Systems and Networking

Abstract

MapReduce is a more and more popular distributed computing framework, especially in largescale data analysis. Although it has been adopted in many places, a theoretical analysis of itsbehavior is lacking. This thesis introduces an analytical model for MapReduce with three parts,the average task performance, the random behavior and the waiting time. This model is thenvalidated using measured data from three categories of workloads. The model's usefulness isdemonstrated by three optimization processes, which give reasonable conclusions yet differentfrom current understandings.