Abstract-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 unpractical to administer with SQL databases due to lack of structure, high scalability, and elasticity needs. NoSQL data stores such as MongoDB and Cassandra provide a desirable platform for fast and efficient data queries. This leads to increased importance in areas such as cloud applications, e-commerce, social media, bio-informatics, and materials science. In an effort to combine the querying capabilities of conventional database systems and the processing power of the MapReduce model, this paper presents a thorough evaluation of the Cassandra NoSQL database when used in conjunction with the Hadoop MapReduce engine. We characterize the performance for a wide range of representative use cases, and then compare, contrast, and evaluate so that application developers can make informed decisions based upon data size, cluster size, replication factor, and partitioning strategy to meet their performance needs.

Abstract-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: MapReduce's best known implementation, Apache Hadoop, only offers native support for Java applications. While Hadoop streaming supports applications compiled in a variety of languages such as C, C++, Python and FORTRAN, streaming has shown to be a less efficient MapReduce alternative in terms of performance, and effectiveness. Additionally, Hadoop streaming offers lesser options than its native counterpart, and as such offers less flexibility along with a limited array of features for scientific software. The Hadoop File System (HDFS), a central pillar of Apache Hadoop is not a POSIX compliant file system. In this paper, we present an alternative framework to Hadoop streaming to address the needs of scientific applications: MARISSA (MApReduce Implementation for Streaming Science Applications). We describe MARISSA's design and explain how it expands the scientific applications that can benefit from the MapReduce model. We also compare and explain the performance gains of MARISSA over Hadoop streaming.

Abstract-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. We show the disparity in performance between homogeneous and heterogeneous clusters in this paper to be high. Subsequently, we present MARLA, a MapReduce framework capable of performing well not only in homogeneous settings, but also when the cluster exhibits heterogeneous properties. We address the problems associated with existing MapReduce implementations affecting cluster heterogeneity, and subsequently present through MARLA the components and trade-offs necessary for better MapReduce performance in heterogeneous cluster and cloud environments. We quantify the performance gains exhibited by our approach against Apache Hadoop and MARIANE in data intensive and compute intensive applications.

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Abstract-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 node. We begin by considering that no cluster is truly homogeneous with respect to energy consumption. From there we develop a MapReduce framework that can evaluate the current status of each node and dynamically react to estimated power usage. In so doing, we shift power consumption work toward more energy efficient nodes which are currently consuming less power. Our work shows that given an ideal framework configuration, certain nodes may consume only 62.3% of the dynamic power they consumed when the same framework was configured as it would be in a traditional MapReduce implementation.