Eager: Collaborative Research: DiRecMR: Reconciling the Dichotomy of MapReduce for Efficient Speculation and Resilience

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MapReduce systems have great capabilities in processing large amounts of data and have become a research target for governmental, academic and industrial organizations. However, their task management and fault handling policies do not recognize a tacit dichotomy that exists between its inherent two phases (map and reduce). This results in a number of critical issues, such as resource underutilization, prolonged task execution, myopic speculation, and failure amplifications. This project adopts a transformative combination of theoretical analysis, simulation and modeling, and systems design and implementation approaches in order to reconcile the dichotomy of MapReduce. The techniques from this project are potentially impactful to all organizations that deploy MapReduce systems and support Big Data applications from business analytics, social networks, and scientific computing research.Instead of empirical analysis of system behaviors to pinpoint resource management and task scheduling abnormalities, this project takes a different perspective on MapReduce efficiency and resilience, and formulates a Markov chain for the transition of Hadoop MapReduce containers, and a fork-join model for the queueing of map and reduce tasks. These formulations facilitate a theoretical analysis of the dichotomy of MapReduce and help shed light on its impact to asymptotic behaviors of large-scale workloads. This project aims to blend simulation and real system development together, and addresses the myopic speculation caused by dichotomy, liberates the scope of task speculation, and ensures task resilience without failure amplifications. These techniques are developed to enhance MapReduce platforms such as YARN and Spark. Besides the target on MapReduce systems, the research from this project addresses a general issue in distributed analytics environments.