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. |
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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 |
On the FaaS Track: Buidling stateful distributed applications with serverless architectures | |
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 | |
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 | |
Workflows allgemein | 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 | |
Scheduling | 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 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 | |
Resource management | Resource managemeent in fog/edge computing: a survey on architectures, infrastructure, and algorithms |
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 | |
Verschiedenes | Galaxy: open, web-based platform for accessible, reproducible, and transparent computational research (galaxyproject.org) |
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