The paper “Semantics-aware Virtual Machine Image Management in IaaS Clouds” has been accepted for publication at 33rd IEEE International Parallel & Distributed Processing Symposium , IPDPS 2019 to be held at Rio de Janerio, Brazil (May 20-24 2019).

Authors: Nishant Saurabh (Alpen-Adria Universität Klagenfurt), Julian Remmers (University of Innsbruck), Dragi Kimovski (Alpen-Adria Universität Klagenfurt), Radu Prodan (Alpen-Adria Universität Klagenfurt), Jorge G. Barbosa (LIACC, Faculdade de Engenharia da Universidade do Porto).

Abstract: 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 require dealing with multiple VMIs, typically in the magnitude of gigabytes, which entails VMI sprawl issues hindering the elastic resource management and provisioning. Nevertheless, 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 publish and retrieval overheads. In this paper, we design, implement and evaluate Expelliarmus, a novel VMI management system that helps to minimize storage, publish and retrieval overheads. To achieve this goal, Expelliarmus incorporates three complementary features. First, it makes use of VMIs modelled as semantic graphs to expedite the similarity computation between multiple VMIs. Second, Expelliarmus provides a semantic aware VMI decomposition and base image selection to extract and store non-redundant base image and software packages. Third, Expelliarmus can also assemble VMIs based on the required software packages upon user request. We evaluate Expelliarmus through a representative set of synthetic Cloud VMIs on the real test-bed. Experimental results show that our semantic-centric approach is able to optimize repository size by 2.2-16 times compared to state-of-the-art systems (e.g. IBM’s Mirage and Hemera) with significant VMI publish and retrieval performance improvement.