Rethinking multicore application scalability on big virtual machines

Document Type

Conference Proceeding

Publication Date

7-2-2017

Abstract

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.

Identifier

85048373200 (Scopus)

ISBN

[9781538621295]

Publication Title

Proceedings of the International Conference on Parallel and Distributed Systems ICPADS

External Full Text Location

https://doi.org/10.1109/ICPADS.2017.00094

ISSN

15219097

First Page

694

Last Page

701

Volume

2017-December

Grant

CNS 1409523

Fund Ref

National Science Foundation

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