For the Microsoft Azure platform, SparkBLAST outperforms CloudBlast on all scenarios. Both datasets (Buz.fasta and Ber.fasta) were processed, and results are presented in Fig. 6 (speedup), Fig. 7 (total execution time), Fig. 8 (Efficiency), Table 5 (Buz.fasta), and Table 6 (Ber.fasta). It is worth noting that the largest dataset (Ber.fasta - 11 GB) was larger than the available memory in the computing nodes. For this reason, CloudBLAST could not process the Ber.fasta dataset, while SparkBLAST does not have this limitation. It is also worth mentioning that larger speedups were achieved on Microsoft Azure when compared to the Google Cloud. This can be partially explained by the fact that all computing nodes allocated on the Microsoft Azure are placed in the same location, while computing nodes on Google Cloud were distributed among 4 different locations. Fig. 6Speedup - Microsoft Azure Fig. 7Total execution time for CloudBLAST x SparkBLAST on Microsoft Azure Fig. 8Efficiency - CloudBLAST x SparkBLAST - Microsoft Azure Table 5Mean execution times, speedups and parallel efficiency (Experiment 2 - Buz.fasta - 805 MB) - SparkBLAST vs CloudBLAST - Microsoft Azure# cores4122860124252SparkBLAST143,228.9547,031.6224,850.5111,692.456,041.643,138.64Speedup3.8311.6722.0946.9590.86174.89Efficiency0.960.970.790.780.730.69CloudBLAST148,512.9547,950.0526,858.7111,951.116,993.523,879.06Speedup3.711.4520.4445.9378.49141.51Efficiency0.920.950.730.770.630.56 Table 6Mean execution times, speedups and parallel efficiency (Experiment 2 - Ber.fasta - 11 GB) - SparkBLAST vs CloudBLAST - Microsoft Azure# cores4122860124252SparkBLAST2,678,902.06859,687.13458,759.75224,869.12110,222.9856,200.21Speedup3.7311.6121.7644.490.57177.64Efficiency0.930.970.780.740.730.7CloudBLAST------Speedup------Efficiency------