Load Scheduling for Bioinformatics Applications in Large Scale Networks

A load scheduling strategy with near-optimal processing time is designed to explore the computational characteristics of DNA sequence alignment algorithms, specifically, the Needleman-Wunsch Algorithm. Following the divisible load scheduling theory, we design an efficient load scheduling strategy to manage such bioinformatics applications in a large-scale network so that the overall processing time of the sequencing tasks is minimized. The row-wise and column-wise partitioning of the workload is adopted in the scheduling strategy. In this study, the load distribution depends on the length of the sequence and number of processors in the network and, the total processing time is also affected by communication link speed. We considered several cases in our study by varying the sequences, communication and computation speeds, and number of processors. Through simulation and numerical analysis, this study demonstrates that for a constant sequence length as the numbers of processors increase in the network the processing time for the job decreases and minimum overall processing time is achieved.Computer Science Departmen