We started the new year with a session on epidemic / gossip protocols. To decide what to read I compiled the following list of papers that I either enjoyed reading in the past, or that were recommended to me. The Swim (Scalable failure detection and membership protocol) paper won the poll.

Das, Abhinandan, Indranil Gupta, and Ashish Motivala. “Swim: Scalable weakly-consistent infection-style process group membership protocol.” Proceedings International Conference on Dependable Systems and Networks. IEEE, 2002.


I have first gotten into touch with Swim through Hashicorp’s Memberlist, given that it is the underlying protocol implementation of Prometheus Alertmanager’s high availability architecture, which I was maintaining in the past. The implementation in itself is not based on Swim but Lifeguard, a follow up paper published by Hashicorp [2].

Trying to summarize a couple of discussions of the session, we first talked about the general use cases of group membership protocols (e.g. Multicast, gossip, databases, pub/sub or gaming) and the trade offs one has to make within such protocols (e.g. latency vs. message load, decentralization vs. complexity, false negative vs. false positive rate).

Quickly we digressed talking about possible improvements e.g. leveraging vector clocks or inverted bloom filters to implement pull based membership updates, instead of push-based updates, alternative architectures, weighting information density higher than message load overhead, e.g. by forming multiple connected graphs between nodes (see Microsoft’s Pingmesh [1]).

Given that we have talked a bunch about CRDTs in the past, there is always a mandatory How would we implement this as a CRDT discussion, ending up as Whether the TCP packet sequence number would be a valid CRDT (majority tended towards ‘yes’).

At first I was a bit surprised by the stats the paper publishes on high failures raids on 10 % packet loss. Given that Swim involves roundtrips within certain protocol steps these failure rates accumulate thus 10% packet loss within a local-area-network is quite significant and a valid excuse for SWIM not to build full membership groups. To quickly recover from such scenarios it might be worth introducing an additional anti-entropy process to do periodic full-state syncs between nodes like done in Hashicorp’s advanced SWIM - Lifeguard [2].

Alternative Papers

Combining the resilience of gossip networks with the efficiency of tree-based broadcast.

Leitao, Joao, Jose Pereira, and Luis Rodrigues. “Epidemic broadcast trees.” 2007 26th IEEE International Symposium on Reliable Distributed Systems (SRDS 2007). IEEE, 2007.


Proximity aware tree based multicast.

Tang, Chunqiang, Rong N. Chang, and Christopher Ward. “GoCast: Gossip-enhanced overlay multicast for fast and dependable group communication.” 2005 International Conference on Dependable Systems and Networks (DSN'05). IEEE, 2005.


Paper laying the foundation of epidemic (gossip) communication in 1988.

Demers, Alan, et al. “Epidemic algorithms for replicated database maintenance.” ACM SIGOPS Operating Systems Review 22.1 (1988): 8-32.


Reducing network bandwidth through efficient set reconciliation in

peer-to-peer networks.

Ozisik, A. Pinar, et al. “Graphene: efficient interactive set reconciliation applied to blockchain propagation.” Proceedings of the ACM Special Interest Group on Data Communication. ACM, 2019.


Algorithm to maintain reliable partial views in gossip networks.

Leitao, Joao, José Pereira, and Luis Rodrigues. “HyParView: A membership protocol for reliable gossip-based broadcast.” 37th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN'07). IEEE, 2007.



[1] Guo, Chuanxiong, et al. “Pingmesh: A large-scale system for data center network latency measurement and analysis.” ACM SIGCOMM Computer Communication Review. Vol. 45. No. 4. ACM, 2015.


[2] Dadgar, Armon, James Phillips, and Jon Currey. “Lifeguard: Swim-ing with situational awareness.” CoRR (2017).