arXiv subjects

Emanuel Onica

Publications and source records attributed to Emanuel Onica.

12 recordsLinked to original sources

arXiv2025

An Introductory Study on the Power Consumption Overhead of ERC-4337 Bundlers

Ethereum is currently the main blockchain ecosystem providing decentralised trust guarantees for applications ranging from finance to e-government. A common criticism of blockchain networks has been their energy consumption and operational costs. The switch from Proof-of-Work (PoW) protocol to Proof-of-Stake (PoS) protocol has significantly reduced this issue, though concerns remain, especially with network expansions via additional layers. The ERC-4337 standard is a recent proposal that facilitates end-user access to Ethereum-backed applications. It introduces a middleware called a bundler, operated as a third-party service, where part of its operational cost is represented by its power consumption. While bundlers have served over 500 million requests in the past two years, fewer than 15 official bundler providers exist, compared to over 100 regular Ethereum access providers. In this paper, we provide a first look at the active power consumption overhead that a bundler would add to an Ethereum access service. Using SmartWatts, a monitoring system leveraging Running Average Power Limit (RAPL) hardware interfaces, we empirically determine correlations between the bundler workload and its active power consumption.

cs.PF↗

arXiv2025

Towards Stream-Based Monitoring for EVM Networks

We believe that leveraging real-time blockchain operational data is of particular interest in the context of the current rapid expansion of rollup networks in the Ethereum ecosystem. Given the compatible but also competing ground that rollups offer for applications, stream-based monitoring can be of use both to developers and to EVM networks governance. In this paper, we discuss this perspective and propose a basic monitoring pipeline.

cs.PF↗

arXiv2025

Where Should I Deploy My Contracts? A Practical Experience Report

Blockchain networks provide a reliable trust anchor to decentralized applications (DApps) backed by smart contracts. The Ethereum ecosystem now encompasses most blockchain networks that provide compatible support for smart contracts code. Recently, many Ethereum Layer 2 (L2) rollup solutions emerged, meant to scale the base Layer 1 (L1) network, consequently decreasing transaction fees and diversifying the usage scenarios. Furthermore, the number of blockchain providers that offer access to the network infrastructure for both L1 and L2 continuously increases. A developer is faced with a multitude of deployment options and must weigh between the gains in costs and the losses in trust that are still an issue with L2. A decisive factor in this trade-off can be the use case itself, depending on its security requirements. Still, the evaluation of costs and performance cannot be ignored and should rely on a set of measurable metrics, although choosing the right metrics can be complicated. In this practical experience report, we explore the relevance of several such metrics in choosing between different providers and rollups. For this purpose, we perform evaluations for two use cases of DApps: a voting DApp with high security demands, suited for L1 deployment, and a cost-sensitive supply chain DApp, where L2 can be an option. We analyze a set of basic metrics by comparing these between two highly used access providers, Alchemy and Infura, for the L1 deployment case, and between two of the most popular rollups, Arbitrum One and OP Mainnet (Optimism), for the L2 deployment scenario.

cs.SE↗

arXiv2022

Using SGX for Meta-Transactions Support in Ethereum DApps

Decentralized applications (DApps) gained traction in the context of the blockchain technology. Ethereum is currently the public blockchain that backs the largest amount of the existing DApps. Onboarding new users to Ethereum DApps is a notoriously hard issue to solve. This is mainly caused by lack of cryptocurrency ownership, needed for transaction fees. Several meta-transaction patterns emerged for decoupling users from paying these fees. However, such solutions are mostly offered via off-chain, often paid relayer services and do not fully address the security issues present in the meta-transaction path. In this paper, we introduce a new meta-transaction architecture that makes use of the Intel Software Guard Extensions (SGX). Unlike other solutions, our approach would offer the possibility to deploy a fee-free Ethereum DApp on a web server that can directly relay meta-transactions to the Ethereum network while having essential security guarantees integrated by design.

cs.CR↗

arXiv2020

Malware in the SGX supply chain: Be careful when signing enclaves!

Malware attacks are a significant part of the new software security threats detected each year. Intel Software Guard Extensions (SGX) are a set of hardware instructions introduced by Intel in their recent lines of processors that are intended to provide a secure execution environment for user-developed applications. To our knowledge, there was no serious attempt yet to overcome the SGX protection by exploiting the weaknesses in the software supply chain infrastructure, namely at the level of the development, build or signing servers. While SGX protection does not specifically take into consideration such threats, we show in the current paper that a simple malware attack exploiting a separation between the build and signing processes can have a serious damaging impact, practically nullifying SGX integrity protection measures. We also explore two possible mitigations against the attack, one centralized leveraging SGX itself, and one distributed that relies on a smart contract deployed on a blockchain infrastructure. Our evaluation shows that both methods are feasible in practice and their added costs are acceptable for the offered protection.

cs.CR↗

arXiv2020

Fair and Efficient Gossip in Hyperledger Fabric

Permissioned blockchains are supported by identified but individually untrustworthy nodes, collectively maintaining a replicated ledger whose content is trusted. The Hyperledger Fabric permissioned blockchain system targets high-throughput transaction processing. Fabric uses a set of nodes tasked with the ordering of transactions using consensus. Additional peers endorse and validate transactions, and maintain a copy of the ledger. The ability to quickly disseminate new transaction blocks from ordering nodes to all peers is critical for both performance and consistency. Broadcast is handled by a gossip protocol, using randomized exchanges of blocks between peers. We show that the current implementation of gossip in Fabric leads to heavy tail distributions of block propagation latencies, impacting performance, consistency, and fairness. We contribute a novel design for gossip in Fabric that simultaneously optimizes propagation time, tail latency and bandwidth consumption. Using a 100-node cluster, we show that our enhanced gossip allows the dissemination of blocks to all peers more than 10 times faster than with the original implementation, while decreasing the overall network bandwidth consumption by more than 40%. With a high throughput and concurrent application, this results in 17% to 36% fewer invalidated transactions for different block sizes.

cs.DC↗

arXiv2018

Cell Grid Architecture for Maritime Route Prediction on AIS Data Streams

The 2018 Grand Challenge targets the problem of accurate predictions on data streams produced by automatic identification system (AIS) equipment, describing naval traffic. This paper reports the technical details of a custom solution, which exposes multiple tuning parameters, making its configurability one of the main strengths. Our solution employs a cell grid architecture essentially based on a sequence of hash tables, specifically built for the targeted use case. This makes it particularly effective in prediction on AIS data, obtaining a high accuracy and scalable performance results. Moreover, the architecture proposed accommodates also an optionally semi-supervised learning process besides the basic supervised mode.

cs.AI↗

arXiv2018

Predicting Destinations by Nearest Neighbor Search on Training Vessel Routes

The DEBS Grand Challenge 2018 is set in the context of maritime route prediction. Vessel routes are modeled as streams of Automatic Identification System (AIS) data points selected from real-world tracking data. The challenge requires to correctly estimate the destination ports and arrival times of vessel trips, as early as possible. Our proposed solution partitions the training vessel routes by reported destination port and uses a nearest neighbor search to find the training routes that are closer to the query AIS point. Particular improvements have been included as well, such as a way to avoid changing the predicted ports frequently within one query route and automating the parameters tuning by the use of a genetic algorithm. This leads to significant improvements on the final score.

cs.LG↗

arXiv2017

Reliable Messaging to Millions of Users with MigratoryData

Web-based notification services are used by a large range of businesses to selectively distribute live updates to customers, following the publish/subscribe (pub/sub) model. Typical deployments can involve millions of subscribers expecting ordering and delivery guarantees together with low latencies. Notification services must be vertically and horizontally scalable, and adopt replication to provide a reliable service. We report our experience building and operating MigratoryData, a highly-scalable notification service. We discuss the typical requirements of MigratoryData customers, and describe the architecture and design of the service, focusing on scalability and fault tolerance. Our evaluation demonstrates the ability of MigratoryData to handle millions of concurrent connections and support a reliable notification service despite server failures and network disconnections.

cs.DC↗

arXiv2017

Grand Challenge: Optimized Stage Processing for Anomaly Detection on Numerical Data Streams

The 2017 Grand Challenge focused on the problem of automatic detection of anomalies for manufacturing equipment. This paper reports the technical details of a solution focused on particular optimizations of the processing stages. These included customized input parsing, fine tuning of a k-means clustering algorithm and probability analysis using a lazy flavor of a Markov chain. We have observed in our custom implementation that carefully tweaking these processing stages at single node level by leveraging various data stream characteristics can yield good performance results. We start the paper with several observations concerning the input data stream, following with our solution description with details on particular optimizations, and we conclude with evaluation and a discussion of obtained results.

cs.PF↗

arXiv2017

Confidentiality-Preserving Publish/Subscribe: A Survey

Publish/subscribe (pub/sub) is an attractive communication paradigm for large-scale distributed applications running across multiple administrative domains. Pub/sub allows event-based information dissemination based on constraints on the nature of the data rather than on pre-established communication channels. It is a natural fit for deployment in untrusted environments such as public clouds linking applications across multiple sites. However, pub/sub in untrusted environments lead to major confidentiality concerns stemming from the content-centric nature of the communications. This survey classifies and analyzes different approaches to confidentiality preservation for pub/sub, from applications of trust and access control models to novel encryption techniques. It provides an overview of the current challenges posed by confidentiality concerns and points to future research directions in this promising field.

cs.CR↗

arXiv2017

A lightweight MapReduce framework for secure processing with SGX

MapReduce is a programming model used extensively for parallel data processing in distributed environments. A wide range of algorithms were implemented using MapReduce, from simple tasks like sorting and searching up to complex clustering and machine learning operations. Many of these implementations are part of services externalized to cloud infrastructures. Over the past years, however, many concerns have been raised regarding the security guarantees offered in such environments. Some solutions relying on cryptography were proposed for countering threats but these typically imply a high computational overhead. Intel, the largest manufacturer of commodity CPUs, recently introduced SGX (software guard extensions), a set of hardware instructions that support execution of code in an isolated secure environment. In this paper, we explore the use of Intel SGX for providing privacy guarantees for MapReduce operations, and based on our evaluation we conclude that it represents a viable alternative to a cryptographic mechanism. We present results based on the widely used k-means clustering algorithm, but our implementation can be generalized to other applications that can be expressed using MapReduce model.

cs.DC↗