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Implementation of the swarm computer for thesis "Organization of computations in a swarm using Hashgraph consensus technology"

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The Swarm

Swarm of heterogeneous computers with ability to perform distributed computations.

Utilizes our implementation of Hashgraph consensus algorithm.

Currently supports only addition, subtraction, and inversion in Galois field of order $2^8$. (Basically only addition, because subtraction is exactly addition, and inversion is equality).

This project was written as Bachelor's degree thesis within Innopolis University in 2022-2023 academic year. Link for the published version will be placed [HERE] later.

Also it was started as 2022 Summer internship at Innopolis University. Schedule and linked reports for the internship can be found here.

Launch instructions

All nodes should be inside single LAN, because local area automatic peer discovery is used (with the help of mDNS from libp2p).

Manual run

Launch $N$ nodes, where $N$ is $n+k$ from Reed-Solomon encoding settings (particular code line)

Launch in terminal

RUST_LOG=info cargo run --release -- -i

Stop each process separately

Launch in docker

docker run --rm -it $(docker build -q .)

Stop each container separately

Run with docker compose

Create a network (needed because it's used in test scenarios), then

docker network create the-swarm-test-net-1
docker compose up -d

When finished, you can stop containers and remove the network:

docker compose down
docker network rm the-swarm-test-net-1

Test scenarios launch

The tests are written for Expect utility. Thus, it's required to run them.

All tests except for run_and_suspend require other peers to be launched manually (instructions for this scenario are described in Section "Run and suspend test"). In other words, for 3 peer network, the launch process can be the following:

  1. Launch 2 peers with cargo run --release -- -i
  2. Start the script with ./scenarios/run_simple.exp
  3. Wait for the result.

Note that launch arguments/enabled features may vary for different test cases. For example, test for large data requires big-array feature to be enabled: cargo run --release --features big-array -- -i. Appropriate command for a particular test you can find in the script files.

"Run and suspend" test

The test is aimed to check if the network works without one peer.

This test case automatically runs containers in a docker network with compose. To run use run script:

./scenarios/run_and_suspend/run_and_suspend.sh

If something failed during execution, you can use cleanup script to get rid of residual containers/networks:

./scenarios/run_and_suspend/cleanup.sh

CLI options

See --help for descriptions.

Interactive mode commands

Use help command to see the list with descriptions.

Debugging

Different log levels can be turned on with RUST_LOG environment variable. Details see in tracing-subscriber documentation.

It is possible to filter logs of message passing by specifying log levels for channel_recv or channel_send targets (to see logged send's and recv's respectively).

Also different workflows have separate targets:

Target Workflow
storage_init Storage initialization
data_distr Data distribution
program_exec Program execution
data_recollect Data recollection
sync Peer synchronization

For example, one can run

RUST_LOG="channel_recv=DEBUG,channel_send=DEBUG,program_exec=TRACE" cargo run --release

to see all debug channel logs, as well as all program execution logs.

Future work

  • Add disk storage option to work with larger data.
  • Use other erasure coding method to allow more instructions.
  • Make less assumptions in terms of security of the system.
  • Consider other errors apart from peer turning off, such as unintentional computation errors.
  • Flexible number of peers.
  • Flexible encoding.
  • Automatic shard loss detection. Addressing the loss before some data is lost.
  • Optimize this implementation and consensus.
  • Support larger data sizes (currently limited by stack).
  • Support not fully (directly) connected networks. Maybe use relays or something else.

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Implementation of the swarm computer for thesis "Organization of computations in a swarm using Hashgraph consensus technology"

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