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:
The manuscript “The Workflow Trace Archive: Open-Access Data from Public and Private Computing Infrastructures” has been accepted for publication in the A* ranked IEEE Transactions on Parallel and Distributed Systems (TPDS) journal.
Authors: Laurens Versluis, Roland Mathá, Sacheendra Talluri, Tim Hegeman, Radu Prodan, Ewa Deelman, and Alexandru Iosup
Abstract: Realistic, relevant, and reproducible experiments often need input traces collected from real-world environments. We focus in this work on traces of workflows—common in datacenters, clouds, and HPC infrastructures. We show that the state-of-the-art in using workflow-traces raises important issues: (1) the use of realistic traces is infrequent, and (2) the use of realistic, open-access traces even more so. Alleviating these issues, we introduce the Workflow Trace Archive (WTA), an open-access archive of workflow traces from diverse computing infrastructures and tooling to parse, validate, and analyze traces. The WTA includes >48 million workflows captured from >10 computing infrastructures, representing a broad diversity of trace domains and characteristics. To emphasize the importance of trace diversity, we characterize the WTA contents and analyze in simulation the impact of trace diversity on experiment results. Our results indicate significant differences in characteristics, properties, and workflow structures between workload sources, domains, and fields.
Acknowledgments: This work is supported by the projects Vidi MagnaData, Commit, the European Union’s Horizon 2020 Research and Innovation Programme, grant agreement number 801091 “ASPIDE”, and the National Science Foundation award number 1664162.
The first review of the ASPIDE project took place on 25.02.2020 in the premises of the European Commission in Luxemburg. During the project review, a live demo of the platform for supporting extreme scale applications was presented and future research and developing activities were discussed with the reviewers.
Aspide Review 2020
We are #ARTICONF, we are people. We are federating trust.
Here is a photo of us today after our periodic review in Brussels. Thank you Peter Friess, @FraukeBehrendt, @joaomagalhaes, @gravesen for your feedback on our project of #decentralised #socialmedia ⛓️ pic.twitter.com/4ovzPJbHBj
— ARTICONF (@articonf) February 20, 2020
ARTICONF: EU first review
Interview with @RaduProdanAAU at #ESMH The days when people work together in an office, are over…
In the future, ‘teams’ will be social networks… able to connect to other federated networks of clouds and at the same time establish privacy and trust. https://t.co/GwaVaQTFFJ— ARTICONF (@articonf) January 30, 2020
The ITEC team participated in the HiPeac 2020 International Workshop on Exascale programing models for extreme data with a presentation with title “Monitoring data collection and mining for Exascale systems”. The ITEC team also attended the collocated ASPIDE meeting and actively participated in the decision of the next research activities in the project.
