Runtime Layer

Runtime Layer

The Oasis Core runtime layer enables independent runtimes to schedule and execute stateful computations and commit result summaries to the consensus layer. In addition to verifying and storing the canonical runtime state summaries the consensus layer also serves as the registry for node and runtime metadata, a scheduler that elects runtime compute committees and a coordinator for key manager replication.

Runtimes

A runtime is effectively a replicated application with shared state. The application can receive transactions from clients and based on those it can perform arbitrary state mutations. This replicated state and application logic exists completely separate from the consensus layer state and logic, but it leverages the same consensus layer for finality with the consensus layer providing the source of canonical state. Multiple runtimes can share the same consensus layer.

In Oasis Core a runtime can be any executable that speaks the Runtime Host Protocol which is used to communicate between a runtime and an Oasis Core Node. The executable usually runs in a sandboxed environment with the only external interface being the Runtime Host Protocol. The execution environment currently includes a sandbox based on Linux namespaces and SECCOMP optionally combined with Intel SGX enclaves for confidential computation.

Runtime Execution

In the future this may be expanded with supporting running each runtime in its own virtual machine and with other confidential computing technologies.

Operation Model

The relationship between consensus layer services and runtime services is best described by a simple example of a "Runtime A" that is created and receives transactions from clients (also see the figure above for an overview).

  1. The runtime first needs to be created. In addition to developing code that will run in the runtime itself, we also need to specify some metadata related to runtime operation, including a unique runtime identifier, and then register the runtime.

  2. We also need some nodes that will actually run the runtime executable and process any transactions from clients (compute nodes). These nodes currently need to have the executable available locally and must be configured as compute nodes.

  3. In addition to compute nodes a runtime also needs storage nodes to store its state.

  4. Both kinds of nodes will register on the consensus layer announcing their willingness to participate in the operation of Runtime A.

  5. After an epoch transition the committee scheduler service will elect registered compute and storage nodes into different committees based on role. Elections are randomized based on entropy provided by the random beacon.

  6. A client may submit transactions by querying the consensus layer to get the current executor committee for a given runtime, connect to it, publish transactions and wait for finalization by the consensus layer. In order to make it easier to write clients, the Oasis Node exposes a runtime client RPC API that encapsulates all this functionality in a SubmitTx call.

  7. The transactions are batched and proceed through the transaction processing pipeline. At the end, results are persisted to storage and the roothash service in the consensus layer finalizes state after verifying that computation was performed correctly and state was correctly persisted.

  8. The compute nodes are ready to accept the next batch and the process can repeat from step 6.

Note that the above example describes the happy path, a scenario where there are no failures. Described steps mention things like verifying that computation was performed correctly and that state was correctly stored. How does the consensus layer actually know that?

Discrepancy Detection and Resolution

The key idea behind ensuring integrity of runtime computations is replicated computation with discrepancy detection. This basically means that any computation (e.g., execution of a transaction) is replicated among multiple compute nodes. They all execute the exact same functions and produce results, which must all match. If they don't (e.g., if even a single node produces different results), this is treated as a discrepancy.

In case of a discrepancy, the computation must be repeated using a separate larger compute committee which decides what the correct results were. Since all commitments are attributable to compute nodes, any node(s) that produced incorrect results may be subject to having their stake slashed and may be removed from future committees.

Given the above, an additional constraint with replicated runtimes is that they must be fully deterministic, meaning that a computation operating on the same initial state executing the same inputs (transactions) must always produce the same outputs and new state. In case a runtime's execution exhibits non-determinism this will manifest itself as discrepancies since nodes will derive different results when replicating computation.

Compute Committee Roles and Commitments

A compute node can be elected into an executor committee and may have one of the following roles:

  • Primary executor node. At any given round a single node is selected among all

    the primary executor nodes to be a transaction scheduler node (roughly equal

    to the role of a block proposer).

  • Backup executor node. Backup nodes can be activated by the consensus layer in

    case it determines that there is a discrepancy.

The size of the primary and backup executor committees, together with other related parameters, can be configured on a per-runtime basis. The primary nodes are the ones that will batch incoming transactions into blocks and execute the state transitions to derive the new state root. They perform this in a replicated fashion where all the primary executor nodes execute the same inputs (transactions) on the same initial state.

After execution they will sign cryptographic commitments specifying the inputs, the initial state, the outputs and the resulting state. In case computation happens inside a trusted execution environment (TEE) like Intel SGX, the commitment will also include a platform attestation proving that the computation took place in a given TEE.

The roothash service in the consensus layer will collect commitments and verify that all nodes have indeed computed the same result. As mentioned in case of discrepancies it will instruct nodes elected as backups to repeat the computation.

Storage Receipts

All runtime persistent state is stored by storage nodes. These provide a Merklized Key-Value Store (MKVS) to compute nodes. The MKVS stores immutable state cryptographically summarized by a single root hash. When a storage node stores a given state update, it signs a receipt stating that it is storing a specific root. These receipts are verified by the roothash service before accepting a commitment from a compute node.

Suspending Runtimes

Since periodic maintenance work must be performed on each epoch transition (e.g., electing runtime committees), fees for that maintenance are paid by any nodes that register to perform work for a specific runtime. Fees are pre-paid for the number of epochs a node registers for. If there are no committees for a runtime on epoch transition, the runtime is suspended for the epoch. The runtime is also suspended in case the registering entity no longer has enough stake to cover the entity and runtime deposits. The runtime will be resumed on the epoch transition if runtime nodes will register and the registering entity will have enough stake.