README.md

Attention Kalman Filter for Short Horizon Prediction and Resource Allocation under Highly Variable Cloud Workloads

What is KF-PCA

Papers on Ksurf & Ksurf+

Disclaimer

This readme is a work in progress and may not yet cover all the features outlined in the papers above

Setup

vim config/testbed.properties

Set Tracker properties

If not installed

pip install -r requirements.txt

Get most recent data from Twitter API

python src/twitter_search.py

How to run the Kafka estimator

sh src/kafka/setup.sh -h
python -m flask run

OR

python src/kafka/consumer.py

Single test

sh src/kafka/setup.sh  --runtest

Multi-test

sh src/kafka/setup.sh --runtests 10

Result data

ls data_tracker.pickle.csv*

Workload data

ls data/

KF accuracy tests

Edit tracker.type = KF/UKF/EKF/EKF-PCA/AKF-PCA config

sed -i s/tracker.type=.*/tracker.type=EKF-PCA/ config/testbed.properties

Run KF csv-based estimator test

python src/ekf.py --testpcacsv -f data/twitter_trace.csv -x 'Tweet Count' -y 'Tweet Count' > out_ekfpca.txt

View summary stats

tail out_ekfpca.txt

Run Adversarial Tests

python src/adversarial_tester.py -f data/analysis/data_kf_2_node_metrics.csv -x "ksurf-master CPU (m)"

or simply

python src/adversarial_tester.py