TY - GEN
T1 - Performance implications from sizing a VM on multi-core systems
T2 - 37th Annual IEEE Computer Software and Applications Conference, COMPSAC 2013
AU - Lim, Seung Hwan
AU - Horey, James
AU - Yao, Yanjun
AU - Begoli, Edmon
AU - Cao, Qing
PY - 2013
Y1 - 2013
N2 - In this paper, we present a quantitative performance analysis of data analytics applications running on multi-core virtual machines. Such environments form the core of cloud computing. In addition, data analytics applications, such as Cassandra and Hadoop, are becoming increasingly popular on cloud computing platforms. This convergence necessitates a better understanding of the performance and cost implications of such hybrid systems. For example, the very first step in hosting applications in virtualized environments, requires the user to configure the number of virtual processors and the size of memory. To understand performance implications of this step, we benchmarked three Yahoo Cloud Serving Benchmark(YCSB) workloads in a virtualized multi-core environment. Our measurements indicate that the performance of Cassandra for YCSB workloads does not heavily depend on the processing capacity of a system, while the size of the data set is critical to performance relative to allocated memory. We also identified a strong relationship between the running time of workloads and various hardware events (last level cache loads, misses, and CPU migrations). From this analysis, we provide several suggestions to improve the performance of data analytics applications running on cloud computing environments.
AB - In this paper, we present a quantitative performance analysis of data analytics applications running on multi-core virtual machines. Such environments form the core of cloud computing. In addition, data analytics applications, such as Cassandra and Hadoop, are becoming increasingly popular on cloud computing platforms. This convergence necessitates a better understanding of the performance and cost implications of such hybrid systems. For example, the very first step in hosting applications in virtualized environments, requires the user to configure the number of virtual processors and the size of memory. To understand performance implications of this step, we benchmarked three Yahoo Cloud Serving Benchmark(YCSB) workloads in a virtualized multi-core environment. Our measurements indicate that the performance of Cassandra for YCSB workloads does not heavily depend on the processing capacity of a system, while the size of the data set is critical to performance relative to allocated memory. We also identified a strong relationship between the running time of workloads and various hardware events (last level cache loads, misses, and CPU migrations). From this analysis, we provide several suggestions to improve the performance of data analytics applications running on cloud computing environments.
UR - https://www.scopus.com/pages/publications/84899720086
U2 - 10.1109/IPDPSW.2013.97
DO - 10.1109/IPDPSW.2013.97
M3 - Conference contribution
AN - SCOPUS:84899720086
SN - 9780769549798
T3 - Proceedings - IEEE 27th International Parallel and Distributed Processing Symposium Workshops and PhD Forum, IPDPSW 2013
SP - 1001
EP - 1008
BT - Proceedings - IEEE 27th International Parallel and Distributed Processing Symposium Workshops and PhD Forum, IPDPSW 2013
PB - IEEE Computer Society
Y2 - 20 May 2013 through 24 May 2013
ER -