The PhD defense is in progress. @ilyushkin of @LargeResearch and @EEMCS_TUD discusses his work with @RaduProdanAAU. Science at work. pic.twitter.com/ubSZoXRYCR
— Alexandru Iosup (@AIosup) December 19, 2019
Prof. Radu Prodan participated at the PhD examination of Dr. Yang Hu and Dr. Huan Zhou at the University of Amsterdam, Netherlands.
With: Prof. Peter van Emde Boas, Prof. Cees de Laat, Prof. Henri E. Bal, Dr. Paola Grosso, Dr. Zhiming Zhao, Prof. Rob van Nieuwpoort, Prof. Pieter Adriaans, Dr. Adam S.Z. Belloum
Read more about the High level Symposium.
@articonf participated on Oct. 7-8 at the "High level Symposium towards an Interdisciplinary view on Social Media" organised by @EU_Commission in #Rome, centered around the question: "Are social media changing our brains?", as input to shape future @EU_H2020 research agendas. pic.twitter.com/QzR7QdlAUZ
— Radu Prodan (@RaduProdanAAU) October 9, 2019
ARTICONF aims to set the agenda for the next generation of social media. The pan-European consortium is continuing its integration meeting today in Porto🇵🇹
Social media today does not live up to its possibilities & potentials. It is fragmented,centralised & can be easily abused. pic.twitter.com/7Es8eZu1Wq
— ARTICONF (@articonf) October 2, 2019
The Klagenfurt University hosted the first ASPIDE technical meeting (30th September – 2nd October), which aims on designing scalable software solutions for exascale computing.
ASPIDE Meeting at Klagenfurt University
2019 ASPIDE Meeting Klagenfurt (Social Event)
The papers titled, ‘ARTICONF: Towards a Smart Social Media Ecosystem in a Blockchain Federated Environment’ , and ‘A Semantic Model with Self-Adaptive and Autonomous Relevant Technology for Social Media Applications’ presented at Seventh Workshop on Large Scale Distributed Virtual Environments, LSDVE 2019 collocated at Europar 2019.
ARTICONF team also took advantage of the conference to hold a round table inter-cooperative discussion with an another Next Genreation Social Media EU H2020 project HELIOS with Dr. Laura Ricci, Dr. Dana Petcu, and Dr. Zhiming Zhao. Both project teams identified a number of similar research topics exiting within both projects namely #Context #Trust #CommunityDetection, and hence pledged support for improved inter and intra- cooperation.
The presented papers description are as follows:
1. ARTICONF: Towards a Smart Social Media Ecosystem in a Blockchain Federated Environment
Authors: Radu Prodan, Nishant Saurabh, Zhiming Zhao and Kate Orton-Johnson
2. A Semantic Model with Self-Adaptive and Autonomous Relevant Technology for Social Media Applications
Authors: Zahra Najafabadi Samani, Alexander Lercher, Nishant Saurabh, Radu Prodan
Articonf LSDVE, Euro-Par 2019
Radu Prodan is program chair at the seventeenth international workshop on algorithms, models and tools for parallel computing on Heterogeneous Platforms (HeteroPar’2019), collocated at Euro-Par 2019, August 27, 2019.
The paper “A Container-driven Approach for Resource Provisioning in Edge-Fog Cloud” has been accepted for publication at the 5th International Symposium on Algorithmic Aspects of Cloud Computing, ALGOCLOUD 2019 to be held at Munich, Germany (September 9-13, 2019).
Authors: Hamid Mohammadi Fard, Radu Prodan and Felix Wolf
The paper “Dynamic Multi-Objective Virtual Machine Placement in Cloud Data Centers” has been accepted for publication at the Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2019 to be held at Chalkidiki, Greece (August 28-30, 2019).
Author: Radu Prodan
Abstract: Minimizing the resource wastage reduces the energy cost of operating a data center, but may also lead to a considerably high resource overcommitment affecting the Quality of Service (QoS) of the running applications. Determining the effective tradeoff between resource wastage and overcommitment is a challenging task in virtualized Cloud data centers and depends on how Virtual Machines (VMs) are allocated to physical resources. In this paper, we propose a multi-objective framework for dynamic placement of VMs exploiting live-migration mechanisms which simultaneously optimize the resource wastage, overcommitment ratio and migration cost. The optimization algorithm is based on a novel evolutionary meta-heuristic using an island population model underneath. We implemented and validated our method based on an enhanced version of a well-known simulator.
The results demonstrate that our approach outperforms other related approaches by reducing up to 57% migrations energy consumption while achieving different energy and QoS goals.
The paper “MAPO: A Multi-Objective Model for IoT Application Placement in a Fog Environment” has been accepted for publication at the 9th International Conference on the Internet of Things, IoT 2019 to be held at Bilbao, Spain (October 22-25, 2019).
Authors: Narges Mehran, Dragi Kimovski, Radu Prodan (Alpen-Adria Universität Klagenfurt).
Abstract: The emergence of the Fog computing paradigm that leverages in-network virtualized resources raises important challenges in terms of resource and IoT application management in a heterogeneous environment offering only limited computing resources. In this work, we propose a novel Pareto-based approach for application placement close to the data sources called Multi-objective IoT Application Placement in fOg (MAPO). MAPO models applications based on a finite state machine and uses three convicting optimization objectives, namely completion time, energy consumption, and economic cost, considering both the computation and communication aspects. In contrast to existing solutions that optimize a single objective value, MAPO enables multi-objective energy and cost-aware application placement. To evaluate the quality of the MAPO placements, we created both simulated and real-world testbeds tailored for a set of medical IoT application case studies. Compared to the state-of-the-art approaches, MAPO reduces the economic cost by up to 27%, while decreasing the energy requirements by 23-68%, and optimizes the completion time by up to 7.3 times.
Keywords: Fog computing, IoT application placement, multi-objective optimization, energy consumption
Acknowledgement: Austrian Research Promotion Agency (FFG), project 848448, Tiroler Cloud, funded this work.