Book ; Online: Pilotfish
Distributed Transaction Execution for Lazy Blockchains
2024
Abstract: Pilotfish is the first scale-out blockchain execution engine able to harness any degree of parallelizability existing in its workload. Pilotfish allows each validator to employ multiple machines, named ExecutionWorkers, under its control to scale its ... ...
| Abstract | Pilotfish is the first scale-out blockchain execution engine able to harness any degree of parallelizability existing in its workload. Pilotfish allows each validator to employ multiple machines, named ExecutionWorkers, under its control to scale its execution layer. Given a sufficiently parallelizable and compute-intensive load, the number of transactions that the validator can execute increases linearly with the number of ExecutionWorkers at its disposal. In addition, Pilotfish maintains the consistency of the state, even when many validators experience simultaneous machine failures. This is possible due to the meticulous co-design of our crash-recovery protocol which leverages the existing fault tolerance in the blockchain's consensus mechanism. Finally, Pilotfish can also be seen as the first distributed deterministic execution engine that provides support for dynamic reads as transactions are not required to provide a fully accurate read and write set. This loosening of requirements would normally reduce the parallelizability available by blocking write-after-write conflicts, but our novel versioned-queues scheduling algorithm circumvents this by exploiting the lazy recovery property of Pilotfish, which only persists consistent state and re-executes any optimistic steps taken before the crash. In order to prove our claims we implemented the common path of Pilotfish with support for the MoveVM and evaluated it against the parallel execution MoveVM of Sui. Our results show that our simpler scheduling algorithms outperforms Sui even with a single execution worker, but more importantly provides linear scalability up to 4 ExecutionWorkers even for simple asset-transfers and to any number of ExecutionWorkers for more computationally heavy workloads. |
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| Keywords | Computer Science - Distributed ; Parallel ; and Cluster Computing |
| Subject code | 004 |
| Publishing date | 2024-01-29 |
| Publishing country | us |
| Document type | Book ; Online |
| Database | BASE - Bielefeld Academic Search Engine (life sciences selection) |
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