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  • 1.
    Boodaghian Asl, Arsineh
    et al.
    Karlstad University, Faculty of Arts and Social Sciences (starting 2013), Karlstad Business School (from 2013).
    Gokan Khan, Michel
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Studying the Effect of Online Medical Applications on Patients Healing Time and Doctors Utilization Using Discrete Event Simulation2019Conference paper (Refereed)
    Abstract [en]

    Online Medical Applications (OMA) has evolved dramatically in the last few years, and, consequently, the number of patients using it has also grown exponentially. Patients who are seeking non-emergency but immediate medical services or consultant may save time and money by approaching online doctors through OMAs instead of visiting them physically in healthcare centers. Additionally, medical doctors quantity contributing to OMAs growth, which can affect patients average waiting time and the queue size in healthcare centers. In this paper, We have developed a Discrete Event Simulation (DES) model to study the effects of using OMA on the patients healing time and doctors utilization by comparing it with the same process in healthcare centers. Additionaly, we compared patients average queue size, maximum number of patients in the queue, and total number of healed patients in our study. The results of this simulation showed that the healing process in OMA could serve the same number of patients in ~46% shorter time compared to healthcare centers with ~5.7% less doctors’ utilization.

  • 2.
    Boodaghian Asl, Arsineh
    et al.
    Karlstad University, Faculty of Arts and Social Sciences (starting 2013), Karlstad Business School (from 2013).
    Gokan, Michel
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    An Empirical Study On GUI-ii Interview Methods In Participatory Design2019In: IADIS (International association for developement of the information society) digital library / [ed] Katherine Blashki and Yingcai Xiao, IADIS Press , 2019, p. 3-10Conference paper (Refereed)
    Abstract [en]

    Graphical user interface interaction interview (GUI-ii), is a recently purposed method in which designers can remotely co-design and review GUI prototypes by eliminating the need for physical presence of co-designers. However, there are some concerns regarding the accuracy of such remote interview methods, as users do not have any physical interaction with the designers during their interview. In this work, for the first time, we compare GUI-ii methods with the traditional face-to-face interview processes to study their effectiveness in various design phases. The result shows that GUI-ii method is most effective when used in Ozlab.    

  • 3.
    Gokan Khan, Michel
    et al.
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Bastani, Saeed
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Taheri, Javid
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Kassler, Andreas
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Deng, Shuigang
    Zhejiang Univ, China.
    NFV-Inspector: A Systematic Approach to Profile and Analyze Virtual Network Functions2018In: 2018 IEEE 7th International Conference on Cloud Networking (CloudNet), IEEE, 2018, p. 1-7Conference paper (Refereed)
    Abstract [en]

    Network Function Virtualization (NFV) focuses on decoupling network functions from proprietary hardware (i.e., middleboxes) by leveraging virtualization technology. Combining it with Software Defined Networking (SDN) enables us to chain network services much easier and faster. The main idea of using these technologies is to consolidate several Virtual Network Functions (VNFs) into a fewer number of commodity servers to reduce costs, increase VNFs fluidity and improve resource efficiency. However, the resource allocation and placement of VNFs in the network is a multifaceted decision problem that depends on many factors, including VNFs resource demand characteristics, arrival rate, configuration of underlying infrastructure, available resources and agreed Quality of Services (QoS) in Service Level Agreements (SLAs). This paper presents a bottom-up open-source NFV analysis platform (NFV-Inspector) to (1) systematically profile and classify VNFs based on resource capacities, traffic demand rate, underlying system properties, placement of VNFs in the network, etc. and (2) extract/calculate the correlation among the QoS metrics and resource utilization of VNFs. We evaluated our approach using an emulated virtual Evolved Packet Core platform (Open5GCore) to showcase how complex relation among various NFV service chains can be systematically profiled and analyzed.

  • 4.
    Gokan Khan, Michel
    et al.
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Taheri, Javid
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Kassler, Andreas
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Darula, Marian
    R&D Technology and Industry, Ericsson, Sweden.
    Automated Analysis and Profiling of VirtualNetwork Functions: the NFV-Inspector Approach2018In: 2018 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN), IEEE, 2018Conference paper (Refereed)
    Abstract [en]

    Discovering insights about Virtual Network Function (VNFs) resource demand characteristics will enable cloud vendors to optimize their underlying Network Function Virtualization (NFV) system orchestration and dramatically mitigate CapEx and OpEx spendings. However, analyzing large-scale NFV systems, especially in mobile network environments, is a challenging task and requires tailor-made approaches for each particular application. In this demo, we showcase NFV-Inspector, an open source and extensible VNF analysis platform that is capable of systematically benchmark and profile NFV deployments. Based on its pluggable framework, NFV-Inspector classifies VNFs resource demand characteristics and correlate their Key Performance Indicators (KPIs) with system-level Quality of Service (QoS) measurements. 

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