency of ant food seeng nodes as Max; meanwhle, fnd whether the node belongs to the feasble node regon Feasble. If feasble, go to Step 2 Step 2: Tae the current node as the center, select the adacent selecton strategy and go to the next node y. The node y belongs to Feasble, then the node y consttutes a combnaton { y }, the nodes select accordng to the possblty value of adacent nodes, and select the node wth relatvely larger possblty value as the cloud node x and as the next node. Step 3: Accordng to the pheromone and other nformaton n the ant colony algorthm left by ant n the travelng process, as tme goes by, pheromone updatng wll follow the formula (2) for local updatng, and leave pheromone for subsequent ants, n order to provde reference to the database query of other nodes n the cloud computng and reduce x x x 200 Copyrght c 2016 SERSC

5 the access tme. Step 4: After the request sent by node x s repled, the global pheromone updatng rule, as mentoned n Secton 4.3, s used for the query pheromone n the shortest path, then update and save. Step 5: Accordng to the defnton of ant colony algorthm, chec whether the node access requrement n cloud computng s repled by server. If yes, go to Step 6. If not, go to Step 2. Step 6: Fnd the path, the server returned node nformaton returns to the startng poston of node x along the orgnal path. 4. Experment and Analyss In order to prove superorty of the algorthm, ths paper conducts the smulaton experment n the VC desgn envronment, and uses sx sets of computers confgured as CPU Core 32.2G, DDR4G memory and 500G hard ds. One set of PC s selected as a server to control the database system, whereas the rest fve sets are nstalled wth SQL Server2008 as the data storage nodes. Ths paper manly apples system analog data for expermental data collecton, sets the capacty of access data between [50M, 100M], maes comparson between two ndcators of the data access networ consumpton tme and data access tme, whch compares the Lterature [6] algorthm wth Lterature [10] algorthm. Fgure 1. Comparson of Networ Consumpton Tme Fgure 2. Data Access Tme Copyrght c 2016 SERSC 201

6 Fgure 3. Node Access Effcency Accordng to Fgure 1-3, compared wth the Lterature [6] algorthm wth Lterature [10] algorthm, the presented algorthm s sgnfcantly mproved n terms of networ consumpton tme, database access tme and node access effcency, whch s manly caused by the pheromone updatng mprovement n ant colony algorthm so as to avod the possblty of local optmal soluton, mae the tme for targetng database of node access reduce and effectvely decrease the networ consumpton. 5. Concluson Ths paper combnes the ant colony algorthm wth cloud computng database, and smulates the record query process of node access database n cloud computng as the process of food seeng. Frst of all, ths paper mproves the pheromone based on the ant colony algorthm, enables the mproved pheromone to effectvely prevent the possblty of local convergence, conducts the smulaton experment, and further proves that the presented algorthm s sgnfcant mproved n terms of query and networ consumpton n the cloud computng database as compared to the lterature algorthms, whch provdes a reference for the database query n the cloud computng envronment. References [1] L. Z. Yun, Research on cloud Database, Journal of software, vol. 23, no. 5, (2012), pp [2] Z. L. Yun, Database Query Schedulng Algorthm Based on the C loud Computng, Journal of Zhengzhou Unversty (Engneerng Scence), vol. 31, no. 7, (2010), pp [3] S. H. Lang, Cloud database dynamc route query based on self-adaptve ant colony optmzaton, Computer Engneerng and Applcatons, vol. 46, no. 9, (2010), pp [4] C. P. Png, Research on scalable cloud relatonal database, Computer Engneerng and Desgn, vol. 33, no. 7, (2012), pp [5] K. Y. Me, Adaptve Resource Provsonng for Cloud Computng, Telecommuncatons Scence, vol. 28, no. 1, (2012), pp [6] L. Yong, Constructon and Operaton Program of Enterprse-Class Database Cloud Platform, Telecommuncatons Scence, vol. 28, no. 2, (2012), pp [7] S. Ghemaw, H. Goboff and L. S. Ta, The Google Fles System, SIGOPS Operaton Systems Revew, vol. 37, no. 5, (2003). [8] F. Chang, J. Dean and S. Ghemaw, Bgtables: A Dstrbuted Storage System for Structured Data, 7th Symposum on Operatng Systems Desgn and Implementaton. Seattle, WA, USA, (2006), pp [9] J. Dean and S. Ghemaw, MapReduces: Smplfed data processng on large clusters, Communcatons of the ACM, vol. 51, no. 1, (2005), pp [10] Z. J. Juan, L. Tao and Q. Yan, Classfcaton Algorthm of Database Request Based on Sesson and Content Level n Cloud Computng, Computer Scence, vol. 40, no. 2, (2013), pp Authors Yongqang L, ( ) Lecturer, Master, Research Orentaton: Computng Networ. Jn Pan, ( ) Assocate Professor, Master, Research Orentaton: Computer Networ. 202 Copyrght c 2016 SERSC

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