SearcharxivSearch

arXiv · 1906.11721

DiPETrans: A Framework for Distributed Parallel Execution of Transactions of Blocks in Blockchain

Abstract

Contemporary blockchain such as Bitcoin and Ethereum execute transactions serially by miners and validators and determine the Proof-of-Work (PoW). Such serial execution is unable to exploit modern multi-core resources efficiently, hence limiting the system throughput and increasing the transaction acceptance latency. The objective of this work is to increase the transaction throughput by introducing parallel transaction execution using a static analysis technique. We propose a framework DiPETrans for the distributed execution of the transactions in a block. Here, peers in the blockchain network form a community to execute the transactions and find the PoW parallelly, using a leader-follower approach. During mining, the leader statically analyzes the transactions, creates different groups (shards) of independent transactions, and distributes them to followers to execute them in parallel. After the transaction executes, the community's compute power is utilized to solve the PoW concurrently. When a block is successfully created, the leader broadcasts the proposed block to other peers in the network for validation. On receiving a block, validators re-execute the block transactions and accept the block if they reach the same state as shared by the miner. Validation can also be done as a community, in parallel, following the same leader-follower approach as mining. We report experiments using over 5 Million real transactions from the Ethereum blockchain and execute them using our DiPETrans framework to empirically validate the benefits of our techniques over traditional sequential execution. We achieve a maximum speedup of 2.2x for the miner and 2.0x for the validator, with 100 to 500 transactions per block. Further, we achieve a peak of 5x end-to-end block creation speedup using a parallel miner over a serial miner when using 6 machines in the community.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Shrey Baheti, Parwat Singh Anjana, Sathya Peri, Yogesh Simmhan. 2021-03-13. DiPETrans: A Framework for Distributed Parallel Execution of Transactions of Blocks in Blockchain. https://doi.org/10.1002/cpe.6804

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Online Treasure Hunt in Vertex-Permuted Dynamic Rings

We study the problem of treasure hunt by a group of $k \geq 1$ agents in vertex-permuted dynamic rings (VP). In this model, the $n$ vertices remain on a ring but are permuted at each time step. We first show that treasure hunt is impossible for any $k \leq n-3$ agents, if there are no restrictions on the sequence of permutations used in the dynamic ring. We then study the $VP(\delta)$ setting, in which for every pair $i, j$ of vertices, the edge $(i, j)$ is guaranteed to appear within $\delta$ steps. We show that the class $VP(\delta)$ is feasible only for $\delta \geq \left\lceil \frac{n-1}{2}\right\rceil$. For the one-agent case, we show a tight bound of $\Theta(\delta n)$ on the worst-case search time as well as competitive ratio of any online algorithm for treasure hunt, provided $\delta \geq 2n$. We then give an optimal algorithm for $k$ agents, thereby showing that $k$ agents can obtain a speedup of $k$ on the worst-case search time. Finally, in the R-VP setting, in which in every step, the vertices are arranged as a ring according to a random permutation, we show that treasure hunt takes expected $\Theta(n)$ steps against an oblivious adversary and $\Theta(n \log n)$ steps against an adaptive adversary.

cs.DC

The Computing Channel: How Modulation Programs the Airwaves

Distributed computing and distributed artificial intelligence require frequent exchanges of intermediate results, although many applications need only an aggregate rather than messages from individual devices. Conventional systems recover each message before computing the aggregate, whereas over-the-air computation (OAC) exploits simultaneous transmission to obtain it directly. However, dominant OAC implementations rely on analog signaling, creating a mismatch with finite-precision data and digital communication procedures. This article presents digital function-oriented communication, in which finite-alphabet symbol representations and receiver decisions are jointly designed so that multiple-access superposition encodes the desired function without recovering individual inputs. We introduce its computational-constellation principle, main design approaches, extensions, and implementation challenges. Federated edge learning illustrates how the framework can reduce user-dependent data-bearing resources while operating directly on quantized model updates.

cs.DC

Can AI Remediate Backend Failures Safely? GuardedAct with Blast-Radius-Aware Sandboxing

Large Language Models (LLMs) have shown promising capabilities in generating remediation actions for microservice failures. However, directly executing AI-generated repair actions in production risks cascading collateral damage. We propose GuardedAct, a sandbox-first remediation framework that interposes a blast-radius-aware verification layer between the LLM action generator and the production environment. GuardedAct operates in four phases: (1) ingesting a diagnosis report together with the live system topology and recent telemetry, (2) prompting an LLM to produce a ranked list of candidate remediation actions, (3) simulating each action in a lightweight digital-twin sandbox that estimates the blast radius and assigns a risk label, and (4) enforcing a rollback-confidence gate that auto-executes only low-risk actions while escalating high-risk ones for human review. We evaluate GuardedAct on five fault scenarios injected into the DeathStarBench social-network application. Experimental results show that GuardedAct achieves an overall recovery rate of 87.4% while reducing collateral damage by 79.7% relative to direct LLM execution (from 25.6% to 5.2%), at the cost of a modest sandbox-induced increase in mean time to recovery (approximately 8 s). Ablation studies confirm that each component contributes meaningfully to the safety-speed trade-off.

cs.DC