New journal paper in the ACM Journal of Distributed Ledger Technologies 2022

I am happy to report that our collaborative work with the IOTA foundation, together with Lin Wang has been accepted at the ACM Journal of Distributed Ledger Technologies: Research and Practice (DLT).

In this work, we investigated how to design a flow-control protocol for open, permissionless public DLTs where there is a significant amount of heterogeneity among the nodes. We came up with a datacenter congestion inspired flow-control protocol that operates with a node’s health approximated by the in-egress network queue sizes. This work started as a master thesis of Jonas Theis, supervised by Luigi Vigneri at the IOTA foundation.

Congratulations everyone!

The paper will be available at

Previously, the initial idea was presented at the SERIAL’20 workshop,

Title: Healthor: Heterogeneity-aware Flow Control in DLTs to Increase Performance and Decentralization

Authors: Jonas Theis (VU Amsterdam/IOTA Foundation), Luigi Vigneri (IOTA Foundation), Lin Wang (VU Amsterdam), and Animesh Trivedi (VU Amsterdam)

Abstract: Permissionless reputation-based distributed ledger technologies (DLTs) have been proposed to overcome blockchains’ shortcomings in terms of performance and scalability, and to enable feeless messages to power the machine-to-machine economy. These DLTs allow machines with widely heterogeneous capabilities to actively participate in message generation and consensus. However, the open nature of such DLTs can lead to centralization of decision-making power, thus defeating the purpose of building a decentralized network. In this paper, we introduce Healthor, a novel heterogeneity-aware flow-control mechanism for permissionless reputation-based DLTs. Healthor formalizes node heterogeneity by defining a health value as a function of its incoming message queue occupancy. We show that health signals can be used effectively by neighboring nodes to dynamically flow control messages while maintaining high decentralization. We perform extensive simulations, and show a 23% increase in throughput, a 76% decrease in latency and four times increased node participation in consensus compared to state of the art. To the best of our knowledge, Healthor is the first system to systematically explore the ramifications of heterogeneity on DLTs and proposes a dynamic, heterogeneity-aware flow control. Healthor’s source code ( and simulation result data set ( are both publicly available.

Jekyll theme inspired by researcher