Scheduling in Mapreduce Clusters

MapReduce is a framework proposed by Google for processing huge amounts of data in a distributed environment. The simplicity of the programming model and the fault-tolerance feature of the framework make it very popular in Big Data processing. As MapReduce clusters get popular, their scheduling becomes increasingly important. On one hand, many MapReduce applications have high performance requirements, for example, on response time and/or throughput. On the other hand, with the increasing size of MapReduce clusters, the energy-efficient scheduling of MapReduce clusters becomes inevitable. These scheduling challenges, however, have not been systematically studied. The objective of this dissertation is to provide MapReduce applications with low cost and energy consumption through the development of scheduling theory and algorithms, energy models, and energy-aware resource management. In particular, we will investigate energy-efficient scheduling in hybrid CPU-GPU MapReduce clusters. This research work is expected to have a breakthrough in Big Data processing, particularly in providing green computing to Big Data applications such as social network analysis, medical care data mining, and financial fraud detection. The tools we propose to develop are expected to increase utilization and reduce energy consumption for MapReduce clusters. In this PhD dissertation, we propose to address the aforementioned challenges by investigating and developing 1) a match-making scheduling algorithm for improving the data locality of Map- Reduce applications, 2) a real-time scheduling algorithm for heterogeneous Map- Reduce clusters, and 3) an energy-efficient scheduler for hybrid CPU-GPU Map- Reduce cluster. Advisers: Ying Lu and David Swanso

Algorithms for Extracting Frequent Episodes in the Process of Temporal Data Mining

An important aspect in the data mining process is the discovery of patterns having a great influence on the studied problem. The purpose of this paper is to study the frequent episodes data mining through the use of parallel pattern discovery algorithms. Parallel pattern discovery algorithms offer better performance and scalability, so they are of a great interest for the data mining research community. In the following, there will be highlighted some parallel and distributed frequent pattern mining algorithms on various platforms and it will also be presented a comparative study of their main features. The study takes into account the new possibilities that arise along with the emerging novel Compute Unified Device Architecture from the latest generation of graphics processing units. Based on their high performance, low cost and the increasing number of features offered, GPU processors are viable solutions for an optimal implementation of frequent pattern mining algorithmsFrequent Pattern Mining, Parallel Computing, Dynamic Load Balancing, Temporal Data Mining, CUDA, GPU, Fermi, Thread