Towards Energy Proportional Cloud for Data Processing Frameworks

Energy efficiency in cloud computing is becoming more and more important for IT operators of data centers. Several effort to use low power machines in the data center level has been explored. Also, data processing frameworks 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. Actually, 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 systems. We also propose AnSwer (Augmentation and Substitution), an energy saving method to reduce energy consumption by introducing low power machines. In An-Swer, augmentation and substitution complement each other to prevent data loss and to improve overall power consumption.