This project is a prototype implementation of the VSSR protocol proposed by Basu et al..

We implemented VSSR on top of the modified BFT-SMaRt library. You can find the modified version of the library here.

The prototype is implemented in Java and was tested using Java 11.0.13.

First, clone this repository. Now inside the main directory VSSR (assuming you did not change the name), execute the following command to compile and package this implementation:

./gradlew installDist

The required jar files and default configurations files will be available inside the VSSR/build/install/VSSR directory.

Since this VSSR prototype extends the BFT-SMaRt library, first configure BFT-SMaRt following instructions presented in its repository. Next, configure VSSR by changing the VSSR/config/vssr.config file.

TIP: Reconfigure the system before compiling and packaging. This way, you don't have to configure multiple replicas.

NOTE: The following commands assume the Linux operating system. For the Windows operating system, use script run.cmd instead of smartrun.sh.

Running throughput and latency experiment:

After compiling and packaging, copy the content of the VSSR/build/install/VSSR directory into different locations/servers. Next, we present an example of running a system with four replicas tolerating one fault.

Execute the following commands across four different server consoles:

./smartrun.sh vssr.benchmark.ThroughputLatencyKVStoreServer 0
./smartrun.sh vssr.benchmark.ThroughputLatencyKVStoreServer 1
./smartrun.sh vssr.benchmark.ThroughputLatencyKVStoreServer 2
./smartrun.sh vssr.benchmark.ThroughputLatencyKVStoreServer 3

Once all replicas are ready, you can launch clients by executing the following command:

./smartrun.sh vssr.benchmark.PreComputedKVStoreClient <initial client id> <num clients> <number of ops> <request size> <write?> <precomputed?>

where:

• <initial client id> - the initial client id, e.g, 100;
• <num clients> - the number clients each execution of command will create, e.g., 20;
• <number of ops> - the number of requests each client will send, e.g., 10000;
• <request size> - the size in byte of each request, e.g., 1024;
• <write?> - requests are of write or read type? E.g., true;
• <precompute?> - are the requests precompute before sending to servers or are created on fly? E.g., true;

Interpreting the throughput and latency results

When clients continuously send the requests, servers will print the throughput information every two seconds. When a client finishes sending the requests, it will print a string containing space-separated latencies of each request in nanoseconds. For example, you can use this result to compute average latency.