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  • 1.
    Toczé, Klervie
    et al.
    Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, Faculty of Science & Engineering.
    Nadjm-Tehrani, Simin
    Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, Faculty of Science & Engineering.
    ORCH: Distributed Orchestration Framework using Mobile Edge Devices2019In: 2019 IEEE 3RD INTERNATIONAL CONFERENCE ON FOG AND EDGE COMPUTING (ICFEC), IEEE , 2019Conference paper (Refereed)
    Abstract [en]

    In the emerging edge computing architecture, several types of devices have computational resources available. In order to make efficient use of those resources, deciding on which device a task should execute is of great importance. Existing works on task placement in edge computing focus on a resource supply side consisting of stationary devices only. In this paper, we consider the addition of mobile edge devices. We explore how mobile and stationary edge devices can augment the original task placement problem with a second placement problem: the placement of the mobile edge devices. We propose the ORCH framework in order to solve the joint problem in a distributed manner and evaluate it in the context of a spatially-changing load. Our implementation of the combined task and edge placement algorithms shows a normalized 83% delay-sensitive task completion rate compared to a perfect edge placement strategy.

  • 2.
    Toczé, Klervie
    et al.
    Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, Faculty of Science & Engineering.
    Schmitt, Norbert
    University of Würzburg; Germany.
    Brandic, Ivona
    Vienna University of Technology, Austria.
    Aral, Atakan
    Vienna University of Technology, Austria.
    Nadjm-Tehrani, Simin
    Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, Faculty of Science & Engineering.
    Towards Edge Benchmarking: A Methodology for Characterizing Edge Workloads2019In: In Proceedings of 4th International Workshop on Foundations and Applications of Self* Systems (FAS*W),, IEEE, 2019Conference paper (Refereed)
    Abstract [en]

    The edge computing paradigm has recently attracted research efforts coming from different application domains. However, evaluating an edge platform or algorithm is impeded by the lack of suitable benchmarks. We propose a methodology for characterizing edge workloads from different application domains. It is a first step towards defining workloads to be included in a future edge benchmarking suite. We evaluate the methodology on three use cases and find that defining a common and standard set of workloads is plausible.

  • 3.
    Toczé, Klervie
    et al.
    Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, Faculty of Science & Engineering.
    Nadjm-Tehrani, Simin
    Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, Faculty of Science & Engineering.
    A Taxonomy for Management and Optimization of Multiple Resources in Edge Computing2018In: Wireless Communications & Mobile Computing, ISSN 1530-8669, E-ISSN 1530-8677, article id 7476201Article, review/survey (Refereed)
    Abstract [en]

    Edge computing is promoted to meet increasing performance needs of data-driven services using computational and storage resources close to the end devices at the edge of the current network. To achieve higher performance in this new paradigm, one has to consider how to combine the efficiency of resource usage at all three layers of architecture: end devices, edge devices, and the cloud. While cloud capacity is elastically extendable, end devices and edge devices are to various degrees resource-constrained. Hence, an efficient resource management is essential to make edge computing a reality. In this work, we first present terminology and architectures to characterize current works within the field of edge computing. Then, we review a wide range of recent articles and categorize relevant aspects in terms of 4 perspectives: resource type, resource management objective, resource location, and resource use. This taxonomy and the ensuing analysis are used to identify some gaps in the existing research. Among several research gaps, we found that research is less prevalent on data, storage, and energy as a resource and less extensive towards the estimation, discovery, and sharing objectives. As for resource types, the most well-studied resources are computation and communication resources. Our analysis shows that resource management at the edge requires a deeper understanding of how methods applied at different levels and geared towards different resource types interact. Specifically, the impact of mobility and collaboration schemes requiring incentives are expected to be different in edge architectures compared to the classic cloud solutions. Finally, we find that fewer works are dedicated to the study of nonfunctional properties or to quantifying the footprint of resource management techniques, including edge-specific means of migrating data and services.

  • 4.
    Toczé, Klervie
    et al.
    Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, Faculty of Science & Engineering.
    Nadjm-Tehrani, Simin
    Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, Faculty of Science & Engineering.
    Where Resources meet at the Edge2017In: 2017 IEEE INTERNATIONAL CONFERENCE ON COMPUTER AND INFORMATION TECHNOLOGY (CIT), IEEE , 2017, p. 302-307Conference paper (Refereed)
    Abstract [en]

    Edge computing is a recent paradigm where network nodes are placed close to the end users, at the edge of the network. Efficient management of resources within this configuration is crucial due to scarcity and geographical spreading of edge resources. We begin by a brief description of the edge paradigm, the most generic edge architecture, and the terminology associated to it. Then, we propose and elaborate on a preliminary taxonomy for edge resource management, together with a substantial review of works in the area. Finally, we identify some research challenges.

  • 5.
    Toczé, Klervie
    et al.
    Ericsson AB, Linköping, Sweden.
    Vasilevskaya, Maria
    Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, Faculty of Science & Engineering.
    Sandahl, Patrik
    Ericsson AB, Linköping, Sweden.
    Nadjm-Tehrani, Simin
    Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, Faculty of Science & Engineering.
    Maintainability of Functional Reactive Programs in a Telecom Server Software2016In: SAC '16 Proceedings of the 31st Annual ACM Symposium on Applied Computing, Association for Computing Machinery (ACM), 2016, p. 2001-2003Conference paper (Other academic)
    Abstract [en]

    Functional Reactive Programming (FRP) is claimed to be a good choice for event handling applications. Current object- oriented telecom applications are known to suffer from additional complexity due to event handling code. In this paper we study the maintainability of FRP programs in the tele- com domain compared to traditional object-oriented programming (OOP), with the motivation that higher maintainability increases the service quality and decreases the costs. Two implementations of the same procedure are created: one using Haskell and the reactive-banana FRP frame- work and one using C++ and the OOP paradigm. Four software experts each with over 20 years of experience and three development engineers working on a product subject to study were engaged in evaluations, based on a questionnaire involving five different aspects of maintainability. The evaluations indicate a higher maintainability profile for FRP compared with OOP. This is confirmed by a more detailed analysis of the code size. While performance was not a main criteria, a preliminary evaluation shows that the OOP prototype is 8-10 times faster than the FRP prototype in the current (non-optimised) implementations.

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