# ROUTING ALGORITHM BASED COST MINIMIZATION FOR BIG DATA PROCESSING

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4 let P v be a new path with cost C + w(u, v) formed by concatenating edge (u, v) to path P u insert P v into B cost for the number of servers, communication and operation are determined. (a) SERVER COST (8) 4 JOINT OPTIMIZATION To linearize the constrains due to product of two variables joint optimization is done. We define a new variable as follows (9) Which can be equivalently replaced by linear constrains as (10) (11) The constrains can be written in linear form as SERVER COST COMMUNICATION COST 1000 JOINT NO OF REPLICAS K MAP (b) COMMUNICATION COST JOINT NO OF SERVER KMAP (12) (13) In a similar way,we define a new variable as Which can be linearized by -(14) OPERATION COST (c) OPERATION COST NO OF SERVER JOINT K MAP -(15) 5 PERFORMANCE MEASURE -(16) The performance results of routing algorithm (k map) is analyzed which is compared with a separate optimization scheme algorithm (joint), in which minimum number of servers to be activated is found, the traffic routing scheme using the network flow model is described. The result graph will be non-joint, joint, genetic algorithmperformance graph.from the below graph,the values of both joint and k-map has been compared. The values of k-map will high value than using joint linear method. Based on this individual 6 CONCLUSION: Thus the study of the data placement, task assignment, data center resizing and routing to minimize the overall operational cost in large-scale geo-distributed data centers for big data applications has done. Therefore first characterize the data processing process using a two-dimensional Markov chain and derive the expected completion time in closed-form, based on which the joint optimization is formulated as an MINLP problem. To tackle the high computational complexity of solving MINLP, linearize it into an MILP problem. Through extensive experiments, show that joint-optimization solution has substantial advantage over the approach by two-step separate optimization.through extensive 29

5 numerical studies, it show the high efficiency of proposed joint-optimization based algorithm. This to be enhanced using Coupling Genetic Algorithm with a Grid Search Method to Solve Mixed Integer Nonlinear Programming Problems. REFERENCES [1]J.Dean and S.Ghemawat, Mapreduce: simplified data processing on large clusters, Communications of the ACM, vol. 51, no. 1, pp , [2] S. Gunduz and M. Ozsu, A poisson model for user accesses to web pages, in Computer and Information Sciences - ISCIS 2003, ser. Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2003, vol. 2869, pp [3] B.L.HongXu,ChenFeng, Temperature Aware Workload Management in Geo-distributed Datacenters, in Proceeding of International Conferences on Measurement and Modelling of Computer Systems (SIGMETRICS).ACM, 2013, pp [4] shortest path routing [5] Lin Gu, DezeZeng Cost Minimization for Big Data Processing in Geo-Distributed Data Centers, Member, IEEE, Peng Li, Member, IEEE and Song Guo, Senior Member, IEEE /TETC , [6] Z.Liu, M.Lin, A.Wierman, S.H.Low, and L.L. Andrew, Greening Geographical Load Balancing, in Proceedings of International Conference on Measurement and Modelling of Computer Systems (SIGMETRICS).ACM, 2011, pp [7] Z. Liu, Y. Chen, C. Bash, A. Wierman, D. Gmach, Z. Wang, M. Marwah, and C. Hyser, Renewable and Cooling Aware Workload Management for Sustainable Data Centers, in Proceedings of International Conference on Measurement and Modeling of Computer Systems (SIGMETRICS). ACM, 2012, pp [8] I.Marshall and C.Roadknight, ss, Computer Networks and ISDN Systems, vol.30, no.223, pp , [9] R. Raghavendra, P. Ranganathan, V. Talwar, Z. Wang, and X. Zhu, No Power Struggles: Coordinated Multi-level Power Management for the Data Center, in Proceedings of the 13th International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS). ACM, 2008, pp [10] M. Sathiamoorthy, M. Asteris, D. Papailiopoulos, A. G. Dimakis, R. Vadali, S. Chen, and D. Borthakur, Xoring elephants: novel erasure codes for big data, in Proceedings of the 39th international conference on Very Large Data Bases, ser. PVLDB 13. VLDB Endowment, 2013, pp [11]A.Qureshi,R.Weber,H.Balakrishnan,J.Guttang,an d B.Maggs, Cutting the Electric Bill for Internetscale Systems, in Proceedings of the ACM Special Interest Group on Data Communication (SIGCOMM).ACM,2009,pp [12]R.Urgaonkar, B.Urgaonkar, M.J.Neely, and A.Sivasubramaniam, Optimal Power Cost Management Using Stored Energy in Data Centers, in Proceeding of International Conferences on Measurement and Modelling of Computer Systems (SIGMETRICS).ACM, 2011, pp

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