TY - GEN
T1 - Rethinking multicore application scalability on big virtual machines
AU - Shan, Jianchen
AU - Jia, Weiwei
AU - Ding, Xiaoning
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/2
Y1 - 2017/7/2
N2 - Virtual machine (VM) sizes keep increasing in the cloud. However, little attention has been paid to analyze and understand the scalability of multicore applications on big VMs with multiple virtual CPUs (VCPUs), assuming that application scalability on VMs can be analyzed in the same ways as that on physical machines (PMs). The paper demonstrates that, since hardware CPU resource is dynamically allocated to VCPUs, the executions of multicore applications on VMs show different scalability from those on PMs. The paper systematically studies how the virtualization of CPU resource changes execution scalability, identifies key application features and system factors that affect execution scalability on VMs, and investigates possible directions to improve scalability. The paper presents a few important findings. First, the execution scalability of applications on VMs is determined by different factors than those on PMs. Second, virtualization and resource sharing can improve scalability by nature. Thus, applications may show better scalability on VMs than on PMs. Linear scalability can be achieved even when there is substantial sequential computation. Third, there is still much space to further improve execution scalability by enhancing system designs. Better scalability can be achieved by increasing allocation period length and/or matching resource allocation and workload distribution.
AB - Virtual machine (VM) sizes keep increasing in the cloud. However, little attention has been paid to analyze and understand the scalability of multicore applications on big VMs with multiple virtual CPUs (VCPUs), assuming that application scalability on VMs can be analyzed in the same ways as that on physical machines (PMs). The paper demonstrates that, since hardware CPU resource is dynamically allocated to VCPUs, the executions of multicore applications on VMs show different scalability from those on PMs. The paper systematically studies how the virtualization of CPU resource changes execution scalability, identifies key application features and system factors that affect execution scalability on VMs, and investigates possible directions to improve scalability. The paper presents a few important findings. First, the execution scalability of applications on VMs is determined by different factors than those on PMs. Second, virtualization and resource sharing can improve scalability by nature. Thus, applications may show better scalability on VMs than on PMs. Linear scalability can be achieved even when there is substantial sequential computation. Third, there is still much space to further improve execution scalability by enhancing system designs. Better scalability can be achieved by increasing allocation period length and/or matching resource allocation and workload distribution.
KW - Cloud computing
KW - Multicore
KW - Resource sharing
KW - Scalability
KW - Virtual machine
UR - http://www.scopus.com/inward/record.url?scp=85048373200&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85048373200&partnerID=8YFLogxK
U2 - 10.1109/ICPADS.2017.00094
DO - 10.1109/ICPADS.2017.00094
M3 - Conference contribution
AN - SCOPUS:85048373200
T3 - Proceedings of the International Conference on Parallel and Distributed Systems - ICPADS
SP - 694
EP - 701
BT - Proceedings - 2017 IEEE 23rd International Conference on Parallel and Distributed Systems, ICPADS 2017
PB - IEEE Computer Society
T2 - 23rd IEEE International Conference on Parallel and Distributed Systems, ICPADS 2017
Y2 - 15 December 2017 through 17 December 2017
ER -