Infrastructure-as-a-service (IaaS) Clouds concurrently accommodate diverse sets of user requests, requiring an efficient strategy for storing and retrieving virtual machine images (VMIs) at a large scale. The VMI storage management requires dealing with multiple VMIs, typically in the magnitude of gigabytes, which entails VMI sprawl issues hindering the elastic resource management and provisioning. Unfortunately, existing techniques to facilitate VMI management overlook VMI semantics (i.e at the level of base image and software packages), with either restricted possibility to identify and extract reusable functionalities or with higher VMI publishing and retrieval overheads. In this paper, we propose Expelliarmus, a novel VMI management system that helps to minimize VMI storage, publishing and retrieval overheads. To achieve this goal, Expelliarmus incorporates three complementary features. First, it models VMIs as semantic graphs to facilitate their similarity computation. Second, it provides a semantically-aware VMI decomposition and base image selection to extract and store non-redundant base image and software packages. Third, it assembles VMIs based on the required software packages upon user request. We evaluate Expelliarmus through a representative set of synthetic Cloud VMIs on a real test-bed. Experimental results show that our semantic-centric approach is able to optimize the repository size by $2.3-22$ times compared to state-of-the-art systems (e.g. IBM’s Mirage and Hemera) with significant VMI publishing and slight retrieval performance improvement.

基础架构即服务（IaaS）云可同时容纳各种用户请求，因此需要一种有效的策略来大规模存储和检索虚拟机映像（VMI）。VMI存储管理需要处理多个VMI，通常以千兆字节为单位，这带来了VMI扩展问题，从而阻碍了弹性资源管理和供应。不幸的是，现有的促进VMI管理的技术忽略了VMI语义（即，在基本映像和软件包的级别上），从而无法识别和提取可重用功能，或者具有更高的VMI发布和检索开销。在本文中，我们提出了Expelliarmus，这是一种新颖的VMI管理系统，有助于最小化VMI的存储，发布和检索开销。为了实现这个目标，Expelliarmus具有三个互补功能。首先，它将VMI建模为语义图，以促进其相似度计算。其次，它提供了语义感知的VMI分解和基本映像选择，以提取和存储非冗余基本映像和软件包。第三，它根据用户要求根据所需的软件包组装VMI。我们通过在真实的测试平台上通过一组代表性的合成Cloud VMI评估Expelliarmus。实验结果表明，我们以语义为中心的方法能够通过以下方式优化存储库大小：它提供了语义感知的VMI分解和基本映像选择，以提取和存储非冗余基本映像和软件包。第三，它根据用户要求根据所需的软件包组装VMI。我们通过在实际测试平台上通过一组代表性的合成Cloud VMI评估Expelliarmus。实验结果表明，我们以语义为中心的方法能够通过以下方式优化存储库大小：它提供了语义感知的VMI分解和基本映像选择，以提取和存储非冗余基本映像和软件包。第三，它根据用户要求根据所需的软件包组装VMI。我们通过在实际测试平台上通过一组代表性的合成Cloud VMI评估Expelliarmus。实验结果表明，我们以语义为中心的方法能够通过以下方式优化存储库大小：$2。3-22$ 与最先进的系统（例如IBM的Mirage和Hemera）相比，具有显着的VMI发布，并且检索性能略有提高。