Systems and methods for generating performance prediction model and estimating execution time for applications is provided. The system executes synthetic benchmarks for a first dataset on a first cluster. Each synthetic benchmark includes a MapReduce (MR) job. The system further extracts sensitive parameters for each sub-phase of the MR job, generates a linear regression prediction model for each sub-phase to obtain one or more linear regression prediction models, based on which the system further generates a performance prediction model to be utilized for predicting, using the sensitive parameters, a Hive query execution time of a DAG of one or more MR jobs executed on a second dataset on a second cluster, wherein the first cluster that includes the first dataset is smaller compared to the second cluster that includes the second dataset.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A processor-implemented method, comprising: executing, via one or more hardware processors, one or more synthetic benchmarks on a first dataset on a first cluster comprising a first set of nodes, wherein each of said one or more synthetic benchmarks comprises a MapReduce (MR) job, said MR job comprises a map task and a reduce task; extracting, via said one or more hardware processors, one or more sensitive parameters related to performance of each sub-phase in said MR job, wherein said one or more sensitive parameters comprises at least one of an input size per map task, map selectivity, number of map tasks, number of reduce tasks, record selectivity, concurrent map tasks per node, one or more map waves, and disk resource contention delay; generating, via said one or more hardware processors, a linear regression prediction model using said one or more sensitive parameters to obtain a set of linear regression prediction models; and generating, via said one or more hardware processors, a performance prediction model based on said set of linear regression prediction models generated using said first dataset. 2. The processor-implemented method of claim 1, further comprising: processing, via said one or more hardware processors, a Hive query such that said Hive query is translated into Directed Acyclic Graph (DAG) of one or more MR jobs; executing, via said one or more hardware processors, said one or more MR jobs on a second dataset on a second cluster comprising a second set of nodes; extracting, via said one or more hardware processors, said one or more sensitive parameters from a job execution log obtained upon executing said DAG of said one or more MR jobs; and predicting, using said performance prediction model, a Hive query execution time of said DAG of said one or more MR jobs based on said one or more sensitive parameters from logs obtained for one or more sub-phases of said one or more MR jobs executed on said second dataset. 3. The processor-implemented method of claim 2, wherein said Hive query execution time is predicted based on a summation of estimated execution time of each of said one or more MR jobs in said DAG executed on said second dataset. 4. The processor-implemented method of claim 2, wherein said first cluster comprising said first dataset is smaller as compared to said second cluster comprising said second dataset. 5. The processor-implemented method of claim 2, wherein one or more sub-phases of said one or more MR jobs in said DAG comprises a read time, a map time, a collect time, a spill time, a merge time, a shuffle time, a sort time, and a write time. 6. The processor-implemented method of claim 5, wherein said read time is calculated as a function of size of input data, number of concurrent maps per node, and number of input records. 7. The processor-implemented method of claim 5, wherein said map time is calculated as a function of input records. 8. The processor-implemented method of claim 5, wherein said collect time is calculated as a function of number of concurrent maps per node and number of output records. 9. The processor-implemented method of claim 5, wherein said spill time is calculated as a function of size of map output data, number of concurrent maps per nodes, and number of output records. 10. The processor-implemented method of claim 5, wherein said merge time is calculated as function of size of map output data, number of concurrent maps per nodes, number of map tasks, and number of reduce tasks. 11. The processor-implemented method of claim 5, wherein said shuffle time is calculated as a function of size of map output data, number of concurrent maps per node, number of map tasks and number of reduce tasks. 12. The processor-implemented method of claim 5, wherein said sort time is calculated as a function of size of map output data, number of concurrent maps per node, number of map tasks and number of reduce tasks. 13. The processor-implemented method of claim 5, wherein said write time is calculated as a function of size of reduce output data and number of reduce output records. 14. A processor implemented system comprising: a memory storing instructions; a communication interface; and one or more hardware processors coupled to said memory, wherein said one or more hardware processor are configured by said instructions to: execute one or more synthetic benchmarks on a first dataset on a first cluster comprising a first set of nodes, wherein each of said one or more synthetic benchmarks comprises a MapReduce (MR) job, said MR job comprises a map task and a reduce task, extract one or more sensitive parameters related to performance of each sub-phase in said MR job, wherein said one or more sensitive parameters comprises an input size per map task, map selectivity, number of map tasks, number of reduce tasks, record selectivity, concurrent map tasks per node, one or more map waves, and disk resource contention delay, generate a linear regression prediction model using said one or more sensitive parameters to obtain a set of linear regression prediction models, and generate a performance prediction model based on said set of linear regression prediction models generated using said first dataset. 15. The processor-implemented system of claim 14, wherein said hardware processor is further configured by said instructions to: process a Hive query such that said Hive query is translated into Directed Acyclic Graph (DAG) of one or more MR jobs, execute said DAG of said one or more MR jobs on a second dataset on a second cluster comprising a second set of nodes, extract said one or more sensitive parameters from a job execution log obtained upon executing said DAG of said one or more MR jobs, and predict, using said performance prediction model, a Hive query execution time of said DAG of said one or more MR jobs based on said one or more sensitive parameters from said job execution log obtained upon executing said DAG of said one or more MR jobs on said second dataset. 16. The processor-implemented system of claim 15, wherein said Hive query execution time is predicted based on a summation of estimated execution time of each of said one or more MR jobs in said DAG executed on said second dataset. 17. The processor-implemented system of claim 15, wherein said first cluster comprising said first dataset is smaller as compared to said second cluster comprising said second dataset. 18. One or more non-transitory machine readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors causes: executing, via said one or more hardware processors, one or more synthetic benchmarks on a first dataset on a first cluster comprising a first set of nodes, wherein each of said one or more synthetic benchmarks comprises a MapReduce (MR) job, said MR job comprises a map task and a reduce task; extracting, via said one or more hardware processors, one or more sensitive parameters related to performance of each sub-phase in said MR job, wherein said one or more sensitive parameters comprises at least one of an input size per map task, map selectivity, number of map tasks, number of reduce tasks, record selectivity, concurrent map tasks per node, one or more map waves, and disk resource contention delay; generating, via said one or more hardware processors, a linear regression prediction model using said one or more sensitive parameters to obtain a set of linear regression prediction models; and generating, via said one or more hardware processors, a performance prediction model based on said set of linear regression prediction models generated using said first dataset. 19. The one or more non-transitory machine readable information storage mediums of claim 18, wherein said one or more instructions which when executed by said one or more hardware processors further causes: processing, via said one or more hardware processors, a Hive query such that said Hive query is translated into Directed Acyclic Graph (DAG) of one or more MR jobs; executing, via said one or more hardware processors, said one or more MR jobs on a second dataset on a second cluster comprising a second set of nodes: extracting, via said one or more hardware processors, said one or more sensitive parameters from a job execution log obtained upon executing said DAG of said one or more MR jobs; and predicting, using said performance prediction model, a Hive query execution time of said DAG of said one or more MR jobs based on said one or more sensitive parameters from logs obtained for one or more sub-phases of said one or more MR jobs executed on said second dataset. 20. The one or more non-transitory machine readable information storage mediums of claim 19, wherein said first cluster comprising said first dataset is smaller as compared to said second cluster comprising said second dataset.