Usable Disk Space Control Based on Hadoop Job Features Hadoop is an opensource platform for big data processing. In the case of analyzing extremely big data, hard disk drives are usually used. Hard disk drives have different performance depending on where data is placed on the disk. In other published work, an approach for improving Hadoop IO performance by using features of Hadoop jobs to decide on the location of data placement was proposed. This approach separates the entire hard disk drive space into two areas, which are outer and inner areas. However, this approach did not actively utilize the fastest available zones in each area. In this paper, we propose a new method for improving this method by active usage of the fastest available zones in each area. Our evaluation of the proposed method with a popular Hadoop benchmark shows that the new method can improve job performance by 9% when compared with existing approach.

Automating Distributed Tiered Storage Management in Cluster Computing Dataintensive platforms such as Hadoop and Spark are routinely used to process massive amounts of data residing on distributed file systems like HDFS. Increasing memory sizes and new hardware technologies (e.g., NVRAM, SSDs) have recently led to the introduction of storage tiering in such settings. However, users are now burdened with the additional complexity of managing the multiple storage tiers and the data residing on them while trying to optimize their workloads. In this paper, we develop a general framework for automatically moving data across the available storage tiers in distributed file systems. Moreover, we employ machine learning for tracking and predicting file access patterns, which we use to decide when and which data to move up or down the storage tiers for increasing system performance. Our approach uses incremental learning to dynamically refine the models with new file accesses, allowing them to naturally adjust and adapt to workload changes over time. Our extensive evaluation using realistic workloads derived from Facebook and CMU traces compares our approach with several other policies and showcases significant benefits in terms of both workload performance and cluster efficiency.