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Paper

#### Optimal Gradient Clock Synchronization in Dynamic Networks (Version 3)

##### Fulltext (public)

1005.2894.pdf

(Preprint), 669KB

##### Supplementary Material (public)

There is no public supplementary material available

##### Citation

Kuhn, F., Lenzen, C., Locher, T., & Oshman, R. (2018). Optimal Gradient Clock Synchronization in Dynamic Networks (Version 3). Retrieved from http://arxiv.org/abs/1005.2894.

Cite as: http://hdl.handle.net/21.11116/0000-0002-F1E8-8

##### Abstract

We study the problem of clock synchronization in highly dynamic networks,
where communication links can appear or disappear at any time. The nodes in the
network are equipped with hardware clocks, but the rate of the hardware clocks
can vary arbitrarily within specific bounds, and the estimates that nodes can
obtain about the clock values of other nodes are inherently inaccurate. Our
goal in this setting is to output a logical clock at each node such that the
logical clocks of any two nodes are not too far apart, and nodes that remain
close to each other in the network for a long time are better synchronized than
distant nodes. This property is called gradient clock synchronization.
Gradient clock synchronization has been widely studied in the static setting,
where the network topology does not change. We show that the asymptotically
optimal bounds obtained for the static case also apply to our highly dynamic
setting: if two nodes remain at distance $d$ from each other for sufficiently
long, it is possible to upper bound the difference between their clock values
by $O(d \log (D / d))$, where $D$ is the diameter of the network. This is known
to be optimal even for static networks. Furthermore, we show that our algorithm
has optimal stabilization time: when a path of length $d$ appears between two
nodes, the time required until the clock skew between the two nodes is reduced
to $O(d \log (D / d))$ is $O(D)$, which we prove to be optimal. Finally, the
techniques employed for the more intricate analysis of the algorithm for
dynamic graphs provide additional insights that are also of interest for the
static setting. In particular, we establish self-stabilization of the gradient
property within $O(D)$ time.