Prof. Radu Prodan
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Paper accepted in IEEE UCC

IEEE/ACM 13th International Conference on Utility and Cloud Computing (UCC) accepted the paper “Dynamic Multi-objective Scheduling of Microservices in the Cloud”.

Authors: Hamid Mohammadi Fard, Radu Prodan, Felix Wolf

Abstract: For many applications, a microservices architecture promises better performance and flexibility compared to a conventional monolithic architecture. In spite of the advantages of a microservices architecture, deploying microservices poses various challenges for service developers and providers alike. One of these challenges is the efficient placement of microservices on the cluster nodes. Improper allocation of microservices can quickly waste resource capacities and cause low system throughput. In the last few years, new technologies in orchestration frameworks, such as the possibility of multiple schedulers for pods in Kubernetes, have improved scheduling solutions of microservices but using these technologies needs to involve both the service developer and the service provider in the behavior analysis of workloads. Using memory and CPU requests specified in the service manifest, we propose a general microservices scheduling mechanism that can operate efficiently in private clusters or enterprise clouds. We model the scheduling problem as a complex variant of the knapsack problem and solve it using a multi-objective optimization approach. Our experiments show that the proposed mechanism is highly scalable and simultaneously increases utilization of both memory and CPU, which in turn leads to better throughput when compared to the state-of-the-art.

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Paper accepted: A channel allocation algorithm for cognitive radio users based on channel state predictors

Authors: Nakisa Shams (ETS, Montreal, Canada), Hadi Amirpour (Alpen-Adria-Universität Klagenfurt), Christian Timmerer (Alpen-Adria-Universität Klagenfurt, Bitmovin), and Mohammad Ghanbari (School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK)

Abstract: Cognitive radio networks by utilizing the spectrum holes in licensed frequency bands are able to efficiently manage the radio spectrum. A significant improvement in spectrum use can be achieved by giving secondary users access to these spectrum holes. Predicting spectrum holes can save significant energy that is consumed to detect spectrum holes. This is because the secondary users can only select the channels that are predicted to be idle channels. However, collisions can occur either between a primary user and secondary users or among the secondary users themselves. This paper introduces a centralized channel allocation algorithm in a scenario with multiple secondary users to control both primary and secondary collisions. The proposed allocation algorithm, which uses a channel status predictor, provides a good performance with fairness among the secondary users while they have the minimal interference with the primary user. The simulation results show that the probability of a wrong prediction of an idle channel state in a multi-channel system is less than 0.9%. In addition, the channel state prediction saves the sensing energy up to 73%, and the utilization of the spectrum can be improved more than 77%.

Keywords: Cognitive radio, Biological neural networks, Prediction, Idle channel.

International Congress on Information and Communication Technology

25-26 February 2021, London, UK

Link: https://icict.co.uk/home.php

Prof. Radu Prodan
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IEEE DeSE 2020: Keynote Speaker

Prof. Radu Prodan is a keynote speaker at the 13th International Conference On The Developments in eSystems Engineering (DeSE), 13th-17th December 2020.

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Paper accepted: Energy Consumption Analysis of R-based Machine Learning Algorithms for Pandemic Predictions

Authors: Shajulin Benedict (IIIT Kottayam, India), Prateek Agrawal (University of Klagenfurt, Austria & Lovely Professional University, India) , Radu Prodan (University of Klagenfurt, Austria)

Abstract: The push for agile pandemic analytic solutions has rapidly attained development-stage software modules instead of functioning as full-fledged production-stage products — i.e., performance, scalability, and energy-related concerns need to be optimized for the underlying computing domains. And while the research continues to support the idea that reducing the energy consumption of algorithms improves the lifetime of battery-operated machines, advisable tools in almost any developer setting, an energy analysis report for R-based analytic programs is indeed a valuable suggestion. This article proposes an energy analysis framework for R-programs that enables data analytic developers, including pandemic-related application developers, to analyze code. It reveals an energy analysis report for R programs written to predict the new cases of 215 countries using random forest variants. Experiments were carried out at the IoT cloud research lab and the energy efficiency aspects were discussed in the article. In the experiments, ranger-based prediction program consumed 95.8 Joules.

4th International Conference on Advanced Informatics for Computing Research (ICAICR-2020) 

Link: http://informaticsindia.co.in/

Acknowledgement: This work is supported by IIIT-Kottayam faculty research fund and OEAD-DST fund.

Prof. Radu Prodan
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SOFA 2020: Guest of Honor and Talk

Prof. Radu Prodan ist guest of honor at the 9th International Workshop on Soft Computing Applications (SOFA), 27-29 Nov 2020, Arad, Romania. The title of his talk is “Distribute one Billion”.

Prof. Radu Prodan
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Project ADAPT: Newspaper Article in “Kronen Zeitung”

The newspaper “Kronen Zeitung” published the article “IM KAMPF GEGEN CORONA: Universität Klagenfurt forscht mit den Chinesen” with Prof. Radu Prodan.

Prof. Radu Prodan
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Project ADAPT: newspaper article in “Kleine Zeitung”

The newspaper “Kleine Zeitung” published the article “Medizinische Schutzausrüstung: Neue IT-Lösung soll Menschenleben retten” with Prof. Radu Prodan.

 

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Paper accepted in the 10th IEEE Conference on Big Data and Cloud Computing: “Cloud — Edge Offloading Model for Vehicular Traffic Analysis”

Authors: Dragi Kimovski, Dijana C. Bogatinoska, Narges Mehran, Aleksandar Karadimce, Natasha Paunkoska, Radu Prodan, Ninoslav Marina

Abstract: The proliferation of smart sensing and computing devices, capable of collecting a vast amount of data, has made the gathering of the necessary vehicular traffic data relatively easy. However, the analysis of these big data sets requires computational resources, which are currently provided by the Cloud Data Centers. Nevertheless, the Cloud Data Centers can have unacceptably high latency for vehicular analysis applications with strict time requirements. The recent introduction of the Edge computing paradigm, as an extension of the Cloud services, has partially moved the processing of big data closer to the data sources, thus addressing this issue. Unfortunately, this unlocked multiple challenges related to resources management. Therefore, we present a model for scheduling of vehicular traffic analysis applications with partial task offloading across the Cloud — Edge continuum. The approach represents the traffic applications as a set of interconnected tasks composed into a workflow that can be partially offloaded to the Edge. We evaluated the approach through a simulated Cloud — Edge environment that considers two representative vehicular traffic applications with a focus on video stream analysis. Our results show that the presented approach reduces the application response time up to eight times while improving energy efficiency by a factor of four.

Prof. Radu Prodan
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FFG project “ADaptive and Autonomous data Performance connectivity and decentralized Transport decision-making Network” (ADAPT) accepted

This project started during the most critical phase of the COVID-19 outbreak in Europe where the demand for Personal Protective Equipment (PPE) from each country’s health care system has
surpassed national stock amounts by far. Therefore, the ADAPT consortia agreed to bundle its joint resources to develop and adaptive and autonomous decision-making network to support the involved stakeholders along the PPE Supply Chain in their endeavour to save and protect human lives as quickly as possible.

The partners will do that by providing a Blockchain solution capable of optimizing supply, demand and transport capacities between them, elaborating a technical solution for transparent and realtime certification checks on equipment and production documentation as well as distributed and parallel decision-making capabilities on all levels of this multi-dimensional research problem.

In total, the world community will spent more than € 49,6 billion on PPE medical equipment in 2020, € 7,7 billion thereof could be saved with the transport optimization of ADAPT and additional € 5,18 billion could be freed up in the financing and banking sector which could be reinvested immediately into the expansion of the world’s national health care systems.

ADAPT is a 36-month duration project submitted to 6th Call for Austrian-Chinese Coop. RTD Projects FFG & CAS.

Partners:

  • Alpen-Adria Universität Klagenfurt, Institute of Information Technology (UNI-KLU)
  • Johannes-Kepler-Universität Linz, Intelligent Transport Systems-Sustainable Transport Logistics 4.0. (JKU)
  • Logoplan – Logistik, Verkehrs und Umweltschutz Consulting GmbH (LP)
  • Intact GmbH (INTACT)
  • Chinese Academy of Sciences, Institute of Computing Technology (ICTCAS)
Prof. Radu Prodan
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Paper accepted in IEEE Internet Computing: “Inter-host Orchestration Platform Architecture for Ultra-scale Cloud Applications”

The manuscript “Inter-host Orchestration Platform Architecture for Ultra-scale Cloud Applications” has been accepted for publication in an upcoming issue of IEEE Internet Computing.

Authors: Sasko Ristov, Thomas Fahringer, Radu Prodan, Magdalena Kostoska, Marjan Gusev, Shahram Dustdar

Abstract: Cloud data centers exploit many memory page management techniques that reduce the total memory utilization and access time. Mainly these techniques are applied to a hypervisor in a single host (intra-hypervisor) without the possibility to exploit the knowledge obtained by a group of hosts (clusters). We introduce a novel inter-hypervisor orchestration platform to provide intelligent memory page management for horizontal scaling. It will use the performance behavior of faster virtual machines to activate pre-fetching mechanisms that reduce the number of page faults. The overall platform consists of five modules – profiler, collector, classifier, predictor, and pre-fetcher. We developed and deployed a prototype of the platform, which comprises the first three modules. The evaluation shows that data collection is feasible in real-time, which means that if our approach is used on top of the existing memory page management techniques, it can significantly lower the miss rate that initiates page faults.