2. INTRODUCTION: To understand the data allocation problem, assume a distributed database

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1 Journal of Recent Research in Engineering and Technology, 3(1), Sep 216, PP ISSN (Online): , ISSN (Print): Bonfay Publications, 216 STATIC DATA ALLOCATION FRAMEWORK FOR DISTRIBUTED DATABASES RAMYA and NATARAJAN Department of Computer Science, Thanthai Roever College Perambalur, Tamil Nadu, India Received 21 September 216; Accepted 2 October ABSTRACT: The data allotment issue includes finding the ideal position of the pieces to the locales of the correspondence system. The optimality can be characterized as for two distinct measures: negligible expense and execution. Biogeography-Based Optimization and Simplified Biogeography-Based Optimization systems have been utilized for both non-repeated and recreated static designation of information. Another heuristic calculation named Threshold and Time Constraint Algorithm has been proposed for non-recreated dynamic assignment of information. The proposed calculation reallocates information concerning the changing information access designs with time requirement. The proposed TTCA for non-recreated dynamic allotment of information in dispersed database framework is an augmentation of existing two methodologies: Optimal calculation and Threshold calculation. This new calculation diminishes the development of information over the system furthermore enhances the general execution of distributed database framework. 2. INTRODUCTION: Amid the most recent three decades, conveyed database innovation has risen as a standout amongst the most huge improvement in the field of database frameworks. Appropriated database innovation has turned into a fundamental piece of the majority of the business associations because of its decentralized nature. Circulated databases have disposed of a significant number of the weaknesses of the concentrated databases and it fits all the more normally in the decentralized structures of numerous associations [1]. All the significant merchants of database frameworks these days support disseminated 42 database innovation. In this way, the configuration of dispersion database is an imperative region of exploration. The discontinuity of the database is an entangled issue in itself. Diverse systems have been proposed for dividing the database by various specialists. The present study focuses just on sections assignment issue accepting that database is as of now divided [2]. The fracture and inquiry advancement systems are not inside the extent of the present examination work. Allocation Problem To understand the data allocation problem, assume a distributed database

2 Journal of Recent Research in Engineering and Technology RAMYA and NATARAJAN, system consisting of sites S = {S1, S2,.,Sn} on which a set of queries Q = {q1,q2,.,qq} is running. Each site has its own processing power, memory, and local database system and all the sites of the network are connected by a communication link. Let F = {F1, F2,.,Fm} be the set of fragments after partitioning all global relations during fragmentation phase of distributed database design. The data allocation problem entails to find out the optimal distribution of the fragments (F) to the sites (S). There are two measures to define the optimality [3]: Minimal Cost: The cost of allocation consists of the cost of storage of data and the data transmission cost i.e. retrieval cost and update cost. The objective of allocation problem is to identify an allocation schema that minimizes the cumulative cost of total transmission and storage. Performance: Minimum response time and maximum system throughput at each site are two different ways to check the performance of distributed database system. The allocation problem attempts to find an allocation strategy that maintains the performance criteria. Information Required for Allocation Allocation of the fragments/data in the distributed database design requires the quantitative information related to the database, storage capacity of each site on the network, the applications which run on the database, processing capabilities of each site and topology of the communication network [4]. This information requirement can be categorized as database information, application information, site information and network information. Database Information The first kind of necessary information that is required for the allocation is related to database fragments. The size of each fragment must be defined before allocation since it plays an important role while computing the communication cost [5]. Application Information The second kind of information requirement is the behavior of all the applications running on the different sites of the communication network. The retrieval and update behaviors are the two important measures to check the behavior of an 43

3 RAMYA and NATARAJAN Journal of Recent Research in Engineering and Technology application [6]. The retrieval behavior is the numbers of read accesses by a query to a fragment while its execution. Update behavior is the numbers of write accesses by a query to a fragment while its execution [7]. Site Information The site information in a communication network is the knowledge about the storage cost and processing capacity [8]. The storage can be calculated by multiplying the size of the fragment with unit cost of storing data at the site. The site information is generally used as the constraints in the allocation model. Network Information Network information is the knowledge about the topology of the communication network, the channel capacities, distance between sites, protocol overhead, cost of initiating a data packet, cost of transmitting a unit of data from one site to another site and so on [9]. Network information plays a key role in computing the total communication cost for the execution of a set of queries. Objectives of Data Allocation Minimize Communication Cost Minimize Storage cost Maximize performance In this Session, the data allocation problem in distributed database design and the objectives of data allocation have been addressed. Session-2 presents the background and reviews of the related work. A detail survey is provided for static as well as dynamic allocation of data in distributed database design. Session 3 proposes the two frameworks for both non-replicated and replicated static allocation of data. In Session 4, the performance of proposed algorithms for both static and dynamic distributed database is presented in the form of results and discussion. Finally, Session 5 concludes the research work presented in this paper. 3. RELATED WORK: Ceri et al. [1] have given a general framework for the design of data distribution. They have presented an entityrelationship schema of the design data dictionary. The data dictionary stores all the information required for the design of distributed database. They presented an integrated toolset for the optimal design of data distribution using Divide- Conquer solution methods. Increase Reliability and Availability 44

4 Journal of Recent Research in Engineering and Technology RAMYA and NATARAJAN, Cornell and Yu [11] proposed an integrated methodology to assign relations over the network and determine join sites simultaneously. The integrated methodology is divided into two separate stages. In the first stage, each query is divided into a sequence of steps of relational algebra operations. The output of the first stage is used as input for allocation of relations and selection of the join sites. This problem is formulated as linear integer programming problem. The objective of the problem is to minimize total communication. The processing power, communication capacity and the storage capacity of each site are the constraints for the optimization. Ciciani et al. [11] developed an approximation model to find out the effect of data replication on the performance of distributed database system. Primary copy approach is used to handle replication of data. They have found that the concurrency control protocol has an impact on the optimal number of replicates. Replication can improve the response time under optimistic and semi-optimistic concurrency control protocols. Blankinship et al. [12] developed an iterative heuristic method to solve two NPhard problems (data allocation and query optimization) simultaneously. Network sites and topology, database unit of allocation, number of queries and their frequencies are the input information require for the iterative method. The optimization iterative method helps to find out a combine local minimum of both the data allocation cost and query strategies cost. Fragments are allocated to the site, which is requesting them most frequently. Replication of data is not considered in them. Reid and Orlowska [13] proposed a model to allocate tables of a relation database to the communication network so that the total cost of executing a set of join queries can be minimize. The model describes the data allocation problem as an optimization program having an integer linear program. The developed optimization program achieved a minimal total cost for execution of a set of queries with permissible replication. Branch-and-bound and cut-set techniques are suggested to solve this problem. Lim and Ng [14] proposed an integrated approach for vertical fragmentation and data allocation. Maximal locality of query evaluation and minimization of communication cost is considered for the integrated design 45

5 RAMYA and NATARAJAN Journal of Recent Research in Engineering and Technology approach. Three different algorithms for vertical fragmentation and one algorithm for allocating rule and fragments have been proposed by the authors. Sarathy et al. [15] modeled a constrained nonlinear -1 programming formulation for allocating copies of relations from a global database to different sites of the network. The non-linear -1 programming formulation is then linearized and solved by the use of subgradient optimization. The objective is to minimize the total cost of transmission which executing a set of queries from various sites of the communication network. 4. Biogeography-Based Optimization (BBO) is the determining factor to measure the rate of change in the patterns of species in terms of number and verity on an island. BBO works on a population of candidate solution to solve a global optimization problem. Candidate solutions are called habitats. Feasible solution to the problem undertaken is represented by habitat. Each solution feature of a habitat is called a suitability index variable (SIV) of that habitat. A quality solution is considered to be a habitat with a high habitat suitability index (HSI). The quality of a candidate solution is measured by its HSI. The fitness of each habitat is represented by its habitat suitability index (HSI). A low HSI habitat provides a poor solution. The biogeography-based optimization (BBO) is a newly developed population-based evolutionary technique. Biogeography-based optimization (BBO) is based on theory of biogeography. Simon developed the biogeography-based optimization (BBO). BBO is primarily based on The Theory of Island Biogeography given by MacArthur and Wilson. The mathematical model on biogeography describes that the stability among the immigration of new species on an island and the emigration of already inhabited species 46 A habitat which is accommodates a high number of species is represented by high HIS solutions whereas a habitat which is accommodates a less number of species is represented by low HSI solutions. Therefore HSI of a solution is determining index to figure out the number of species present in the solution. High HSI solutions share their features with other solutions in the population and low HSI solutions accepts shared features from other solutions. Biogeography-based optimization (BBO) [16] assumes that each solution

6 Journal of Recent Research in Engineering and Technology RAMYA and NATARAJAN, (habitat) has a symmetric species curve with E = I, but HSI of a solution determines the S (number of species) value of that solution. Figure 3.2 represents the emigration and immigration curves of two candidate solutions to some problem. S 1 represents a low HSI solution and S 2 represents a high HSI solution. The comparison of S1 and S2 depicts that a small number of species represented by S 1 with low HSI habitat, whereas the number of Where, I is the maximum possible immigration rate; E is the maximum possible emigration rate; K is the number of species of the k th individual and n is the maximum number of species. In biogeography-based optimization, there are two operators: migration and mutation BBO Algorithm Step 1: Initialize the BBO parameters: Maximum Immigration rate (I) = Maximum Emigration rate (E) Maximum Mutation rate, Elitism Parameter, Size of the Population, Termination Condition Step 2: Generate a random set of habitats. Each habitat represents a potential solution to the given problem Figure Illustration of two candidate solutions In BBO, λ k and μ k are, respectively, representing the immigration rate and the emigration rate of the kth candidate solution (habitat). The immigration rate and emigration rate are functions of number of species in the habitat. The immigration rate (λ k ) and emigration rate (μ k ) can be calculated as follows : 47 Step 3: Evaluate habitats and compute corresponding HSI value of each habitat Step 4: calculate the immigration rate (λ) and emigration rate (μ) for each habitat according to HSI of each habitat Step 5: Probabilistically choose a non-elite immigration habitat (Hi) based on the immigration rate (λ i) Step 6: If Hi is selected then select emigrating habitat (Hj) based on the emigration rate (μ j)

7 RAMYA and NATARAJAN Journal of Recent Research in Engineering and Technology Step 7: If Hj is selected then randomly select a SIV from Hj and Step 4: Find the fittest solution. Call this solution He Step 5: Randomly select an SIV from He Step 8: Probabilistically perform mutation based on the mutation rate Step 9: If the termination condition is not met go to Step 3 Else, terminate Step 6:Select the immigrating habitat (Hi) from a uniform probability distribution Step 7: Replace a random SIV in Hi with one selected from He i.e. Hi (SIV) <- He (SIV) SBBO Algorithm for Data Allocation The algorithm of the proposed allocation of data fragments in a static distributed database environment is given below: Input: Database Information, Applications Information, Sites Information and Network Information Output: Non-replicated/Replicated Fragment Allocation Schema Method: Step 1: Initialize the SBBO parameters: Maximum Mutation rate (MR), Size of the Population, Termination Condition Step 2: Generate a random set of habitats equal to the size of the population satisfying the different constraints Step 3: Evaluate habitats and compute corresponding HSI value of each habitat Step 8: Generate rϵ [,1]; Step 9: If r < MR (Maximum mutation rate) then a randomly selected SIV from Hi is replaced with a randomly generated SIV; Step 1: Calculate HSI of Hi. Verify the feasibility of the solution and check the corresponding constraints; Step 11: If the modified habitat (Hi) is feasible and satisfies the respective constraints then the modified habitat will be replaced with its new version in the population of the next generation; Step 12: If the termination condition is not met go to Step 3 Else terminate; 5. EXPERIMENTAL SETUP AND RESULTS Non-Replicated Allocation of Data (NRAD) 48

8 Journal of Recent Research in Engineering and Technology RAMYA and NATARAJAN, Table 5.1 shows the performance comparison of GA, BBO and SBBO based nonreplicated static allocation of data for 4 sites and number of fragments ranging from 4 to 24. Minimum cost of allocation for all the algorithms is same when the numbers of fragments are 4, 6, 8, 1, 12 and 16. Minimum cost of allocation for SBBO and BBO is less than that of GA when the numbers of fragments are 2 and 24. Average cost of allocation of SBBO is either same or less than that of BBO and GA for all the cases. But average cost of allocation of BBO is more than GA in most of the cases. Figure 4.1 shows that BBO and SBBO based fragments allocation techniqes are faster as compare to GA based fragments allocation for 4 sites and SSBO is fastest among all the three techniqes. Num ber of Frag men ts G.A. BBO SBBO Min imu m Cost Av era ge Cos t Min imu m Cost Av era ge Cos t Min imu m Cost Av era ge Cos t Table 5.1 shows the performance comparison of GA, BBO and SBBO based nonreplicated static allocation of data for 8 sites and number of fragments ranging from 4 to 24. Minimum cost of allocation for all the algorithms is same when the numbers of fragments are 4, 6, 8 and 1. Minimum cost of allocation for SBBO and BBO is less than that of GA when the numbers of fragments are 12, 16, 2 and 24. But average cost of allocation of BBO is more than GA in most of the cases. Average cost of allocation of SBBO is less than that of BBO and GA for all the cases. Figure 6.2 shows that BBO and 49

9 RAMYA and NATARAJAN Journal of Recent Research in Engineering and Technology SBBO based fragments allocation techniques are faster as compare to GA based fragments allocation for 8 sites and SSBO is fastest among all the three techniqes. 6. CONCLUSION AND FUTURE SCOPE OF WORK Two frameworks have been proposed for both non-replicated and replicated static allocation of data. The first proposed framework is based on the biogeography-based optimization (BBO). Biogeography-based optimization (BBO) is a newly developed population-based evolutionary technique. The second proposed framework is based on the simplified biogeography-based optimization (SBBO). Simplified biogeography-based optimization (SBBO) is a modified version of the biogeography-based optimization. The performance of these new algorithms has been compared with genetic algorithm for data allocation in distributed database systems. All the three algorithms are tested on the same data set for each experiment and the results are obtained after running each algorithm 2 times independently for each experiment. Comparison is done on the basis of quality of the solution provided by all the algorithms and average running time taken by the algorithms. From the experimental results, it is observed that the performance of SBBO is much better than BBO and GA for non-replicated as well as for replicated allocation of data. SBBO is providing quality solution within shorter period of time as compare to BBO and GA. In almost all the experiments, the proposed SBBO algorithm is providing fragments allocation schema having cost of allocation less than that of BBO and GA. BBO is also providing good solutions as compare to GA when the numbers of fragments are relatively less in numbers. The performance of BBO degrades as the number of fragments increases. Therefore BBO can be used as an alternative technique for data allocation when the number of fragments and the number of sites are relatively less in number. Overall it has been observed that the SBBO algorithm outperformed BBO and GA in terms of solution quality and computational speed. REFERENCES: [1] L. Bellatreche, K. Karlapalem and A. Simonet, Algorithms and Support for Horizontal Class Partitioning in Object- Oriented Databases, Distributed and Parallel Databases, Vol. 8, No. 2, pp , 2. 5

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