This week, we will be playing and discussing all entries from the 6th Klagenfurt Game Jam live on-stream!
Join us on Friday, May 3rd, 11am UTC/1pm CEST at
Link to the Journal of Grid Computing special issue.
Authors: Abdelhak Bentaleb, Christian Timmerer, Ali C. Begen, and Roger Zimmermann
Abstract: HTTP adaptive streaming (HAS) with chunked transfer encoding can be used to reduce latency without sacrificing the coding ef- ficiency. While this allows a media segment to be generated and delivered at the same time, it also causes grossly inaccurate bandwidth measurements, leading to incorrect bitrate selections. To overcome this effect, we design a novel Adaptive bitrate scheme for Chunked Transfer Encoding (ACTE) that leverages the unique nature of chunk downloads. It uses a sliding window to accurately measure the available bandwidth and an online linear adaptive filter to predict the available bandwidth into the future. Results show that ACTE achieves 96% measurement accuracy, which translates to a 64% reduction in stalls and a 27% increase in video quality.
Acknowledgment: This research has been supported in part by the Singapore Ministry of Education Academic Research Fund Tier 1 under MOE’s official grant number T1 251RES1820 and the Austrian Research Promotion Agency (FFG) under the Next Generation Video Streaming project “PROMETHEUS”.
Keywords: HAS; ABR; DASH; CMAF; low-latency; HTTP chunked transfer encoding; bandwidth measurement and prediction; RLS.
Link: http://nossdav.org/2019/
Within the scope of the AAU’s young Scientists Mentoring Programme, Andreas Leibetseder is visiting his mentor Oge Marques, Professor at the Department of Computer and Electrical Engineering and Computer Science and Florida Atlantic University (FAU) in Boca Raton, Florida. During his stay over the course of April, he intends to advance in his PhD studies by focusing on the sub-topic of region-based Endometriosis classification in laparoscopic media. He intends to approach this problem by adapting and applying deep learning technologies, profiting from the knowledge and insights of his mentor as well as other students of the local mlab research group (http://mlab.fau.edu/www/). Currently, several work packages have been defined, which include investigating lesion detection approaches on radiological image datasets for application in the endoscopic domain.
The paper “Active Online Learning for Social Media Analysis to Support Crisis Management” was published as an “early access” article in the journal IEEE Transactions on Knowledge and Data Engineering (Q1 in ScimagoJR). The research leading to these results received funding from the EU FP7 Programme under grant agreement no. 261817 and was performed in collaboration with Bournemouth University, UK.
Authors: Daniela Pohl (AAU Klagenfurt, Inst. of Information Technology), Abdelhamid Bouchachia (Bournemouth University, Dept. of Computing, UK), Hermann Hellwagner (AAU Klagenfurt, Inst. of Information Technology)
Abstract: People use social media (SM) to describe and discuss different situations they are involved in, like crises. It is therefore worthwhile to exploit SM contents to support crisis management, in particular by revealing useful and unknown information about the crises in real-time. Read more
Univ.-Prof. DI Dr. Radu Prodan has been invited to be a part of panel discussion on emerging Blockchain and Edge computing technology and paradigms at PAISE (Parallel AI and Systems for the Edge), collocated workshop at the prestigious 33rd IEEE International Parallel & Distributed Processing Symposium, IPDPS 2019 to be held at Rio de Janerio, Brazil (May 20-24 2019).
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.
