Distributed Parallelizability Analysis and Optimization of Legacy Code in Cloud Migration.

More and more organizations plan to migrate their legacy systems to cloud so as to improve the efficiency of data processing. In order to take full advantage of the parallel virtue of cloud computing, legacy system need to be refactored according to the cloud computing program model. Before that, the parallelizability analysis is the first thing to do. This paper first proposes Distributed parallelizability of legacy code in the process of refactoring from Legacy code to Mapreduce code (DPLM), and then derives four parallel-determinable features according to the rule: data dependency, continuous dependency, non-homology and randomness. Then algorithms are developed to detect parallel-determinable features. Only the legacy code which is not satisfied with all the four-point features can be refactored to parallelizable MapReduce code. However, through practice, the situation is discovered that there are some type of legacy codes canu0027t take advantage of the parallelism of MapReduce. To solve this problem, parallel-determinable features is divided into strong and weak features. Weak features can be resolved by reorganizing source input file. Partial strong features can be resolved by iterative grading. Finally, the experiments show the validity of DPLM and optimization method, parallel-determinable features and optimization methods.