An Efficient Implementation of Mobile Raspberry Pi Hadoop Clusters for Robust and Augmented Computing Performance
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Rapid advances in science and technology with exponential development of smart mobile devices,workstations, supercomputers, smart gadgets and network servers has been witnessed over the past few years. The sudden increase in the Internet population and manifold growth in internet speeds has occasioned thegeneration of an enormous amount of data, now termed ‘big data’. Given this scenario, storage of data onlocal servers or a personal computer is an issue, which can be resolved by utilizing cloud computing. Atpresent, there are several cloud computing service providers available to resolve the big data issues. This paperestablishes a framework that builds Hadoop clusters on the new single-board computer (SBC) MobileRaspberry Pi. Moreover, these clusters offer facilities for storage as well as computing. Besides the fact that theregular data centers require large amounts of energy for operation, they also need cooling equipment andoccupy prime real estate. However, this energy consumption scenario and the physical space constraints canbe solved by employing a Mobile Raspberry Pi with Hadoop clusters that provides a cost-effective, low-power,high-speed solution along with micro-data center support for big data. Hadoop provides the requiredmodules for the distributed processing of big data by deploying map-reduce programming approaches. In thiswork, the performance of SBC clusters and a single computer were compared. It can be observed from theexperimental data that the SBC clusters exemplify superior performance to a single computer, by around 20%. Furthermore, the cluster processing speed for large volumes of data can be enhanced by escalating thenumber of SBC nodes. Data storage is accomplished by using a Hadoop Distributed File System (HDFS),which offers more flexibility and greater scalability than a single computer system.
Abstract
Rapid advances in science and technology with exponential development of smart mobile devices,workstations, supercomputers, smart gadgets and network servers has been witnessed over the past few years. The sudden increase in the Internet population and manifold growth in internet speeds has occasioned thegeneration of an enormous amount of data, now termed ‘big data’. Given this scenario, storage of data onlocal servers or a personal computer is an issue, which can be resolved by utilizing cloud computing. Atpresent, there are several cloud computing service providers available to resolve the big data issues. This paperestablishes a framework that builds Hadoop clusters on the new single-board computer (SBC) MobileRaspberry Pi. Moreover, these clusters offer facilities for storage as well as computing. Besides the fact that theregular data centers require large amounts of energy for operation, they also need cooling equipment andoccupy prime real estate. However, this energy consumption scenario and the physical space constraints canbe solved by employing a Mobile Raspberry Pi with Hadoop clusters that provides a cost-effective, low-power,high-speed solution along with micro-data center support for big data. Hadoop provides the requiredmodules for the distributed processing of big data by deploying map-reduce programming approaches. In thiswork, the performance of SBC clusters and a single computer were compared. It can be observed from theexperimental data that the SBC clusters exemplify superior performance to a single computer, by around 20%. Furthermore, the cluster processing speed for large volumes of data can be enhanced by escalating thenumber of SBC nodes. Data storage is accomplished by using a Hadoop Distributed File System (HDFS),which offers more flexibility and greater scalability than a single computer system.
참고문헌 (17)
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