Improving performance on data-intensive applications using a load balancing methodology based on divisible load theory

Keyphrases

• Data-intensive Applications 100%
• Load Balancing 100%
• Performance Improvement 100%
• Divisible Load Theory 100%
• Data Partitioning 66%
• Data Chunk 66%
• Processing Node 66%
• Application-oriented 33%
• Performance Problems 33%
• Bioinformatics Tools 33%
• Query Analysis 33%
• Homogeneous Cluster 33%
• Processing Time 33%
• Tuning Parameter 33%
• Total Execution Time 33%
• Inefficient Use 33%
• Partitioned Data 33%
• Partitioning Factor 33%
• Number of Data 33%
• Load Imbalance 33%
• Very Large Datasets 33%

Computer Science

• Load Balancing 100%
• Divisible Load Theory 100%
• data-intensive application 100%
• Data Partition 66%
• Processing Node 66%
• Performance Problem 33%
• Large Data Set 33%
• Homogeneous Cluster 33%
• Total Execution Time 33%
• Partitioned Data 33%
• Application Condition 33%
• Load Imbalance 33%
• Reported Result 33%
• Processing Time 33%
• Application Execution 33%
• Bioinformatics 33%

Engineering

• Nodes 100%
• Execution Time 50%
• Processing Time 50%
• Bioinformatics 50%