Claus Pahl

Full Professor of Software Engineering
Faculty of Engineering
Free University of Bozen-Bolzano, Italy.

 Tel:     +39 0471 016 177
 Fax:    +39 0471 016 009

 EMail: Claus.Pahl-at-unibz-dot-it


[ Publications ]

[ Research ]

[ Projects ]

[ Teaching ]


RDDS Requirements and Design For Dependable Systems ]

SE Software Engineering ]


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  • Research Areas:

    Software Architecture, with Applications to:

    Automation and Adaptive Systems:
    • Adaptivity in Cloud Computing: data integration for on-demand architectures; ontology-based service mediation for on-demand service systems; multi-tenancy SOA and policy-based governance.
    • Scalable Cloud Computing Platforms: infrastructure scalability (VM, storage, network); pattern-based quality prediction; SLA management architectures.
    • Dynamic Cloud Models - Quality and Monitoring: dynamic architectural configuration, service models at runtime; logic and ontology for runtime SOA modelling and composition; constraints monitoring; multi-tenancy SOA and SOA governance.
    • AI-Driven Controller: use of AI techniques, such as nature-inspired (PSO) or reinforcement learning (Q-learning, SARSA), to construct controllers for resource management and workload distribution. Quality management and metrics for controllers.
    IoT and Edge Computing Architecture:
    • Fog and Edge Cloud Architectures: edge cloud architectures; single-board computing infrastructures; Raspberry Pi; cluster management; self-management.
    • Container Architectures and Microservices for the IoT Edge : container technology; Docker; container platforms and clusters; performance management; orchestration.

    Possible BSc and MSc Projects:

    • Container and Microservice Architecture (Area: software systems architecture): the aim is to allow container virtualisation to run on smaller devices like Raspberry Pis and to learn how to configure these best. Involves platform installation, configuration and experimentation (e.g. performance engineering).
      One sample problem is to experiment with Kubernetes on RPi clusters - from a performance engineering perspective, looking at scalability and other aspects.
    • Cloud Controllers (Area: autonomous systems + performance engineering + AI): the aim of the project is to investigate automatic cloud resource optimisation. This can involve different AI and machine learning techniques to create an autonomous resource management system.
    • Cluster Management (Area: distributed systems + performance engineering + dependability): the objective is to investigate distribution and workload orchestration strategies for IoT and edge clusters (i.e. distributed systems management applied to IoT and edge).
      A specific problem is high mobility: what is the right level of 'edge'? how close to the network should the MEC be? For that, we can measure traffic (user to MEC, inter MEC if the user is highly mobile), and also include costs for local edge clouds into the determination of results.
    • Tools for Software Engineering Education (Area: educational technologies): the objective is the development of tools for the delivery and assessment.

    For these projects, the CECL Cloud and Edge Computing Lab (hardware and simulation tools) can be used. Concrete use cases can be derived from large research projects, e.g. 5G-CARMEN on autonomous cars and intelligent mobility, that UniBZ is involved in.

    Consultation Hours:

    • after the lecture and lab sessions


    Research Group:

    SEAS - Software Engineering and Autonomous Systems

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