Benchmarking Bridge Aggregators

Abstract

Blockchain aggregators play an instrumental role in the evolution of blockchain technology, serving as pivotal enablers of interoperability, efficiency, and user accessibility in an increasingly decentralized digital world. However, the literature on this emerging technology is scarce and not systematized, making it harder for practitioners and researchers to understand the field. In this paper, we systematize blockchain aggregators, with a specific emphasis on bridge aggregators. We present an exhaustive analysis of a diverse array of token and message aggregators, each distinguished by its unique architecture. Our investigation delves into critical aspects of these aggregators, encompassing their functionality, security measures, pricing models, and latency characteristics. The objective of this research is to furnish readers—encompassing both users and developers—with insightful and actionable information, thereby facilitating informed navigation through the complex landscape of blockchain aggregators.

I. INTRODUCTION

Efforts to address blockchain interoperability, such as cross-chain bridges, Decentralized Exchanges (DEXes), off-chain API-based protocols, and on-chain oracle services, fall short in providing an integrated experience to users, as there is a lack of abstractions that unify disparate protocols and blockchains. The unification of fragmented resources is nothing new; the early stages of the internet had a similar problem with e-commerce. Internet aggregators consolidated these scattered resources, thereby enhancing user experience.

Recognizing the significance of such aggregators in streamlining and enhancing user and developer experiences, this paper analyzes similar aggregation structures within the blockchain ecosystem. This article focuses specifically on bridge aggregators, exploring their potential to revolutionize the blockchain landscape. Aggregators can substantially augment functionality and accessibility of blockchain technologies for both seasoned and novice users.

A. Problem

The contemporary landscape of blockchain technology is marked by significant fragmentation and a lack of comprehensive abstraction layers. Aggregators present a viable solution to the interoperability challenge by unifying fragmented resources. However, the practical and theoretical analysis of cross-chain aggregators is a cumbersome task for developers and users.

B. Contributions

The primary objective of this paper is to empirically analyze bridge aggregator archetypes. Our study dissects the various elements that make up an aggregator and evaluates them. We examine the diverse architectures and design choices. This research provides valuable insights to prospective users who intend to utilize these protocols and future researchers and developers who will design and implement cross-chain solutions.

II. OVERVIEW OF AGGREGATORS

We define an aggregator as a mechanism that allows users to interact with one or more services by abstracting the individual protocol handlers. We classify aggregators into three types:

1. Aggregators that allow users to interact with different blockchains by reading data and sending transactions.
2. Aggregators that allow smart contracts to interact with smart contracts on other blockchains.
3. Aggregators that allow for multi-protocol DeFi.

The interaction flow of an aggregator is depicted in Figure 1. The aggregator feeds the transaction parameters into the protocols. The blockchain aggregators that are involved with on-chain state change are called Bridge Aggregators.

A. System Model and Actors

We classify bridge aggregators based on their architectures, composed of their user interaction model and the open source nature of the aggregator. Bridge aggregators can function as Token or Message Aggregators.

B. Bridge Aggregator Architecture

We present a generic architecture of a bridge aggregator. It contains components from the API Based Aggregators, Open Sourced Model Aggregators, and the Cross-Chain Protocol Agents. The user interaction with the aggregator is facilitated by SDKs that translate user inputs into aggregator-specific queries.

C. Parts of an Aggregator Transaction

We separate the parts involved in an aggregator transaction into components and parameters:

1. Components: User Quote Query, Aggregator Quote, Aggregator Route.
2. Parameters: Fees, Latency, User Interface, Customizability.

III. INSTANTIATIONS OF AGGREGATORS

In this section, we classify a set of aggregators based on their archetypes and look at a few aggregators of each type. We came up with architectures such as:

• Centralized Token Bridge Aggregators
• Decentralized Liquidity Aggregators
• On-chain Pool Based Token Swap Aggregators
• Message Aggregators

IV. BENCHMARKS

In order to analyze bridge aggregators, we designed a benchmarking tool that generates routes for aggregators, implements contracts, executes routes, and executes cross-chain transactions.

A. Setup

The experiments were executed on a machine with 8 cores@1.9 GHz base clock CPU and 32 GB RAM.

B. Benchmark Process

We collected results over a two-day window, polling a new route every 20 minutes. This gave us batches of token aggregator quotes.

C. Hypotheses

Before benchmarking, we hypothesized:

1. API-based aggregators have tighter bounds on the relation between aggregator quote value and the actual value of a token.
2. Open source aggregators have higher variance in token pricing and more latency.

V. RESULTS

In this section, we present the benchmarking results generated by our benchmarking tool in our experimental campaign.

A. Fees and Gas Price

We present aggregator net fees collected over constant parameters.

VI. DISCUSSION

A. Token aggregators

Our hypotheses showed that most aggregators didn't pick the same protocol that provided maximal trade value.

B. Message Aggregators

Deterministic fee computation is strongly influenced by message length and networks involved...

C. Security

Aggregators allow for more attack vectors than standalone protocols.

D. Summary

API aggregators usually had the lowest latencies but had more downtime. The decentralized and open-source models had higher latencies, but the lowest downtime.

VII. CONCLUSION

Aggregators are here to stay in the evolving blockchain landscape. We provided future research directions, including cross-chain privacy and enterprise-grade aggregators.