The leader bottleneck will continue to exist even if there great sharding because the leaders simply process more messages than the replicas, so TAPIR will allow each shard to support more throughput.<p>The paper has an evaluation for multi-data-center replication in Figure 12. We assume that the clients are web servers, so they are always close to one of the replicas, but not all of them. The result we found is basically that TAPIR performs better in the multi-data center case except when the leader is in the same data center as the client. So it depends on whether you can always guarantee that the leader is in the same data center as the client.<p>The abort rate continues to essentially track the latency needed for commit. So, TAPIR reduces the abort rate compared to OCC because it reduces the commit latency. At very high contention, locking is likely to make slightly more progress, but no systems with strong consistency will be able to provide high performance. If you are interested in some other ways to optimize for the high contention case, take a look at our work on Claret: <a href="http:&#x2F;&#x2F;homes.cs.washington.edu&#x2F;~bholt&#x2F;projects&#x2F;claret.html" rel="nofollow">http:&#x2F;&#x2F;homes.cs.washington.edu&#x2F;~bholt&#x2F;projects&#x2F;claret.html</a><p>We also tested with high clock skew. The paper notes, &quot;with a clock skew of 50 ms, we saw less than 1% TAPIR retries.&quot; Since the clients can use the retry timestamps to sync their clocks, it only adds an extra round-trip, so it still leaves TAPIR with the same latency as a conventional system, even in cases of extremely high clock skew.