User:Nouman.akhtar04

DYNAMIC MR - A SLOT OPTIMIZATION FRAMEWORK FOR MAPREDUCE CLUSTERS A report submitted to M S RAMAIAH INSTITUTE OF TECHNOLOGY Bengaluru for ful�lling the requirement of Bachelor of Engineering (B.E) in Information Science and Engineering by Akshay Raj ( USN- 1MS11IS009 ) Deepak Gahlot ( USN- 1MS11IS026 ) Sairaj Bilgundi ( USN- 1MS11IS097 ) Sukesh Kumar ( USN- 1MS11IS114 ) under the guidance of Dr.Megha.P.Arakeri Abstract For large data processing in the cloud map reduce is a process we can split the data into multiple parts or make it into the slot and then process and mapping process will happen .the slot based map reduce is not too e�ective it gives the poor performance because of the unop- timized resource allocation and they have the various challenges. The map reduce job task execution have the two unique feature. The map slot allocation only allocate the map task and reduce task only be allocated to reduce task and the map task process before the reduce task. The data locality maximization for the e�ciency and utiliza- tion is required to improve the quality of the system proposed the various challenge to address this problem.The DyanamicMR is a dy- namic slot allocation framework to improve the performance of map reduce. The DyanamicMR focuses on Hadoop fair scheduler(HFS). The Dynamic scheduler consist of three optimization techniques Dy- namic Hadoop slot Allocation(DHAS),Soeculative Execution perfor- mance Balancing(SEPB) and Slot Prescheduling.