Overview

This repository contains files for conducting experiments related to the paper A Cost Model to Optimize Queries over Heterogeneous Federations of RDF Data Sources.

We have implemented the cost model introduced in the paper (as well as the baseline approach) in our query federation engine called HeFQUIN.

For running the experiments we utilize KOBE, a benchmarking system based on Kubernetes infrastructure, to containerize and configure federations of RDF datasets, queries, federation engines, and experiments. The typical workflow for defining a KOBE experiment includes the following steps: 1. DatasetTemplate: Create one for each dataset server used in your benchmark. 2. Benchmark: Define it with a list of datasets and queries. 3. FederatorTemplate: Create one for the federator engine used in your experiment. 4. Experiment: Define it over your previously defined benchmark.

Installation

Prerequisites

Before installing the KOBE benchmarking engine, ensure the following prerequisites are met:

• kubectl: Install using native package management. Detailed instructions for Linux can be found here. The experiments in the paper use version v1.20.7. Verify the installation: bash kubectl version --client
• nfs-common: Install on the nodes of the cluster. For Debian or Ubuntu: bash apt-get install nfs-common
• minikube: Detailed installation instructions can be found here.

Starting the Cluster

Start your cluster using the following commands: bash minikube start kubectl get po -A minikube dashboard Open the Minikube dashboard locally by accessing the default port in your browser: http://127.0.0.1:44393/api/v1/namespaces/kubernetes-dashboard/services/http:kubernetes-dashboard:/proxy/

If the cluster is started on a remote server, such as ontology.ida.liu.se, use SSH tunneling to access the dashboard.

Install KOBE

A detailed instruction for installing KOBE can be found here. There are some notes for the installation. 1. When installing the Networking subsystem, you can consult the official installation guide or follow the steps below. - To download 'istio' with specific verion, use the follwoing curl command: curl -L https://istio.io/downloadIstio | ISTIO_VERSION=1.11.3 TARGET_ARCH=x86_64 sh - - Then, add the istioctl client to your path (Linux or macOS): export PATH=$PWD/istio-1.11.3/bin:$PATH - Then, install Istio using the following command: istioctl install --set profile=demo -y 2. Before installing the logging subsystem, 'helm' need to be installed. Note the current commend only works for helm2. An instruction to install and set up helm2 and tiller can be found here

After set up all required components, you can check status of all Pods via browswer, or via command: kubectl get pods

Conducting Experiments

First, make sure the cluster is running minikube status 1. Deploy dataset servers: Virtuoso, TPF, and brTPF server bash kobectl apply dataset/dataset-virtuoso/virtuosotemplate.yaml kobectl apply dataset/dataset-ldfserver-hdt/ldfservertemplate-hdt.yaml kobectl apply dataset/dataset-brtpfserver/brtpfservertemplate.yaml For the TPF server and brTPF server, several docker images are used and corresponding source code for these images can be found in the corresponding directory. If any changes applied, these docker images need to be rebuilt using the following command: - TPF server: ``` cd dataset/dataset-ldfserver-hdt cd ldfserver-init-hdt docker build --no-cache -t chengsijin817/ldfserver-init-hdt . docker push chengsijin817/ldfserver-init-hdt

cd ../ldfserver-main-hdt docker build --no-cache -t chengsijin817/ldfserver-main-hdt . docker push chengsijin817/ldfserver-main-hdt ``` Note: 'chengsijin817/ldfserver-init-hdt' and 'chengsijin817/ldfserver-main-hdt' are image names, which can be renamed but should be the same as specified in ldfservertemplate-hdt.yaml file.

• brTPF server: ``` cd dataset/dataset-brtpfserver cd brtpfserver-init docker build --no-cache -t chengsijin817/brtpfserver-init . docker push chengsijin817/brtpfserver-init

cd ../brtpfserver-main docker build --no-cache -t chengsijin817/brtpfserver-main . docker push chengsijin817/brtpfserver-main ``` Similarly, 'chengsijin817/brtpfserver-init' and 'chengsijin817/brtpfserver-main' are image names, which can be renamed but should be the same as specified in brtpfservertemplate.yaml file.

1. Deploy a benchmark, specifying all federation members and benchmark queries. kobectl apply benchmark-fedbench/fedbench-het3-nodelay.yaml kobectl show benchmark fedbench-het3-nodelay Note: Queries should use correct URIs in SERVICE clause, depending on the type of interface of each federation member. Two example federations are provided under directory 'benchmark-fedbench'.

2. Deploy HeFQUIN engine

Use one of the following commands to apply a implementation of HeFQUIN engine: kobectl apply federator-hefquin/hefquin-mincost-greedy.yaml Or kobectl apply federator-hefquin/hefquin-card-greedy.yaml For example, hefquin-mincost-greedy.yaml invokes a docker image of HeFQUIN engine, which implements cost-based greedy algorithm applied.

To integrate the HeFQUIN engine with the KOBE system, use the following Docker images: 'chengsijin817/hefquin-init' and 'chengsijin817/hefquin-init-all'. The source code for these images can be found in the repository directory. To rebuild these images after updates: ```bash cd federator-hefquin/hefquin-init docker build --no-cache -t chengsijin817/hefquin-init . docker push chengsijin817/hefquin-init

cd ../hefquin-init-all docker build --no-cache -t chengsijin817/hefquin-init-all . docker push chengsijin817/hefquin-init-all 4. Deploy experiments based on the provided configuration files, fDepending on the algorithm and federation used, apply the appropriate experiment configuration: bash kubectl apply -f experiment/fed3nodelay-mincostgreedy-exp.yaml

or

kubectl apply -f experiment/fed4nodelay-mincostgreedy-exp.yaml

or

kubectl apply -f experiment/fed3nodelay-cardgreedy-exp.yaml

or

kubectl apply -f experiment/fed4nodelay-cardgreedy-exp.yaml ```

After completion, download the log file via the Minikube dashboard.

Cleaning Up Before Next Experiment

To clean up all components: bash kubectl delete experiments.kobe.semagrow.org --all kubectl delete benchmarks.kobe.semagrow.org --all kubectl delete federatortemplates.kobe.semagrow.org --all kubectl delete datasettemplates.kobe.semagrow.org --all kubectl delete pod kobenfs Alternatively, remove specific components using the following command: bash kubectl delete experiment het3nodelay-mincostgreedy-exp kubectl delete federatortemplate hefquintemplate-mincost-greedy

Stopping the Cluster

To stop the cluster: bash minikube status minikube stop minikube delete Note: Previous experiment results will be lost upon restarting Minikube.