The following list is merely a collection of possible topics for the seminar. Topics in color other than black are new topics. Seminar topics can also be suggested by students. The exact determination of topics and times will be made during the preliminary discussion
| 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 | |
| Serverless Computing | The Rise of Serverless Computing; What Serverless Computing is and should become: next phase of cloud computing |
| A decentralized framework for serverless edge cmputing in the IoT | |
| On the FaaS Track: Buidling stateful distributed applications with serverless architectures | |
| Kalix: high performance microservices and APIs with no operations required | |
| eigr: A serverless runtime on the BEAM (stateful services) | |
| A practical declarative programming framework for serverless computing, by Shannon Joyner et al. | |
| Serverless computing with OpenWhisk and AWS Lambda | |
| Serverless computing with Google | |
| Serverless computing with Microsoft Azure | |
| lithops.cloud | |
| FaaSm: Lightweight isolation for efficient stateful serverless computing | |
| Backend as a Service (BaaS) | |
| Google cloud dataflow | |
| AWS glue | |
| google App Engine | |
| PyWren and numpywren | |
| ExCamera | |
| FaaSM: lightweight isolation for efficient stateful serverless computing | |
| notification services: AWS SNS, AWS SQS, etc. | |
| 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 | |
| Events | CloudEvents |
| AsyncAPI | |
| OpenTelemetry | |
| Triggermesh | |
| Distributed Data Stream Processing |
Storm-RTS: stream processing with stable performance for multi-cloud and cloud-edge |
| Apache Flink | |
| Apache Storm | |
| Amazon Kinesis Data Streams | |
| Azure Stream Analytics and Azure Event Hubs | |
| StreamSets | |
| Google Cloud Pub/Sub | |
| ElasticStack | |
| StreamSets | |
| Workflows | Galaxy Workflows |
| Sweep: accelerating scientific research through scalable serverless workflows | |
| workflowpatterns.com | |
| Node-RED von IBM | |
| FAIR workflows: fair-workflows.github.io | |
| Workflow provenance | |
| The future of scientific workflows, by E. Deelman et al | |
| Serverless Workflows | 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 | |
| CRUCIAL: buillding stateful distributed applications with serverless architectures, by. Daniel Barcelona-Pons, et al. | |
| 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 | |
| Distributed Programming | Apache Flink: distributed stateful applications for data streams |
| Object-oriented choreographic programming, Saverio Giallorenzo et al. | |
| REScala: Bridging between object-oriented and functional style in reactive applications | |
| A fault-tolerant programming model for distributed interactive applications | |
| State Machine Applications | machine.js |
| JSON Finite State machine (JFMS) | |
| xstate | |
| SCXML (state chart XML) | |
| Itemis Create (https://www.itemis.com/en/products/create) | |
| Scheduling | Declarative and Linear Programming Approaches to Service Placement, Reconciled |
| Multiple workflow scheduling with offloading tasks to edge cloud |
|
| MCDS: AI augmented workflow scheduling in mobile edge cloud computing systems | |
| Addressing application latency requirements through edge scheduling | |
| Task offloading for mobile edge computing in software defined ultra-dense network | |
| Microservices scheduling model over heterogeneous cloud-edge environments as support for IoT applications | |
| Online job dispatching and scheduling in edge-clouds | |
| A data-replica placement strategy for IoT workflows in collaborative edge and cloud environments | |
| Dynamic scheduling for sotchastic edge-cloud computing environments using A3C learning and residual recurrent neural networks | |
| A task scheduling strategy in edge-cloud collaborative scenario based on deadline | |
| IoT-Edge without the cloud | Picasso: A lightweight edge computing platform |
| Incremental deployment and migration of geo-distributed situation awareness applications in the fog | |
| Tasklets: better than best-effort computing | |
| Resource management | IEEE P1935 Edge/Fog Manageability and Orchestration: Standard and Usage Example |
| Resource managemeent in fog/edge computing: a survey on architectures, infrastructure, and algorithms | |
| Hadoop YARN | |
| Apache Mesos | |
| Kubernetes | |
| HUNTER: AI based holistic resource management for sustainable cloud computing | |
| Disaggregated datacenters | |
| LegoOS: a disseminated, distributed OS for Hardware Resource Disaggregration | |
| EdgeOS: an edge operating system | |
| 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 dynmaic 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 | |
| Distributed and Local Storage | IBM cloud object storage project |
| Plasma object storage from apache arrow: local per-node cache | |
| Infinispan: cluster based caching | |
| RUCIO: scientific data management | |
| CRUCIAL distributed shared objects: https://github.com/danielBCN/crucial-dso | |
| Cassandra: a decentralized structured storage system | |
| dataClay: distributed data storage system | |
| Local-first software: your own data, in spite of the cloud | |
| Measurement, Monitoring, and Prediction | PowerAPI |
| µP: A Development Framework for Predicting Performance of Microservices by Design | |
| HydraGen: A Microservice Benchmark Generator | |
| Verschiedenes | Galaxy: open, web-based platform for accessible, reproducible, and transparent computational research (galaxyproject.org) |
| Software, tools, and respositories for code mining (D3.1 from the Morphemic EU project) | |
| A survey on edge performance benchmarking, Blesso Varghese et al. | |
| New Directions in Cloud programming, A. Cheung et al | |
| Consistency analysis in Bloom: ca CALM and collected approach | |
| CAMEL: Cloud application modellling and execution language | |
| PLEDGER: benchmarking for the cloud | |
| Riotbench: an IoT benchmark for distributed stream processing systems. | |
| Unikraft OS toolki8t for lightweight OS images |
T. Fahringer, Institut für Informatik, Universität Innsbruck