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Prototypes

Below, you can find a list of research prototypes that we are currently working on or have worked on in the past. To learn more about the prototypes, visit the linked Github pages.

 

GeoBroker

In the Internet of Things, the relevance of data often depends on the geographic context of data producers and consumers. Today’s data distribution services, however, mostly focus on data content and not on geo-context, which could help to reduce the dissemination of excess data in many IoT scenarios. We propose to use the geo-context information associated with devices to control data distribution. For this, we designed GeoBroker, a data distribution service that uses the location of things, as well as geofences for messages and subscriptions, to control data distribution. This way, we enable new IoT application scenarios while also increasing overall system efficiency for scenarios where geo-contexts matter by delivering only relevant messages.

Related publications:

  • GeoBroker: Leveraging Geo-Contexts for IoT Data Distribution (Hasenburg and Bermbach 2020) PDF
  • Towards Geo-Context Aware IoT Data Distribution (Hasenburg and Bermbach 2020) PDF
Repository
Description
github.com/MoeweX/geobroker
GeoBroker prototype

MQTT Broadcast Groups

Today, communication between IoT devices heavily relies on fog-based publish/subscribe (pub/sub) systems. Communicating via the cloud, however, results in a latency that is too high for many IoT applications. This project is about a fog-based pub/sub system that integrates edge resources to improve communication latency between end devices in proximity. To this end, geo-distributed MQTT compliant broker instances organize themselves in dynamically sized broadcast groups that are connected via a scale-able fog broker.

Related publication:

  • Managing Latency and Excess Data Dissemination in Fog-Based Publish/Subscribe Systems (Hasenburg et al. 2020) PDF
Repository
Description
github.com/MoeweX/moquette
Extension of moquette that supports broadcast groups
github.com/MoeweX/broadcast-group-simulation
A simulation of the broadcast group formation process

tinyFaaS

The Function-as-a-Service (FaaS) model is a great fit for data and event processing in the Internet of Things (IoT). Sending all data to a cloud-based FaaS platform, however, may cause performance and privacy issues. While these issues could be mitigated using edge computing, existing FaaS approaches, designed for the cloud, are too heavyweight to run on small, constrained edge nodes.

tinyFaaS is a new FaaS system that is specifically designed for edge environments and their unique challenges. It is lightweight enough to run on low-performance single machine edge nodes, provides a CoAP endpoint to support communication with low-power devices, and uses Docker containers to isolate tenants.

Related publications:

  • tinyFaaS: A Lightweight FaaS Platform for Edge Environments (Pfandzelter and Bermbach 2020) PDF
Repository
Description
github.com/OpenFogStack/tinyFaas
Main repository with the tinyFaaS prototype

MockFog

Fog computing is an emerging computing paradigm that uses processing and storage capabilities located at the edge, in the cloud, and possibly in between. Testing fog applications, however, is hard since runtime infrastructures will typically be in use or may not exist, yet.

MockFog is a tool that can be used to emulate such infrastructures in the cloud. Developers can freely design emulated fog infrastructures, configure their performance characteristics, and inject failures at runtime to evaluate their application in various deployments and failure scenarios.

Related publications:

  • MockFog: Emulating Fog Computing Infrastructure in the Cloud (Hasenburg et al. 2019) PDF
Repository
Description
github.com/OpenFogStack/MockFog-Meta
Meta repository with a presentation and a demo video
github.com/OpenFogStack/MockFog-IaC
MockFog Infrastructure as Code artifacts
github.com/OpenFogStack/MockFog-NodeManager
MockFog Node Manager
github.com/OpenFogStack/MockFog-Agent
MockFog Agent 
github.com/OpenFogStack/MockFogLight
A lightweight version of MockFog without a visual interface

FogExplorer

Fog application design is complex as it comprises not only the application architecture, but also the runtime infrastructure, and the deployment mapping from application modules to infrastructure machines. For each of these aspects, there is a variety of design options that all affect quality of service and cost of the resulting application. FogExplorer, is an interactive simulation tool for the QoS and cost evaluation of fog-based IoT applications already during the design phase.

Related publications:

  • Supporting the Evaluation of Fog-based IoT Applications during the Design Phase (Hasenburg et al. 2018) PDF
  • FogExplorer (Hasenburg et al. 2018) PDF
Repository
Description
github.com/OpenFogStack/FogExplorer/
FogExplorer prototype

FBase

The combination of edge and cloud in the fog computing paradigm enables a new breed of data-intensive applications. These applications, however, have to face a number of fog-specific challenges which developers have to repetitively address for every single application.

FBase is a replication service specifically tailored to the needs of data-intensive fog applications that aims to ease or eliminate challenges caused by the highly distributed and heterogeneous environment fog applications operate in.

Related publications:

  • FBase: A Replication Service for Data-Intensive Fog Applications (Hasenburg and Bermbach 2019) PDF
  • Towards a Replication Service for Data-Intensive Fog Applications (Hasenburg and Bermbach 2020) PDF
Repository
Description
github.com/OpenFogStack/FBase
Main repository with the FBase system
github.com/OpenFogStack/FBaseNamingService
The FBase Naming Service
github.com/OpenFogStack/FBaseCommons
Common utility classes used by FBase and the FBase Naming Service
github.com/OpenFogStack/FBaseExample
Example FBase setup that uses Vagrant and VirtualBox 

SimRa

An increased modal share of bicycle traffic is a key mechanism to reduce emissions and solve traffic-related problems. However, a lack of (perceived) safety keeps people from using their bikes more frequently. To improve safety in bicycle traffic, city planners need an overview of accidents, near miss incidents and bike routes. 

Such information, however, is currently not available. In our platform called Simra, we collect data on bicycle routes and near miss incidents using smartphone-based crowdsourcing.

Repository
Description
github.com/simra-project/simra-android

The SimRa app for Android
github.com/simra-project/simra-ios
The SimRa app for iOS
github.com/simra-project/backend

The SimRa backend software
github.com/simra-project/dataset

Result data from the SimRa project
github.com/simra-project/screenshots

Screenshots of both the iOS and Android app
github.com/simra-project/SimRa-Visualization

Web application for visualizing the dataset

Benchmarking in CI/CD

Continuous integration and deployment are established paradigms in modern software engineering. Both intend to ensure the quality of software products and to automate the testing and release process. Today’s state of the art, however, focuses on functional tests or small microbenchmarks such as single method performance while the overall quality of service (QoS) is ignored. We want to extend these pipelines with an additional application benchmark step which ensures QoS requirements and prevents performance regressions. Our current focus is centered on the evaluation of individual microservices and microservice applications.

Our benchmark plugin analyzes results and detects the violation of fixed performance metrics (e.g., defined in SLAs), sudden significant performance fluctuations, and long lasting regression trends. Our openISBT tool to benchmark arbitrary REST microservices generates its workload based on interaction patterns and the openAPI interface description of the respective service(s).

Related publications:

  • Continuous Benchmarking: Using System Benchmarking in Build Pipelines (Grambow et al. 2019) PDF
  • Benchmarking Microservice Performance: A Pattern-based Approach (Grambow et al. 2020)
    PDF
Repository
Description
github.com/jenkinsci/benchmark-plugin
This Plugin analyzes benchmark results and decides whether the release fulfills predefined QoS goals
github.com/martingrambow/openISBT
Universal benchmark tool for REST microservices

Watchyourfac.es

In public video surveillance, there is an inherent conflict between public safety goals and privacy needs of citizens. Generally, societies tend to decide on middleground solutions that sacrifice neither safety nor privacy goals completely.

Watchyourfac.es is a prototype that leverages the inherent geo-distribution of fog computing to preserve privacy of citizens while still supporting camera-based digital manhunts of law enforcement agencies.

Related publications:

  • Public Video Surveillance: Using the Fog to Increase Privacy (Grambow et al. 2018) PDF
Repository
Description
github.com/OpenFogStack/PublicVideoSurveillance
Watchyourfac.es prototype

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