Die
folgende
Liste ist lediglich eine Sammlung der möglichen Themen
für das Seminar. Themen in roter Farbe sind neue Themen.
Seminarthemen können
auch von Studierenden
vorgeschlagen
werden. Die
genaue Festlegung der Themen und der Zeiten erfolgt in der
Vorbesprechung!
| Themenkatalog |
Themen |
| Einführung | Cloud Computing: Eine
Einführung (IaaS, PaaS, SaaS) |
| Introduction
to Edge and IoT |
|
| From event streams to
process models and back: challenges and
opportunities, by Pnina Soffer et al. |
|
| Fog computing and its role in the IoT | |
| Fogbus: a blockchain-based lightweight framework for edge and fog computing | |
| Edge-Fog computing | Fog Function: Serverless fog computing for data intensive IoT services |
| FogFlow: Easy Programming of IoT services over cloud and edges for smart cities | |
| Cloudlet | |
| KubeEdge | |
| Telcofog | |
| Baidu OpenEdge | |
| Interoperabilität | Interoperability in
Internet of Things: Taxonomies and Open
Challenges |
| Semantic Interoperability | |
| Next generation service interface to achieve interoperability for distributed systems (NGSI) | |
| FIWARE: The open source platform for our smart digital future | |
| Formal foundations of serverless computing, by Abhinav Jangda et al. | |
| Cloud Integration Hubs: integrate data through batch, real-time and events over the Cloud | |
| Workflows allgemein | workflowpatterns.com |
| FAIR workflows: fair-workflows.github.io | |
| Workflow provenance | |
| The future of scientific workflows, by E. Deelman et al | |
| Serverless workflows | A practical declarative programming framework for serverless compute, by Shannon Joyner et al. |
| Serverless computing with OpenWhisk and AWS Lambda | |
| Serverless computing with Google | |
| Serverless computing with Microsoft Azure | |
| RADICAL-Cybertools: Building blocks for middleware for workflow systems | |
| Parsl: phython library for programming and executing data oriented workflows in parallel | |
| Flux: the workflow of workflows | |
| Triggerflow: Trigger-based Orchestration of SErverless Workflows | |
| serverlessworkflow.org | |
| Ripple: A practical declarative programming framework for serverless compute | |
| Fogflow (NEC) | |
| Azzure IoT Edge | |
| Amazon Greengrass | |
| Workflows with Microsoft LogicApps | |
| Workflows with Azzure Durable Functions | |
| Workflows with Amazon StepFunctions | |
| Workflows with Google Cloud Composer | |
| Workflows with IBM Composer | |
| Workflows with Fission | |
| Triggerflow: Trigger-based orchestration of serverless workflows | |
| FaaS orchestration of parallel workflows, Gerard Paris, et al. | |
| Heterogeneous hierarchical workflow composition (Rosa Badia et al) | |
| Comparison of FaaS orchestration systems (by Pedro Garcia Lopez et al) | |
| In search of fast and efficient serverless DAG engine, by Benjamin Carver, et al. | |
| FunctionBench: A suite of workloads for serverless cloud function services | |
| Data management | NGSI: The context api in the oma next generation service interface, M. Bauer et al. |
| MQTT message queueing | |
| Apache NiFi | |
| S3 storage | |
| Fog computing: data lakes | |
| Modeling data pipelines, by A. Raj, et al | |
| Serverless data pipeline | |
| Pocket: elastic ephemeral storage for serverless analytics | |
| Scheduling
and Optimization of Applications |
Multiple
workflow scheduling
with offloading tasks to edge cloud |
| A data-replica placement strategy for IoT workflows in collaborative edge and cloud environments | |
| Dynamic scheduling for stochastic edge-cloud computing environments using A3C learning and residual recurrent neural networks | |
| In search of a fast and efficient serverless DAG engine | |
| A task scheduling strategy in edge-cloud collaborative scenario based on deadline | |
| Performance optimization for edge-cloud serverless platforms via dynamic task placement | |
| Resource management | Resource managemeent in fog/edge computing: a survey on architectures, infrastructure, and algorithms |
| BARISTA: efficient and scalable serverless serving system for deep learning prediction services | |
| Reliable capacity provisioning for distributed cloud/edge/fog computing applications | |
| Resource management approaches in fog computing: a comprehensive review | |
| ENORM: a framework for edge node resource management | |
| Resource management at the network edge: a deep reinforcement learning approach | |
| Connecting and managing a large number of of IoT devices: Azure IoT Hub | |
| Holistic resource managment for sustainable and reliable cloud computing | |
| Automated fine-grained cpu cap control in serverless computing platform | |
| Deadline-based dynamic resource allocation and provisioning algorithms in fog-cloud environment | |
| A dynamic resource controller for a lambda architecture | |
| Amoeba: Qos-awareness and reduced resource usage of microservices with serverless computing | |
| Verschiedenes | Galaxy: open, web-based platform for accessible, reproducible, and transparent computational research (galaxyproject.org) |
| Riotbench: an IoT benchmark for distributed stream processing systems. | |
| Formal foundations of serverless computing |
T. Fahringer, Institut für Informatik, Universität Innsbruck