Adithya Bhat

Adithya Bhat, Harshal Bhadreshkumar Shah, Mohsen Minaei

In primary-backup replication, consensus latency is bounded by the time for backup nodes to replay (re-execute) transactions proposed by the primary. In this work, we present Ira, a framework to accelerate backup replay by transmitting compact \emph{hints} alongside transaction batches. Our key insight is that the primary, having already executed transactions, possesses knowledge of future access patterns which is exactly the information needed for optimal replay. We use Ethereum for our case study and present a concrete protocol, Ira-L, within our framework to improve cache management of Ethereum block execution. The primaries implementing Ira-L provide hints that consist of the working set of keys used in an Ethereum block and one byte of metadata per key indicating the table to read from, and backups use these hints for efficient block replay. We evaluated Ira-L against the state-of-the-art Ethereum client reth over two weeks of Ethereum mainnet activity ($100,800$ blocks containing over $24$ million transactions). Our hints are compact, adding a median of $47$ KB compressed per block ($\sim5\%$ of block payload). We observe that the sequential hint generation and block execution imposes a $28.6\%$ wall-time overhead on the primary, though the direct cost from hints is $10.9\%$ of execution time; all of which can be pipelined and parallelized in production deployments. On the backup side, we observe that Ira-L achieves a median per-block speedup of $25\times$ over baseline reth. With $16$ prefetch threads, aggregate replay time drops from $6.5$ hours to $16$ minutes ($23.6\times$ wall-time speedup).

Sri Aravinda Krishnan Thyagarajan, Adithya Bhat, Bernardo Magri et al.

Although blockchains aim for immutability as their core feature, several instances have exposed the harms with perfect immutability. The permanence of illicit content inserted in Bitcoin poses a challenge to law enforcement agencies like Interpol, and millions of dollars are lost in buggy smart contracts in Ethereum. A line of research then spawned on Redactable blockchains with the aim of solving the problem of redacting illicit contents from both permissioned and permissionless blockchains. However, all the existing proposals follow the build-new-chain approach for redactions, and cannot be integrated with existing systems like Bitcoin and Ethereum. We present Reparo, a generic protocol that acts as a publicly verifiable layer on top of any blockchain to perform repairs, ranging from fixing buggy contracts to removing illicit contents from the chain. Reparo facilitates additional functionalities for blockchains while maintaining the same provable security guarantee; thus, Reparo can be integrated with existing blockchains and start performing repairs on the pre-existent data. Any system user may propose a repair and a deliberation process ensues resulting in a decision that complies with the repair policy of the chain and is publicly verifiable. Our Reparo layer can be easily tailored to different consensus requirements, does not require heavy cryptographic machinery and can, therefore, be efficiently instantiated in any permission-ed or -less setting. We demonstrate it by giving efficient instantiations of Reparo on top of Ethereum (with PoS and PoW), Bitcoin, and Cardano. Moreover, we evaluate Reparo with Ethereum mainnet and show that the cost of fixing several prominent smart contract bugs is almost negligible. For instance, the cost of repairing the prominent Parity Multisig wallet bug with Reparo is as low as 0.000000018% of the Ethers that can be retrieved after the fix.

Alekh Karkada Ashok, Chandan G, Adithya Bhat et al.

Solar energy has the potential to become the backbone energy source for the world. Utility scale solar power plants (more than 50 MW) could have more than 100K individual solar modules and be spread over more than 200 acres of land. Traditionally methods of monitoring each module become too costly in the utility scale. We demonstrate an alternative using the recent advances in deep learning to automatically analyze drone footage. We show that this can be a quick and reliable alternative. We show that it can save huge amounts of power and the impact the developing world hugely.