Flexible service consolidation with nested virtualization and library operating systems

作     者：Kourai, Kenichi Sannomiya, Kouta 

作者机构：Kyushu Inst Technol Dept Comp Sci & Networks 680-4 Kawazu Iizuka Fukuoka 8208502 Japan 

出 版 物：《SOFTWARE-PRACTICE & EXPERIENCE》 (软件:实践与经验)

年 卷 期：2020年第50卷第1期

页      面：3-21页

核心收录：

学科分类：08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：19304 

主　　题：IaaS clouds library operating system nested virtualization process pool service consolidation virtual private network 

摘      要：In Infrastructure-as-a-service (IaaS) clouds, users can reduce costs by scale-in or scale-down when running services are underutilized. Since these optimizations of instance deployment require at least one minimum instance even for running an underutilized service, cost reduction is limited. For further optimization, multiple services can be consolidated into one instance. However, services have to be stopped temporarily at the consolidation time, and isolation between services becomes weaker after the consolidation. To solve these problems, this paper proposes FlexCapsule, which enables seamless and secure service consolidation in existing IaaS clouds. FlexCapsule runs each service in a lightweight virtual machine (VM) called an app VM, using a library operating system. An app VM runs inside an instance using a technique called nested virtualization. FlexCapsule can optimize instance deployment with negligible downtime by flexibly migrating app VMs. Due to strong isolation provided by app VMs, it can guarantee security between consolidated services. In addition, FlexCapsule provides multiprocess support using app VMs by emulating process fork and process pools. We have implemented FlexCapsule in Xen using both fully virtualized OSv and paravirtualized MiniOS. Then, we examined the effectiveness of FlexCapsule using several applications. Due to the premature implementation of nested virtualization in Xen, the performance of app VMs largely degraded, but we believe that the performance could be improved using several existing optimizations.