Thumbnail
Access Restriction
Open

Author Kim, Hyeong S. ♦ In, Dong ♦ Young, Shin ♦ Yu, Jin ♦ Eom, Hyeonsang ♦ Yeom, Heon Y.
Source CiteSeerX
Content type Text
File Format PDF
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Architectural Paradigm Shift ♦ Substitution Complement ♦ Data Loss ♦ Low Power Computer ♦ Towards Energy Proportional Cloud ♦ Cloud Application ♦ Energy Con-sumption ♦ Data Center ♦ Energy Efficiency ♦ Apache Hadoop ♦ Low Power Machine ♦ Data Processing Framework ♦ Data Intensive Job ♦ Cloud Computing ♦ Data Center Level ♦ Sev-eral Effort ♦ Cloud Sys-tems ♦ Extensive Study ♦ Overall Power Consumption
Description Energy efficiency in cloud computing is becoming more and more important for IT operators of data centers. Sev-eral effort to use low power machines in the data center level has been explored. Also, data processing frame-works such as MapReduce and Hadoop are frequently used to process data intensive jobs. However, there have not been an extensive study on the impact of low power computers on such data processing frameworks. Actu-ally, development of low power computers is demanding the architectural paradigm shift for cloud applications. In this paper, we evaluate Apache Hadoop on low power machines and study the feasibility of them in cloud sys-tems. We also propose AnSwer (Augmentation and Sub-stitution), an energy saving method to reduce energy con-sumption by introducing low power machines. In An-Swer, augmentation and substitution complement each other to prevent data loss and to improve overall power consumption. 1
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study
Learning Resource Type Article
Publisher Institution in Proceedings of the First USENIX conference on Sustainable information technology, ser. SustainIT’10