Frog leap algorithm for homology modelling in grid environment

This research work shows how homology modelling works for protein sequences in grid environment based on the relevance match from the database. In First Come First Served FCFS strategy, the protein sequence is scheduled to the resource on FCFS order and processed until completion. On average it takes more time to search sequences and users may wait a long time for results. To overcome this, FCFS is implemented in grid. Here the time taken to process the sequences gets reduced, but the inputs are processed one by one. To overcome the maximum time execution and users' waiting time, Frog Leap Algorithm has been used for grid scheduling. The scheduling is done based on the size of the protein sequence. The system that takes the minimum time to process the particular protein sequence is found out initially in grid, through which the time complexity for scheduling has been improved.

[1]  Ian Foster,et al.  The Grid 2 - Blueprint for a New Computing Infrastructure, Second Edition , 1998, The Grid 2, 2nd Edition.

[2]  Mateo Valero,et al.  Linear programming based parallel job scheduling for power constrained systems , 2011, 2011 International Conference on High Performance Computing & Simulation.

[3]  Cristina Boeres,et al.  Distributed and dynamic self‐scheduling of parallel MPI Grid applications , 2007, Concurr. Comput. Pract. Exp..

[4]  K. Vivekanandan,et al.  Bacteria foraging optimization for protein sequence analysis on the grid , 2012, Future Gener. Comput. Syst..

[5]  Suyu Mei,et al.  Homology based Multi-instance Kernel combination for Gram-negative protein subcelluar localization , 2010, 2010 International Conference on Bioinformatics and Biomedical Technology.

[6]  M. R. Islam,et al.  Dynamic scheduling approach for data-intensive cloud environment , 2012, 2012 International Conference on Cloud Computing Technologies, Applications and Management (ICCCTAM).

[7]  Maozhen Li,et al.  The grid - core technologies , 2005 .

[8]  Abdul Hanan Abdullah,et al.  Genetic algorithm in grid scheduling with multiple objectives , 2006 .

[9]  A. Williamson.  Creating a structural genomics consortium , 2000, Nature Structural Biology.

[10]  D. Ramyachitra,et al.  Architectural Frame Work for Homology Modeling in Grid Environment , 2013 .

[11]  Jinkyu Lee,et al.  EDZL Schedulability Analysis in Real-Time Multicore Scheduling , 2013, IEEE Transactions on Software Engineering.

[12]  Č. Venclovas,et al.  Comparative modeling in CASP6 using consensus approach to template selection, sequence‐structure alignment, and structure assessment , 2005, Proteins.

[13]  M. H. Kashani,et al.  A novel method for task scheduling in distributed systems using Max-Min Ant Colony Optimization , 2011, 2011 3rd International Conference on Advanced Computer Control.

[14]  Dr.G.S. Raju,et al.  A Study on Applications of Grid Computing in Bioinformatics , 2010 .

[15]  Muzaffar Eusuff,et al.  Shuffled frog-leaping algorithm: a memetic meta-heuristic for discrete optimization , 2006 .

[16]  Srimat T. Chakradhar,et al.  ValuePack: Value-based scheduling framework for CPU-GPU clusters , 2012, 2012 International Conference for High Performance Computing, Networking, Storage and Analysis.

[17]  Donald E. Grierson,et al.  A modified shuffled frog-leaping optimization algorithm: applications to project management , 2007 .

[18]  R Leplae,et al.  Analysis and assessment of comparative modeling predictions in CASP4 , 2001, Proteins.

[19]  Yang Zhang,et al.  The protein structure prediction problem could be solved using the current PDB library. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[20]  Sandro Zappatore,et al.  Grid Enabled Remote Instrumentation , 2008 .

[21]  S. Kumar,et al.  A Cpu scheduling based on multi criteria with the help of evolutionary algorithm , 2012, 2012 2nd IEEE International Conference on Parallel, Distributed and Grid Computing.

[22]  K. Hemant Kumar Reddy,et al.  A hierarchical load balancing algorithm for efficient job scheduling in a computational grid testbed , 2012, 2012 1st International Conference on Recent Advances in Information Technology (RAIT).

[23]  A. Sali,et al.  Comparative protein structure modeling of genes and genomes. , 2000, Annual review of biophysics and biomolecular structure.

[24]  Zhuoqun Xu,et al.  A Distributed Parallel Computing Environment for Bioinformatics Problems , 2007, Sixth International Conference on Grid and Cooperative Computing (GCC 2007).

[25]  Yi Shen,et al.  Protein Fold Recognition and Remote Homology Detection Based on Profile-Level Building Blocks , 2010, 2010 International Conference on Biomedical Engineering and Computer Science.

[26]  Evgenia Smirni,et al.  ASIdE: Using Autocorrelation-Based Size Estimation for Scheduling Bursty Workloads , 2012, IEEE Transactions on Network and Service Management.

[27]  S. Suuriniemi,et al.  Powerful heuristics make computational homology viable , 2010, Digests of the 2010 14th Biennial IEEE Conference on Electromagnetic Field Computation.