A dynamic block device reconfiguration algorithm in virtual MapReduce cluster

Lee, Kwonyong; Nam, Yoonsung; Kim, Taekhee; Park, Sung-Yong; Lee, Hyuk-Jun; Yang, Jihoon

doi:10.1007/s10586-014-0375-y

Cited by 3 publications

(1 citation statement)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…DRR [19], a dynamic VM reconfiguration technique for data-intensive computing in the cloud, was proposed to adjust the computing capability of individual VMs to improve the data locality for Hadoop running in the cloud. A dynamic block device reconfiguration algorithm was proposed by Kwonyong et al [20] to reduce the data transfer time between VMs and thereby improving the performance of virtual MapReduce clusters in the cloud. Different from these studies, our work attempts to achieve the better data block distribution to improve the data locality and the performance of Hadoop in the cloud.…”

Section: Related Workmentioning

confidence: 99%

LDBAS: Location-aware Data Block Allocation Strategy for HDFS-based Applications in the Cloud

2018

KSII TIIS

View full text Add to dashboard Cite

Big data processing applications have been migrated into cloud gradually, due to the advantages of cloud computing. Hadoop Distributed File System (HDFS) is one of the fundamental support systems for big data processing on MapReduce-like frameworks, such as Hadoop and Spark. Since HDFS is not aware of the co-location of virtual machines in the cloud, the default scheme of block allocation in HDFS does not fit well in the cloud environments behaving in two aspects: data reliability loss and performance degradation. In this paper, we present a novel location-aware data block allocation strategy (LDBAS). LDBAS jointly optimizes data reliability and performance for upper-layer applications by allocating data blocks according to the locations and different processing capacities of virtual nodes in the cloud. We apply LDBAS to two stages of data allocation of HDFS in the cloud (the initial data allocation and data recovery), and design the corresponding algorithms. Finally, we implement LDBAS into an actual Hadoop cluster and evaluate the performance with the benchmark suite BigDataBench. The experimental results show that LDBAS can guarantee the designed data reliability while reducing the job execution time of the I/O-intensive applications in Hadoop by 8.9% on average and up to 11.2% compared with the original Hadoop in the cloud.

show abstract

Section: Related Workmentioning

confidence: 99%