Transcript By: Bryan Bishop
Arbitrum 2.0: Faster Off-Chain Contracts with On-Chain Security
Ed Felten, Off-chain Labs
Thank you, good morning everybody. I’m going to talk about our latest version of our Abitrum system. The first version was discussed in a paper in 2018. Since then, there’s a lot of advances in my technology and a real running system has been made. This is the first working roll-up system for general smart contracts.
First I will set the scene to provide some context on what we’re talking about and how what we’re doing fits into the larger picture. Then I’ll talk about how the system works, what has changed since the last paper, and some lessons learned from building and deploying it.
Layer 2 protocols
So what about layer 2 systems that support smart contracts? They generally rely on having a set of parties like validators that are responsible for tracking the state of the layer 2 chain and taking action to make sure it develops correctly.
What is the assumption about how many validators you trust? There’s usually k-of-n validator honesty requirement, like (n+1)/n. On the right, you have systems where you assume only 1/n validators are honest. That one validator wins out against n-1 malicious validators.
Today we will be talking about 1/n trust assumption.
The next branch here is whether there’s a fixed validator set, or a situation where anyone can be a validator. When there’s anyone that can be a validator, that’s typically called a roll-up protocol. The alternative is to have a fixed validator set. At any given time, there’s a single enumerated set of validators. In that subset, the best known type is a state channel approach.
In state channels, you operate entirely off-chain. If all the validators agree unanimously, then they all mutually sign and move on, and only at the end of the period of agreement do you have to take a signed receipt and cash out on chain. State channels are incredibly efficient when everyone agres and participates, but if there’s any validator that stops, then you have to fall back to doing everything on the main chain.
In our 2018 paper, we introduced Arbitrum channels which is a hybrid of state channels and a fixed set of validators– when they are all operating by unanimous agreement you operate off-chain like a state channel. But when you lose unanmity, you fallback to a roll-up permissioned chain.
I am going to be talking about Arbitrum channels- the roll-up part of the protocol, which is the most interesting and complex part of the Arbitrum channel protocols.
Let me drill down into how a system like ours and similar ones work. So you have some amount of source code that represents a smart contract, it might be some legacy Solidity code or something out. You’re going ot take that source code, run it through a custom compiler, and generate an executable program that runs on a virtual machine that the Layer 2 system is implementing. Once you have that layer 2 executable, then you can launch your chain by launching a set of validators. Each validator has an emulator for emulating the VM and it can emulate programs in that VM architecture. You run the program on that virtual machine.
If all goes well, and all the validators are honest, then all the validators will have equivalent replicas of that VM as it runs. Dishonest validators will do what they do. But honest validators if the protocol is correct will always agree and have a replication of the state. They will agree and make progress quickly.
If they don’t agree, then they interact with an on-chain contract and that contract is responsible for the ultimate abjudication for any dispute betwee nvalidators. So the special sauce is how to make this on-chain manager contract as small and fast as possible, while still getting generality.
Our first goal was to run general-purpose code and contracts. We want it fast and cheap, even with a slower layer 1. We want that any any-trust guarantees of safety, liveness and finality. Any one party acting alone can ensure that this property holds. Safety means that no bad state change will occur. Safety means that no bad thing will happen. Liveness means that some good thing will eventually happen, which combined with safety means some good thing will eventually happen. Finality means that if a valid state change is proposed and it’s pending, then it will eventually be confirmed, which allows parties as “fated to be confirmed” even before they are confirmed by the protocol, which allows faster responses for users.
We want to interop with the layer one chain so that you can make calls back and forth between contracts, or you want to move tokens back and forth. And we want censorship resistance as the underlying layer one chain.
The approach we took in building this is first of all, we did obvious stuff to minimize on-chain work and try to go fast. But in particula,r here are some distinguishing factors.
We wanted to run many separate layer 2 chains, which gives you more parallelism so that you can go faster in aggregate. It offers better incentives with respect to who is going to be a validator and who is going to pay for a chain. If your application is munched together with 10 other applications, then if you’re validating then you’re validating the things you care about but also the things from those 9 other applications. So coherent communities of interest has a better incentive structure than a single large chain.
On each chain, we run a single virtual machine instance. We don’t have a separate virtual machine per contract. All the activities on-chain should be run in a single program. So all the separate contracts and libraries thta might be runninng on the chain. We also need to have a runtime system built into that system able to do bookkeeping, check signatures, check formats, and various other things you would do in a runtime system or operating system on a conventional machine. The role of that runtime system is important for both efficiency and correctness. Having that runtime system is a big benefit.
We want to create incentives to keep the validators in the fastest mode.
We also pay attention to the safe speed limit, which is the speed at which you can allow activity to happen without outrunning the ability of validators to keep up. Validators need to be able to check on everything that is happening. It’s possible to propose stuff faster than validators can check it. The safe speed limit is based on how fast the validators can operate. So we want to make sure validators can always run at full speed.
Two use cases, two protocols
There’s the Arbitrum Rollup protocol in which anyone can be a validator. As in any roll-up protocol, if you want anyone to be a validator then you need to make sure they can get the information to become a validator. They need to find this information on the main chain so they can get up to date. If someone gives them a recent checkpoint of the state of the system, then there needs to be enough information on-chain to verify that. In roll-up protocols, we have a protocol based on proposals, and proposals are challengeable, and if they are challenged then there’s a dispute.
In the Arbitrum Channel protocol, there’s an enumerated set of validators, it can be private so that only validators know the full state. It’s just like a state channel, and when they aren’t in cooperation, then they fallback to the Arbitrum Rollup protocol.
I’ll talk about the roll-p protocol, resisting delay attacks with a branch-and-prune state management. And then how to handle time, messages and interoperability in a layer 2 situation.
We have something called a roll-up block or assertion. It’s a claim about the execution of a VM in a chain. This might cover many transactions worth of work. Any validator can post an on-chain assertion which is a claim about what the chain will do or has done. It consists of a set of messages that the chain is consuming from the head of its inbox. These are things like requests to the contract to execute transactions, or incoming transfers of currency. There’s a number of steps that can be executed by the chain’s VM. It includes a root hash of the state machine after those steps are executed. The entire machine is organized in a merkle tree so you can summarize its state in a single hash. Then it asserts a set of outputs that are allegedly produced by the execution, like events, payments, log items made by those contracts. The arbitrum protocol decides after an assertion is made, whether to accept or reject an assertion.
How does that work? Well, there’s a state tree. I’m going to start by storing in this state… you can imagine that the history of this chain extends way off to the left. There’s a long history back to some genesis state. Alice places a stake on her assertion, claiming that the state will eventually be confirmed. The rules of the protocol say that if and when that state is confirmed, Alice can get her stake back. If the state is rejected, then Alice will lose her stake and it will go somewhere else. Once she has staked, a challenge period begins and every validator has some amount of time to check the assertion, decide if it is right, and if it is wrong then to challenge the assertion from Alice.
Say a validator sees that the assertion is correct. The validator can choose to stake on that same state node. The rules of his stake are going to be the same: if the assertion is confirmed, then he gets his stake back. The deadline runs out, let’s say, and nobody else can stake for or against that assertion. The system looks at this and says everyone who has stake is staked on the same branch, therefore we have unanimous agreement, so we’re going to go ahead and confirm that node. The top node will be confirmed, Alice and Bob gets their stake back, and time goes on.
But another thing that can happen is that another validator can stake on a state where Alice’s assertion is rejected. So now we have a disagreement between Alice and Bob on one hand, and Charlie on the other. So they enter into a dispute, and they execute an efficient dispute resolution which involves an interactive protocol where they use bisections to narrow down the scope of the dispute to a single instruction and then an on-chain instruction evaluates that single instruction. Then the dispute can be resolved on-chain. When Charlie loses, half the stake goes to Alice, the other stake gets burned, and then Charlie gets booted from the system. Then the protocol runs again and Alice and Bob are in agreement and the state gets turned green.
What happens when the challenge interval is running? We’d like to be able to pipeline these assertions. While Alice’s assertion is under consideration, Bob can make another assertion on top of Alice’s. He can pick any leaf from the tree, and make an assertion, and cause two leafs to sprout from that. By staking on that state node, he’s also implicitly staking on the correctness of Alice’s assertions because Bob’s stake cannot be confirmed unless Alice’s have been confirmed place. So he’s not undoing any commitments he has made before, he’s just committing to more stuff. Bob has made a new assertion, he moved his stake, and Alice might make another assertion on top of Bob’s even though her first one is still under consideration, and she is still required to stake.
The normal state of an operating chain is like this: you have a linear pipeline sequence of assertions and nobody wants to lose stake by asserting something false… They aren’t challenged, because nobody wants to lose their stake. So you have a frontier of assertions marching along, and behind you have a frontier of confirmations marching along. By having a pipeline, we can keep the validators always busy, and we don’t hav to keep the system waiting.
So we can make progress even when there’s disputes people can keep working out the truthful branch. Honest validators can always continue building the truthful branch. Dishonest validators can build out a false branch, but everyone else can ignore it knowing that eventually that branch will be proven away.
Efficient on-chain tracking
This might sound expensive to track this on-chain, but there are some tricks to reduce those costs. You can summarize the state. You have to track the hash of every leaf of the tree. If you have the hash of a leaf and the root of the tree, then you can prove any node is a member of the tree by using a set of merkle proofs. For each staker, you need the identity and the hash of the staking action.
This gives you trustless finality. You get the trustless properties I was talking about, happy to explain why out-of-band later.
Why time is hard to handle in layer two
The problem is that legacy programs like in Solidity it likes to ask what’s the current block number in the first layer? The result of this is determined when the transaction is put on-chain. But in the layer 2 transaction, you have to propose what happens in the transaction. It can’t be determined later when it gets adopted on-chain. So the outcome of the assertion has to be unique, and it can’t change over time. Also, layer 2 execution is asynchronous from the layer one clock.
In a previous paper, we talked about using time as a precondition where you label each assertion with lower and upper bounds about when the assertion can be accepted. The assumption is kept as moot outside those bounds. The application can ask, what are the current time bounds? That’s deterministic, and always correct. Because the assertion is moot outside those time bounds, it will always be true at the time that the assertion is accepted. Execution is deterministic. You’re given an interval, but real applications want a single scalar time.
So what do you do when the app asks for the time? One good solution is to take the max over the lower time bounds of all the assertions you’ve seen so far, and tell that to the application as the current time. It will never decrease, and it’s always within the time bounds. But the layer 2 runtime has to prevent the application from seeing anomalies. This might lag behind the real itme. There might be incoming messages that are timestamped after that, and you don’t want messages to appear to becomng from the future.
The l2 runtime system can see the current timebounds but also messages pegged in the future. We know what we’re doing; the l2 runtime is able to show a consistent and sensible notion of time to the application. So this is another example of how the use of an l2 runtime is pretty beneficial.
The branch-and-prune approach to state management allows high performance and strong guarantees of safety, liveness and finality. There’s significant advantages for compiling everything into a single L2 program per chain, consisting of a substantial runtime component in that. There’s also a surprising amount of “systems problem solving” needed to make this work. You have to solve a bunch of difficult problems to get this to work.
Arbitrum Rollup is a commercial product. We have plugins for the standard front-end system so you can port existing applications to run in L2 and get those benefits. We have easy tooling for launching your dApp. This is the first roll-up for general contracts to be working on testnet. Also, we’re hiring. Thank you.
Disclaimer: These are unpaid transcriptions, performed in real-time and in-person during the actual source presentation. Due to personal time constraints they are usually not reviewed against the source material once published. Errors are possible. If the original author/speaker or anyone else finds errors of substance, please email me at email@example.com for corrections or contribute online via github/git. I sometimes add annotations to the transcription text. These will always be denoted by a standard editor’s note in parenthesis brackets ((like this)), or in a numbered footnote. I welcome feedback and discussion of these as well.
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