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Consensus Simulation Framework

The Consensus Simulation Framework is a set of software components for describing, running and analyzing simulations of the consensus algorithm in a controlled manner. It is also used to unit test the generic Ripple consensus algorithm implementation. The framework is in its early stages, so the design and supported features are subject to change.

Overview

The simulation framework focuses on simulating the core consensus and validation algorithms as a discrete event simulation. It is completely abstracted from the details of the XRP ledger and transactions. In the simulation, a ledger is simply a set of observed integers and transactions are single integers. The consensus process works to agree on the set of integers to include in the next ledger.

CSF Overview

The diagram above gives a stylized overview of the components provided by the framework. These are combined by the simulation author into the simulation specification, which defines the configuration of the system and the data to collect when running the simulation. The specification includes:

  • A collection of Peers that represent the participants in the network, with each independently running the consensus algorithm.
  • The Peer trust relationships as a TrustGraph. This is a directed graph whose edges define what other Peers a given Peer trusts. In other words, the set of out edges for a Peer in the graph correspond to the UNL of that Peer.
  • The network communication layer as a BasicNetwork. This models the overlay network topology in which messages are routed between Peers. This graph topology can be configured independently from the TrustGraph.
  • Transaction Submitters that model the submission of client transactions to the network.
  • Collectors that aggregate, filter and analyze data from the simulation. Typically, this is used to monitor invariants or generate reports.

Once specified, the simulation runs using a single Scheduler that manages the global clock and sequencing of activity. During the course of simulation, Peers generate Ledgers and Validations as a result of consensus, eventually fully validating the consensus history of accepted transactions. Each Peer also issues various Events during the simulation, which are analyzed by the registered Collectors.

Example Simulation

Below is a basic simulation we can walk through to get an understanding of the framework. This simulation is for a set of 5 validators that aren't directly connected but rely on a single hub node for communication.

Example Sim

Each Peer has a unique transaction submitted, then runs one round of the consensus algorithm.

Sim sim;
PeerGroup validators = sim.createGroup(5);
PeerGroup center = sim.createGroup(1);
PeerGroup network = validators + center;
center[0]->runAsValidator = false;

validators.trust(validators);
center.trust(validators);

using namespace std::chrono;
SimDuration delay = 200ms;
validators.connect(center, delay);

SimDurationCollector simDur;
sim.collectors.add(simDur);

// prep round to set initial state.
sim.run(1);

// everyone submits their own ID as a TX and relay it to peers
for (Peer * p : validators)
    p->submit(Tx(static_cast<std::uint32_t>(p->id)));

sim.run(1);

std::cout << (simDur.stop - simDur.start).count() << std::endl;
assert(sim.synchronized());

Sim and PeerGroup

Sim sim;
PeerGroup validators = sim.createGroup(5);
PeerGroup center = sim.createGroup(1);
PeerGroup network = validators + center;
center[0]->runAsValidator = false;

The simulation code starts by creating a single instance of the Sim class. This class is used to manage the overall simulation and internally owns most other components, including the Peers, Scheduler, BasicNetwork and TrustGraph. The next two lines create two differ PeerGroups of size 5 and 1 . A PeerGroup is a convenient way for configuring a set of related peers together and internally has a vector of pointers to the Peers which are owned by the Sim. PeerGroups can be combined using +/- operators to configure more complex relationships of nodes as shown by PeerGroup network. Note that each call to createGroup adds that many new Peers to the simulation, but does not specify any trust or network relationships for the new Peers.

Lastly, the single Peer in the size 1 center group is switched from running as a validator (the default) to running as a tracking peer. The Peer class has a variety of configurable parameters that control how it behaves during the simulation.

trust and connect

validators.trust(validators);
center.trust(validators);

using namespace std::chrono;
SimDuration delay = 200ms;
validators.connect(center, delay);

Although the sim object has accessible instances of TrustGraph and BasicNetwork, it is more convenient to manage the graphs via the PeerGroups. The first two lines create a trust topology in which all Peers trust the 5 validating Peers. Or in the UNL perspective, all Peers are configured with the same UNL listing the 5 validating Peers. The two lines could've been rewritten as network.trust(validators).

The next lines create the network communication topology. Each of the validating Peers connects to the central hub Peer with a fixed delay of 200ms. Note that the network connections are really undirected, but are represented internally in a directed graph using edge pairs of inbound and outbound connections.

Collectors

SimDurationCollector simDur;
sim.collectors.add(simDur);

The next lines add a single collector to the simulation. The SimDurationCollector is a a simple example collector which tracks the total duration of the simulation. More generally, a collector is any class that implements void on(NodeID, SimTime, Event) for all Events emitted by a Peer. Events are arbitrary types used to indicate some action or change of state of a Peer. Other existing collectors measure latencies of transaction submission to validation or the rate of ledger closing and monitor any jumps in ledger history.

Note that the collector lifetime is independent of the simulation and is added to the simulation by reference. This is intentional, since collectors might be used across several simulations to collect more complex combinations of data. At the end of the simulation, we print out the total duration by subtracting simDur members.

std::cout << (simDur.stop - simDur.start).count() << std::endl;

Transaction submission

// everyone submits their own ID as a TX and relay it to peers
for (Peer * p : validators)
    p->submit(Tx(static_cast<std::uint32_t>(p->id)));

In this basic example, we explicitly submit a single transaction to each validator. For larger simulations, clients can use a Submitter to send transactions in at fixed or random intervals to fixed or random Peers.

Run

The example has two calls to sim.run(1). This call runs the simulation until each Peer has closed one additional ledger. After closing the additional ledger, the Peer stops participating in consensus. The first call is used to ensure a more useful prior state of all Peers. After the transaction submission, the second call to run results in one additional ledger that accepts those transactions.

Alternatively, you can specify a duration to run the simulation, e.g. sim.run(10s) which would have Peers continuously run consensus until the scheduler has elapsed 10 additional seconds. The sim.scheduler.in or sim.scheduler.at methods can schedule arbitrary code to execute at a later time in the simulation, for example removing a network connection or modifying the trust graph.