Jessica Hartog

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MapReduce has gradually become the framework of choice for "big data". The MapReduce model allows for efficient and swift processing of large scale data with a cluster of compute nodes. However, the efficiency here comes at a price. The performance of widely used MapReduce implementations such as Hadoop suffers in heterogeneous and load-imbalanced clusters.(More)
—In the last decade, the increased use and growth of social media, unconventional web technologies, and mobile applications, have all encouraged development of a new breed of database models. NoSQL data stores target the unstructured data, which by nature is dynamic and a key focus area for " Big Data " research. New generation data can prove costly and(More)
—MapReduce has since its inception been steadily gaining ground in various scientific disciplines ranging from space exploration to protein folding. The model poses a challenge for a wide range of current and legacy scientific applications for addressing their " Big Data " challenges. For example: MapRe-duce's best known implementation, Apache Hadoop, only(More)
—MapReduce has become a popular framework for Big Data applications. While MapReduce has received much praise for its scalability and efficiency, it has not been thoroughly evaluated for power consumption. Our goal with this paper is to explore the possibility of scheduling in a power-efficient manner without the need for expensive power monitors on every(More)
—When data centers employ the common and economical practice of upgrading subsets of nodes incrementally, rather than replacing or upgrading all nodes at once, they end up with clusters whose nodes have non-uniform processing capability, which we also call performance-heterogeneity. Popular frameworks supporting the effective MapReduce programming model for(More)
MapReduce has become a popular framework for Big Data applications. While MapReduce has received much praise for its scalability and efficiency, it has not been thoroughly evaluated for power consumption. Our goal with this paper is to explore the possibility of scheduling in a power-efficient manner without the need for expensive power monitors on every(More)
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