The FOG just moved from the Lake Wörthersee to ITEC ;)! Lead researchers Dragi Kimovski, and Narges Mehran from Radu Prodan’s Lab and Josef Hammer from Hermann Hellwagner’s Lab setup UNI-KLU’s first FOG infrastructure with 40 computing nodes including 5 GPU-enabled ones.
Why should Cloud have all the FUN xD?
Faculty of Technical Sciences, University of Klagenfurt nominated Alexander Lercher from ITEC (Radu Prodan‘s group) for Best Performer Award owing to his outstanding performance in studies. He will be conferred with this honor at a public presentation in lecture hall -3 of the University of Klagenfurt on September 16, 2020. In the course of research carried out by the Studies and Examination Department, Alexander was identified as the most successful student in this field of study.
Elsevier’s Journal of Information and Software Technology (INSOF) accepted the manuscript ”A Dynamic Evolutionary Multi-Objective Virtual Machine Placement Heuristic for Cloud Infrastructures”.
Authors: Ennio Torre, Juan J. Durillo (Leibniz Supercomputing Center), Vincenzo de Maio (Vienna University of Technology), Prateek Agrawal (University of Klagenfurt), Shajulin Benedict (Indian Institute of Information Technology), Nishant Saurabh (University of Klagenfurt), Radu Prodan (University of Klagenfurt).
Abstract: Minimizing the resource wastage reduces the energy cost of operating a data center, but may also lead to a considerably high resource over-commitment affecting the Quality of Service (QoS) of the running applications. The effective trade-off between resource wastage and over-commitment is a challenging task in virtualized Clouds and depends on the allocation of virtual machines (VMs) to physical resources. We propose in this paper a multi-objective method for dynamic VM placement, which exploits live migration mechanisms to simultaneously optimize the resource wastage, over-commitment ratio and migration energy. Our optimization algorithm uses a novel evolutionary meta-heuristic based on an island population model to approximate the Pareto optimal set of VM placements with good accuracy and diversity. Simulation results using traces collected from a real Google cluster demonstrate that our method outperforms related approaches by reducing the migration energy by up to 57 % with a QoS increase below 6 %.
Acknowledgements:
This work is supported by:
The manuscript ”Expelliarmus: Semantic-Centric Virtual Machine Image Management in IaaS Clouds” is accepted for publication at the Journal of Parallel and Distributed Computing (JPDC) (https://www.journals.elsevier.com/journal-of-parallel-and-distributed-computing).
Authors: Nishant Saurabh (University of Klagenfurt), Shajulin Benedict (Indian Institute of Information Technology, Kottayam), Jorge G. Barbosa (LIACC, Faculdade de Engenharia da Universidade do Porto), Radu Prodan (University of Klagenfurt).
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 2.3-22 times compared to state-of-the-art systems (e.g. IBM’s Mirage and Hemera) with significant VMI publish and slight retrieval performance improvement.
Acknowledgements:
This work is supported by: