Database Replication Algorithm Performance in High Speed Networks Under Load Balancing

Transcription

1 Database Repication Agorith Perforance in High Speed Networks Under Load Baancing Rekh Nath Singh 1, Raghura Singh 2 1 Research Schoar, A. P. J. Abdu Kaa Technica University, Lucknow, India. 2 Director, K.N.I.T., Sutanpur, Sutanpur, India. Abstract Database repication process aintains obects of database ike tabes, in a nuber of databases that buid a syste of distributed database. The database repication requireents are increasing day by day due to the ore use of internet. To eet such requireents priorities of request can be used. In this work two casses of requests are considered i.e., Low Priority and High Priority. Low priority requests are not so iportant and they can be deayed or drooped over high priority requests. For exape, downoading a song is ow priority requests, whie inforation send by defense appications are high priority requests. The request oss rate can be further reduced using oad baancing conditions where soe of the contending requests sent to soe other nodes, and they reach their destination using soe aternative paths. In view of above aspects perforance evauation of M-PDDRA (Modified Pre-bringing Based Dynaic Data Repication Agorith) is done using coputer siuation. Keywords: Database repiation; database; priorities; oad baancing etc. cabe, with these cabes servers are connected using O/E or E/O conversions as required. The request arriving on these servers ay have priorities, and if request cannot be served, then it wi be dropped. To save the dropping of requests, buffering is perfored at the servers. But if buffer overfows, then it is ost ikey that ow priority requests wi be dropped. To iniize this oss network oad baancing schee can be appied. This paper, investigate the perforance of the agorith, under ow and high priority of requests aong-with oad baancing conditions at various server. INTRODUCTION We coud define the process of Repication of copying and keeping up the obects of database ike tabes, in a nuber of databases that buid a syste of distributed database [1]. We notice and ake storing of these variations that are put into one site prior to sending and are being appied at a the reote positions. This process akes use of technique of distributed database in order to ake sharing of data between nuerous sites, however we can concude that a repicated database and a distributed database are different. If we tak about a distributed database, we can find the data at nuerous positions, sti a specific tabe is avaiabe at ust one position. We are going to ention few typica causes for aking use of repication: This technique gives rapid, oca access to shared data due to the fact that it aintains activity over a nuber of sites. A few users can have the authority to access one server however other ay have the benefit of accessing various servers, hence diinishing the oad at each server. In addition, the repication site with the east expense of access coud be source fro where users can access data. Generay, this is the geographicay nearest site to the [2]. However, in the distributed database servers can be ocated anywhere across the gobe. Nowadays, backbone network runs on fiber optic Figure 1: Scheatic of a four-hosts custer a singe virtua server to hande network traffic. We get great accessibiity and versatiity to enterprise-wide TCP/IP services by Network Load Baancing. These services ay be streaing edia, proxy, Web, Terina Services, Virtua Private Networking (VPN) services. IP traffic is distributed to nuerous copies of a TCP/IP service by Network Load Baancing ike a Web server, a going on a host inside the custer. Network Load Baancing straightforwardy segents the requests of the cient aong the hosts and gives the cients a chance to get to the custer by aking use of at east one or ore "virtua" IP addresses. If we consider the cient's perspective, the cient find custer to 3475

2 be a soitary server that gives responses to these requests ade by cients. With the enhanceent in the enterprise traffic, network adinistrators coud ust connect an extra server into the custer. For instance, as shown in the Figure 1, the custered hosts operator with one another with the purpose to serve traffic of the network fro the Internet. A copy of an IP-based service ike Internet Inforation Services 5.0 (IIS) is run by each server, and networking workoad is distributed by Network Load Baancing aong the. This pace up ordinary processing in the anner that cient of Internet can observe speedier turnaround in regards to their requests. For incuded fraework accessibiity, the appication at the back-end (suppose a database) ay work on a two-node custer going on Custer service. In coparison to the other software soutions, Network Load Baancing gives better resuts. For exape, round robin DNS (RRDNS), akes the distribution of workoad aong a nuber of different servers however it is not abe to give a echanis for the avaiabiity of server. In the event of a server faiure, RRDNS, not in the way ike Network Load Baancing, wi carry on to transfer it work ti the faiure is observed by a network adinistrator and eiinates the particuar server fro the DNS address ist. This is in turn, brings in service interruption for cients. We have soe benefits of Network Load Baancing over soe other options for oad baancing on the basis of both hardware- and software that present singe faiure points or execution hindrances by aking use of a centraized dispatcher. Since Network Load Baancing got no restrictive hardware necessities, we can use any proper coputer. This gives noteworthy cost reserve funds when contrasted with excusive equipent oad baancing soutions. RELATED WORKS A PDDRA (Pre-bringing Based Dynaic Data Repication Agorith) is exhibited in [7]. The principe thought is to preget a few inforation utiizing the heuristic agorith prior to the rea repication begin to essen atency. In earier research, adustents in PDDRA (M-PDDRA) are recoended to ake the further reduction in atency. In yadav et. a. odified the PDDRA schee and aso estabish connections aong RS (regiona servers) for sharing inforation, this aow oca searching of the required inforation [8-11]. For ore detai Pease aude to [7] for further detais. The fundaenta purposes of the agorith are outined as beow: 1. We consider the internet coud in M-PDDRA technique as aster node due to the fact that there is avaiabiity of data in the internet for the repication (Figure 2) Figure 2: Scheatic of the PDDRA schee 2. In the case a node deveops any repication request then it wi get ooked for in oca network through edge node, and further a siutaneous request wi be transitted to the goba network. 3. It is possibe that we ay not have the avaiabiity of data at any oca node or we have a arge waiting tie is too arge, due to the reason that siutaneous request is transitted to both to a oca node as we as a aster node, in the event of aster node access is in queue for suppose tie t q then we can ake the oca search for tie t s < t q. The above discussed siutaneous requests to both goba and oca network wi ake the reduction in atency as copared to the initia request send to oca network and after that to goba network. SIMULATION AND RESULTS We carry out the siuation in MATLAB. The siuator is based on a rando event generator and popuary tered as Monte Caro siuation. In the siuation rando traffic ode is considered. This ode is not copex; and even then it gives decent insight about the repication process. This ode considers that the request can be originated fro any of one the cient with probabiity ρ and each generated request is equay ikey to be served by any of the N servers with probabiity 1/N. Therefore, probabiity that requests arrive for a specific server in any tie sot is [12] N! Pr( ) 1 for! N! N N N 0 N, (1) Let Q 1, Q 2,.., Q q denote the ratio type-1,type-2,, type-q requests to the tota nuber of requests; q i1 Q 1.where q is the priority types ( 1 is the highest, q is the owest). i 3476

3 Probabiity that n 1 type-1, n 2 type-2,... n q type-q requests arrive at the server in sae tie sot can be foruated as: ( n )! n1 nq n Pr( ( )...( ) ) 1,,..., n q 1 P K ( n!) b Q Q The requests wi ony be generated at the sot boundary ony. Most of the systes have oad in range of 0.4 to 0.8. Systes having oad 1 wi aways be saturated and in genera it is ipractica. If requests is generated then it wi randoy assigned a server fro the avaiabe servers which can serve the request. However, if ore than one server can serve the request than server seection is done randoy. However, if oad baancing schee is epoyed, then request wi be assign a server with esser request to serve. Again if sae nuber of requests is eft to be served then any one of the avaiabe server is randoy assigned. This paper adds one ore paraeter on the request generation, i.e., the priority. In this echanis each generated request carries priority. We considered two type of priority; high and ow. High priority requests are served first over ow priority requests. If request is generated on the given oad then high priority requests are serve first over ow priority requests. If arriving request can be served instanty, then it wi be paced in the buffer and ater on it wi be retrieved fro the buffer and served. The nuber of requests that can be buffered wi depends on the buffer capacity of the server. If arriving request cannot be served at the server then it wi be drooped and a negative acknowedgeent (Server in not found/ pease try again) is send back to the sender and sender again regenerate request after a few ore tie sots. To avoid oss of arger nuber of requests a hard oad baancing schee which restrict the nuber of request that can be send to particuar server, whie other eftover requests are send to other servers is epoyed. Requests are fied in the buffer using rues defined under: A. Rues for fiing Buffer 1. For each arriving request first buffer is checked, if buffer is epty, then request wi be served instantaneousy. 2. If buffer is not epty, then priorities of the buffered wi be checked and one high priority request eaves the buffer in FIFO anner, and incoing request wi be buffered using rue 5 3. If in the buffer ony ow priority requests are stored and arriving request aso has ow priority then it wi be buffered using rue If in the buffer ony ow priority requests are stored and arriving request has high priority then it wi be served. 5. The nuber of requests in the buffer shoud be esser or equa to buffer capacity. 6. In above schee ow priority request ay reain in the buffer for very ong duration, to avoid this after a (2) fix tie sots a ow priority request eave the buffer. This tie sot is chosen randoy depending on buffer capacity. 7. To avoid overfow of buffer a hard oad baancing schee is epoyed at each server which restricts the nuber of requests that need to be served by particuar server. B. Resuts and Discussions Request Loss probabiity: It coud be defined as the voue of data that cannot fow via a network, or ese we can define it as the fraction of the generated requests which are not served by any one of the server. Network Load: We can define network oad as the easure of data (traffic) is fowing through the network. In the siuation two types of request requests ow and high is considered. In figures 3 and 4 egends TRL, HPR and LPR are stand for tota request oss, high priority request oss and ow priority request oss respectivey. The nubers of cients/servers (N) are considered to be 4. Figure 3: Request oss probabiity vs. oad for Low priority 0.2 under buffer 4 Figure 4: Request oss probabiity vs. oad for Low priority 0.6 under buffer

4 Figure 3 shows the request oss probabiity vs. Load. In our work we have not shown throughput vs. oad pot because ow request oss rate. Moreover throughput is equa to 1- request oss probabiity. Therefore, both the graph can be used as they ead to sae concusions. In the request generated 4 cients are considered and servers are aso considered to be 4. The perforance ow priority requests is shown with diaond arker, for high priority requests is shown by square arker whie tota request oss which incude the oss of both high and ow priority requests is shown with circe arker. Out of the tota generated requests 20% are of ow priority whie eft over 80% are high priority requests. At the oad of 0.4, the request oss probabiity for high priority requests is , for ow priority requests it is which is neary equa to the tota oss it is evident fro the figure that the request oss rate of high priority requests is uch ess than that of ow priority requests. Figure 4 shows the request oss probabiity vs. Load. Out of the tota generated requests 60% are of ow priority whie rest 40% are high priority requests. Considering the request oss probabiity at the oad of 0.8, for HPR is , for LPR and TRL is Here it is cear fro the figure that ti oad 0.8, HPR oss is zero and ony ow priority requests are ost. Coparing figures 3 and 4, it is cear that ow priority packets are ost first, and as their proportion in tota requests increases, their oss aso increases. However, the perforance of HPR iproves significanty. Load Baancing The oad on a particuar node can be reduced by defecting the soe of the arriving packets. The nuber of packets arriving for a particuar output can be expressed as N N! E[ ] 1. (3)! N! N N 0 N Now, g is the fraction of packets that are defected that effective oad is e N 0 N! (1 g ) 1! N! N N N N. (4) In core nodes once packets arrive then decision regarding defection is perfored, therefore above equation can be sipified to (1 g) (5) e Figure 5: N = 4, B = 4, Low priority 0.2, oad baancing factor 0.1 Figure 5 shows the request oss probabiity vs. Load. Out of the tota generated requests 20% are of ow priority whie rest 80% are high priority requests and out of generated requests 10% requests foows soe aternative path to reach to the server. Coparing the resuts at the oad of 0.8, the request oss probabiity (HPRL) for high priority requests is , for ow priority (LPRL) requests it is whie the tota oss (TRL) is Figure 6 shows the request oss probabiity vs. Load. Out of the tota generated requests 25% are of ow priority whie rest 75% are high priority requests and out of generated requests 25% requests are directed towards the output through soe aternative path. Initiay beow 0.6 oad, a significant difference is observed between high and ow priority requests oss. Coparing the resuts at the oad of 0.8, the request oss probabiity for high priority requests is , for ow priority requests it is whie the tota oss is Figure 7 shows the request oss probabiity vs. Load. Out of the tota generated requests 50% are of ow priority whie rest 50% are high priority requests and out of generated requests 25% requests are directed towards the output through soe aternative routes. Coparing the resuts at the oad of 0.8, the request oss probabiity for high priority requests is , for ow priority requests it is whie the tota oss is In the figure 3.7 at the oad of 0.8, the request oss probabiity for high priority requests is for ow priority requests it is Thus it is evident fro the figures oad baancing reduces the request oss probabiity. 3478

5 Figure 6: N = 4, B = 4, Low priority 0.25, oad baancing factor 0.25 Figure 8: Bar graph for request oss for different proportion of high priority requests Figure 9: Bar graph for request oss for different buffering conditions Figure 7: N = 4, B = 4, Low priority 0.25, oad baancing factor 0.5 It is cear fro Figs 5 to 7 that as the oad baancing factor increases the request oss probabiity decreases. However, due to the high priority of soe requests they get upper hand over other requests, therefore iproveent for high priority requests is ore. As the nuber of ow and high priorities requests affects the tota oss, therefore for cear observation various types of requests osses are obtained for this Monte Caro siuation is perfored for iterations whie keeping oad to a fixed vaue of 0.6 for different proportion of high priority packets. The obtained resuts are shown in Figure 8. Here, for higher proportion of high priority requests, in tota oss both high and ow priority request contributes. Whie for oderate vaue of high priority requests in tota oss aor contribution is due to ow priority requests. For ower proportion of high priority requests, in tota oss is due to the ow priority requests ony. As discussed above, different proportions of high and ow priorities can change the proportion of the oss of different types of requests, but over-a oss cannot be reduced. The over-a oss can be reduced by using ore buffers as shown in Figure 9. In this figure proportion of ow priority request is taken to be 0.2.By increasing the buffer for 4 to 6 and then fro 6 to 8, the request oss reduces by a factor of ore than 10. However, the quaity of service is aintained, and oss of high priority requests is owest and by increasing buffer and using oad baancing can be brig down to a negigiby sa vaue. 3479

6 Load Baancing Figure 10: Scheatic of n nodes network On a particuar node i the arriving oad is the su of the partia oad arriving fro various inks (Figure 10) and can be written as n 1, under the condition 0 1 and where is the nuber of nodes directy connected to node i and n is the tota nuber of nodes in the network. Eqn 6, provides the iniu vaue of fraction of oad that needs to be defected on node i for oad baancing to be effective. Whie ρ denotes the oad arriving for ink towards node i and it considered to be rando between 0 and 1. The siuation resuts for two networks are detaied in Figure 10. In high speed optica networks, the nubers of core nodes are ess than 20, whie in current eectronic networks nuber of core nodes can grow up-to to iions on nodes. In two networks 10 and 100 nodes are considered which can be considered as representation of optica and eectronic networks. The resuts for 10 nodes network is shown in Figure 10, whie for a genera eectronic networks of 100 nodes is shown in Figure 11. As shown in figure 10, the iniu vaue of oad defection factor is high with esser nuber of nodes and as the nuber of nodes increases the vaue of oad defection factor reduces. For exape in a 4 node network iniu vaue for oad defection is whie for 10 nodes it becoes in case of arge node network, oad defection factor reduces to zero if nuber of nodes are greater than 80. Therefore resuts presented in figures 3-9 are appicabe for arge node networks. However, with esser nuber of nodes soe corrections need to be done in packet oss resuts. g (6) eff i 1 1 where g i denotes the fraction of oad which is being defected. In addition to this other nodes which are directy connected to node i can aso defect their data to node i. Therefore effective oad shoud be written as g g p eff i If inks are chosen unifory then we have, g eff gi w. (7) Figure 11: Load baancing factor vs. nuber of input/output inks (10). Where, w denotes the nuber of input/outgoing inks to a particuar node. The oad baancing is effective when g g i 1 1 w 0 Therefore, g i 1 1 g w. (6) Figure 12: Load baancing factor vs. nuber of input/output inks (100). 3480

7 particuar server. Further, it is shown that in high speed networks, oad baancing echanis is affected by both outgoing and incoing traffics, and oad baancing at particuar node is aso affected by oad baancing of other nodes. Figure 13: Request oss probabiity vs. oad under oad baancing factor (0.5) expected and actua oss. Figure 14: Request oss probabiity vs. oad under oad baancing factor (0.2) expected and actua oss. In figure 13, request oss probabiity vs. oad is potted, under oad baancing factor of 0.5, here expected oss is uch esser in coparison to actua oss, and obtained difference is significant. Here, expected oss is obtained using eqn. 6, whie actua oss is obtained using eqn. 7. At the oad of 0.6, the expected request oss probabiity is whie actua oss probabiity is In figure 14, request oss probabiity vs. oad is potted, under oad baancing factor of 0.2, here again expected oss is uch esser in coparison to actua oss, and obtained difference is ess significant. At the oad of 0.6, the expected request oss probabiity is whie actua oss probabiity is CONCLUSIONS In this paper, perforance evauation of per-fetching repication agorith is done. The perforance evauation is done under prioritized traffic whie considering oad baancing schee. In the resuts it has been found that, using buffering, high priority requests can be served with neary 100 percent efficiency. However, at higher oads (>0.8) for ow priority requests throughput is sighty esser. To keep oss of high and ow priority requests to a very ow eve, we have aso adopted a hard oad baancing echanis, which reduces the oad on a REFERENCES [1] B. Kee and G. Aonso, Database repication: a tae of research across counities, Proceedings of the Internationa Conference on VLDB Endowent. Switzerand), Vo. 3, No. 1, pp.5-12, [2] A. Yair, D. Caudiu, M.A. Micha, S. Jonathan and T. Ciprian, Practica wide-area database repication. Technica report, Johns Hopkins University, [3] Y. Chen, D. Berry and P. Dantressange, Transaction based grid database repication, Proceedings of UK e-science. Edinburgh, U.K. pp , [4] A. Correia, L. Rodrigues, N. Carvaho, R. Viaça, R. Oiveira and S. Guedes, GORDA: An open architecture for database repication Proceedings of Sixth Internationa Syposiu on Network Coputing and Appications. Boston, USA, pp , [5] S. Goe, R. Buyya, Data repication strategies in wide area distributed systes, Enterprise service coputing: fro concept to depoyent, pp , [6] A. Thoson, T. Diaond, S. C. Weng, K. Ren, P. Shao and D. J. Abadi, Cavin: fast distributed transactions for partitioned database, Proceedings of the ACM SIGMOD Internationa Conference on Manageent of Data. Scottsdae, Arizona, USA, pp. 1-12, [7] N. Saadat and A.M. Rahani, PDDRA: A new prefetching based dynaic data repication agorith in data grids, Springer: Future Generation Coputer Systes, Vo.28, pp , [8] S.K. Yadav, G. Singh and D. S. Yadav, Matheatica fraework for a nove database repication agorith, Internationa ourna of ModernEducation & Coputer Science, Vo.5, No. 9, pp.1-10, [9] S.K. Yadav, G. Singh and D. S. Yadav, Anaysis of database repication agorith in oca and goba networks, Internationa ourna of Coputer Appications, Vo.84, No. 6, pp.48-54, [10] S.K. Yadav, G. Singh and D. S. Yadav, Throughput and deay anaysis of database repication agorith, Internationa ourna of ModernEducation & Coputer Science, Vo.5, No. 12, pp.47-53, [11] S.K. Yadav, G. Singh and D. S. Yadav, Anaysis of a database repication agorith under oad sharing in networks,, Journa of engineering Science and 3481

8 Technoogy (JESTEC),, Vo.11, No. 2, pp , [12] R. J. Mishra, and A. Jain, Perforance of Data Repication Agorith in Loca and Goba Networks under Different Buffering Conditions, Internationa ourna of ModernEducation & Coputer Science, Vo.7, No. 9, pp.34-41,