Name
Abstract | ||
|---|---|---|
Estimating collective state is an important component of many distributed systems, but has inherent challenges in balancing the availability of estimates against their accuracy. In this paper, we analyze the error bounds and dynamics of a commonly used family of self-stabilizing state estimation algorithms based on spanning trees. We find that in the worst case transients can duplicate values leading to exponential overestimates or can drop values leading to near total loss of information. The same analysis, however, also suggests that these problems can be mitigated by prioritizing smoothness in the adaptation of distance estimates used to maintain the spanning tree, and this mitigating effect is supported by results in simulation. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/FAS-W.2017.112 | 2017 IEEE 2nd International Workshops on Foundations and Applications of Self* Systems (FAS*W) |
Keywords | Field | DocType |
control theory,aggregate programming,predictable composition | Mathematical optimization,Algorithm design,Exponential function,Computer science,Spanning tree,Smoothness | Conference |
ISBN | Citations | PageRank |
978-1-5090-6559-2 | 1 | 0.37 |
References | Authors | |
14 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuanqiu Mo | 1 | 2 | 3.42 |
| Jacob Beal | 2 | 214 | 13.64 |
| Soura Dasgupta | 3 | 679 | 96.96 |