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Mucache Experiments

Prerequisites

Cloudlab Cluster

Initialize a cluster of 13 machines on Cloudlab using the following profile: https://www.cloudlab.us/p/8e054430c6b73652669ec36f24c1ecb716f80c46

Wait until all nodes are ready, download the minifest file as ./manifest.xml.

Controller Machine

The controller send files and commands to all worker machines. Any machine (e.g. Your host) that has Python3 can acts as the controller.

On the controller

pip3 install fabric
git clone https://github.com/eniac/mucache

copy the minifest file to the mucache/

Setup

On the controller

cd mucache
export node_username=${username_of_cloudlab}
export private_key=${ssh_key_location}
python3 scripts/host/upload.py
python3 scripts/host/setup.py

After it finishes, login into node-0 and you should see the following:

kubectl get nodes
NAME     STATUS   ROLES           AGE    VERSION
node-0   Ready    control-plane   168m   v1.26.1
node-1   Ready    <none>          168m   v1.26.1
node-2   Ready    <none>          167m   v1.26.1
node-3   Ready    <none>          167m   v1.26.1
node-4   Ready    <none>          167m   v1.26.1
node-5   Ready    <none>          167m   v1.26.1
...

Run

Build applications

In this artifact, we have four open-source microservice applications, including SocialMedia, MovieReview, HotelRes, and OnlineBoutique; we have four synthetic applications, including Proxy, Chain, Fanout, and Fanin. To run these applications, you can use our pre-built images on dockerhub.

Alternatively, you can build the images on any x86 machines and push to dockerhub by running

./scripts/host/build_and_push=${docker_io_username} # application image
./scripts/cm/build_and_push=${docker_io_username} # cache manager image

If you're using pre-built images, set on the controller node

export docker_io_username=tauta

Cache Size (Figure 13)

python3 experiments/cachesize/run.py

The results are printed to the console and saved in hotel-md.json and hotel-md-upper.json. hotel-md.json contains a json map with keys being the cache size in MB and values being the p50 and p95 latency. hotel-md-upper is the TTL-inf baseline.

Microbenchmark (Figure 14)

To run the three microservices, Chain, Fanout and Fanin.

python3 experiments/chain/run.py # chain
python3 experiments/star/run.py  # fanout
python3 experiments/fanin/run.py # fanin

The results will be saved to {APP}-baseline.json for baseline and {APP}.json for mucache.

Hitrate (Figure 17)

python3 experiments/twoservices/hitrate.py

The results are saved at hitrate_hdr_baseline for the baseline, hitrate_hdr_{hit_rate} for mucache and hitrate_hdr_{hit_rate}_upper for TTL-inf.

Real-world applications (Figure 10)

To run the four real-world applications, run

python3 experiments/movie/run.py
python3 experiments/hotel/run.py
python3 experiments/boutique/run.py
python3 experiments/social/run.py

The scripts will print the output and write the results to {APP}-upper.json, {APP}-baseline.json and {APP}.json, which correspond to TTL-inf, baseline and mucache respectively.

Different TTL baselines (Figure 11)

python3 experiments/hotel/ttl.py

The script will print the output and write and result to hotel-upper-ttl.json.

Sharding (Figure 12)

To build the applications and cache manager for the sharding example, run

./scripts/host/shard_build_and_push=${docker_io_username} # application image
./scripts/cm/shard_build_and_push=${docker_io_username} # cache manager image