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Efficient and Simple Algorithms for Fault Tolerant Spanners

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Abstract

It was recently shown that a version of the greedy algorithm gives a construction of fault-tolerant spanners that is size-optimal, at least for vertex faults. However, the algorithm to construct this spanner is not polynomial-time, and the best-known polynomial time algorithm is significantly suboptimal. Designing a polynomial-time algorithm to construct (near-)optimal fault-tolerant spanners was given as an explicit open problem in the two most recent papers on fault-tolerant spanners ([Bodwin, Dinitz, Parter, Vassilevka Williams SODA '18] and [Bodwin, Patel PODC '19]). We give a surprisingly simple algorithm which runs in polynomial time and constructs fault-tolerant spanners that are extremely close to optimal (off by only a linear factor in the stretch) by modifying the greedy algorithm to run in polynomial time. To complement this result, we also give simple distributed constructions in both the LOCAL and CONGEST models.
arXiv:2002.10889v2 [cs.DS] 24 May 2020
Efficient and Simple Algorithms for Fault-Tolerant Spanners
Michael Dinitz
Johns Hopkins University
Caleb Robelle
University of Maryland, Baltimore County
Abstract
It was recently shown that a version of the greedy algorithm gives a construction of fault-
tolerant spanners that is size-optimal, at least for vertex faults. However, the algorithm to
construct this spanner is not polynomial-time, and the best-known polynomial time algorithm
is significantly suboptimal. Designing a polynomial-time algorithm to construct (near-)optimal
fault-tolerant spanners was given as an explicit open problem in the two most recent papers on
fault-tolerant spanners ([Bodwin, Dinitz, Parter, Vassilevka Williams SODA ’18] and [Bodwin,
Patel PODC ’19]). We give a surprisingly simple algorithm which runs in polynomial time and
constructs fault-tolerant spanners that are extremely close to optimal (off by only a linear factor
in the stretch) by modifying the greedy algorithm to run in polynomial time. To complement
this result, we also give simple distributed constructions in both the LOCAL and CONGEST
models.
1 Introduction
Let G= (V, E ) be a graph, possibly with edge lengths w:ER0. A t-spanner of G, for t1,
is a subgraph G= (V, E) that preserves all pairwise distances within factor t, i.e.,
dG(u, v)t·dG(u, v) (1)
for all u, v V(where dHdenotes the shortest-path distance in a graph H). The distance preser-
vation factor tis called the stretch of the spanner. Less formally, graph spanners are a form of
sparsifiers that approximately preserve distances (as opposed to other notions of graph sparsification
which approximately preserve cuts [BK15], the spectrum [SS11,BSS14], or other graph properties).
When considering spanners through the lens of sparsification, perhaps the most important goal in
the study of graph spanners is understanding the tradeoff between the stretch and the sparsity.
The main result in this area, which is tight assuming the “Erd˝os girth conjecture” [Erd64], was
given by Alth¨ofer et al.:
Theorem 1 ( [ADD+93]).For every positive integer k, every weighted graph G= (V, E)has a
(2k1)-spanner with at most O(n1+1/k)edges.
This notion of graph spanners was first introduced by Peleg and Sch¨affer [PS89] and Peleg
and Ullman [PU89] in the context of distributed computing, and has been studied extensively for
the last three decades in the distributed computing community as well as more broadly. Span-
ners are not only inherently interesting mathematical objects, but they also have an enormous
Supported in part by NSF award CCF-1909111
1
number of applications. A small sampling includes uses in distance oracles [TZ05], property test-
ing [BGJ+09, BBG+14], synchronizers [PU89], compact routing [TZ01], preprocessing for approxi-
mation algorithms [BKM09, DKN17]), and many others.
Many of these applications, particularly in distributed computing, arise from modeling computer
networks or distributed systems as graphs. But one aspect of distributed systems that is not
captured by the above spanner definition is the possibility of failures. We would like our spanner
to be robust to failures, so that even if some nodes fail we still have a spanner of what remains.
More formally, Gis an f-(vertex-)fault-tolerant t-spanner of Gif for every set FVwith |F| ≤ f
the spanner condition holds for G\F, i.e.,
dG\F(u, v)t·dG\F(u, v)
for all u, v V\F. If Fis instead an edge set then this gives a definition of an f-edge-fault-tolerant
t-spanner.
This notion of fault-tolerant spanners was first introduced by Levcopoulos, Narasimhan, and
Smid [LNS98] in the context of geometric spanners (the special case when the vertices are in
Euclidean space and the distance between two points is the Euclidean distance), and has since
been studied extensively in that setting [LNS98, Luk99, CZ04, NS07]. Note that in the geometric
setting dG\F(u, v) = dG(u, v) for all u, v V\F, since faults do not change the underlying geometric
distances.
In general graphs, though, dG\F(u, v) may be extremely different from dG(u, v), making this
definition more difficult to work with. The first results on fault-tolerant graph spanners were by
Chechik, Langberg, Peleg, and Roditty [CLPR10], who showed how to modify the Thorup-Zwick
spanner [TZ05] to be f-fault-tolerant with an additional cost of approximately kf: the number
of edges in the f-fault-tolerant (2k1)-spanner that they create is approximately ˜
O(kfn1+1/k)
(where ˜
Ohides polylogarithmic factors). Since [CLPR10] there has been a significant amount of
work on improving the sparsity, particularly as a function of the number of faults f(since we
would like to protect against large numbers of faults but usually care most about small stretch
values). First, Dinitz and Krauthgamer [DK11] improved the size to ˜
O(f21/kn1+1/k ) by giving a
black-box reduction to the traditional non-fault-tolerant setting. Then Bodwin, Dinitz, Parter, and
Vassilevska Williams [BDPW18] decreased this to O(exp(k)f11/k n1+1/k), which they also showed
was optimal (for vertex faults) as a function of fand n(i.e., the only non-optimal dependence
was the exp(k)). Unlike previous fault-tolerant spanner constructions, this optimal construction
was based off of a natural greedy algorithm (the natural generalization of the greedy algorithm
of [ADD+93]). An improved analysis of the same greedy algorithm was then given by Bodwin and
Patel [BP19], who managed to show the fully optimal bound of O(f11/kn1+1/k ).
Unlike the previous fault-tolerant spanner construction of [DK11] and the greedy non-fault-
tolerant algorithm of [ADD+93], the greedy algorithm of [BDPW18,BP19] has a significant weak-
ness: it takes exponential time. Obtaining the same (or similar) size bound in polynomial time was
explicitly mentioned as an important open question in both [BDPW18] and [BP19].
1.1 Our Results and Techniques
In this paper we design a surprisingly simple algorithm to construct nearly-optimal fault-tolerant
spanners in polynomial time, in both unweighted and weighted graphs.
2
Theorem 2. There is a polynomial time algorithm which, given integers k1and f1and a
(weighted) graph G= (V, E)with |V|=nand |E|=m, constructs an f-fault-tolerant (2k1)-
spanner with at most Okf11/kn1+1/k edges in time O(mkf 21/k n1+1/k).
Note that while we are a factor of kaway from complete optimality (for vertex faults), this is
truly optimal when the stretch is constant and, for non-constant stretch values, is still significantly
sparser than the analysis of the exponential time algorithm by [BDPW18] (which lost an exponential
factor in k).
The main idea in our algorithm is to replace the exponential-time subroutine used in the
greedy algorithm of [BDPW18, BP19] with an appropriate polynomial-time approximation algo-
rithm. More specifically, the main step of the exponential time greedy algorithm is to consider
whether a given candidate edge is “already spanned” by the subgraph Hthat has already been
built. This means determining whether, for some candidate edge {u, v}, there is a fault set Fwith
|F| ≤ fsuch that dH\F(u, v)>(2k1) ·dG\F(u, v). If such a fault set exists then the algorithm
adds {u, v}to H, and otherwise does not1. In both [BDPW18] and [BP19], the only method given
to find such a set Fwas to try all possible sets, giving running time that is exponential in fand
thus exponential in the size of the input.
Our main approach is to speed this up by designing a polynomial-time algorithm to replace this
exponential-time step. Unfortunately, the corresponding problem (known as Length-Bounded
Cut) is NP-hard [BEH+06], so we cannot hope to actually solve it efficiently. Instead, we design an
approximation algorithm for Length-Bounded Cut and use it instead. We end up with a fairly
weak approximation (basically a k-approximation), and one which only holds in the unweighted
case. But this turns out to be enough for the unweighted case: it intuitively allows us to build (in
polynomial time) an f-fault-tolerant spanner with the size of a kf-fault-tolerant spanner, which
changes the size from O(f11/kn1+1/k ) to O((kf )11/kn1+1/k ) = O(kf11/kn1+1/k). However, this
is only intuition. The graph we end up creating is not necessarily even a subgraph of the kf -fault-
tolerant spanner that the true greedy algorithm would have built, so we cannot simply argue that
our algorithm returns something with at most as many edges as the greedy kf-fault-tolerant greedy
spanner. Instead, we need to analyze the size of our spanner from scratch. Fortunately, we can do
this by simply following the proof strategy of [BP19] with only some minor modifications.
A natural approach to the weighted case would be to try to generalize this by creating an O(k)-
approximation for Length-Bounded Cut in the weighted setting. Such an algorithm would
certainly suffice, but unfortunately we do not know how to design any nontrivial approximation
algorithm for Length-Bounded Cut in the presence of weights. While this might appear to
rule out using a similar technique, we show that special properties of the greedy algorithm allow
us to essentially reduce to the unweighted setting. We use the weights to determine the order
in which we consider edges, but for the rest of the algorithm we simply “pretend” to be in the
unweighted setting. Since the size bound for the unweighted case worked for any ordering, that
same size bound will apply to our spanner. And then we can use the fact that we considered edges
in order of nondecreasing weights to argue that the subgraph we create is in fact an f-fault-tolerant
(2k1)-spanner even though we ignored the weights.
1Note that in the fault-free case this just means checking whether there is already a path of stretch at most (2k1)
between the endpoints, which is precisely the original greedy algorithm of [ADD+93].
3
Distributed Settings
While the focus of this paper is on a centralized polynomial-time algorithm since the existence of
such an algorithm was an explicit open question from [BDPW18] and [BP19], we complement this
result with some simple algorithms in the standard LOCAL and CONGEST models of distributed
computation.
In the LOCAL model, we can use standard network decompositions to find a clustering of the
graph where the clusters have low diameter, every edge is in at least one cluster, and the clustering
comes from O(log n) partitions. Since in the LOCAL model we are allowed unbounded message
sizes, this means that in O(log n) time we can send the subgraph induced by each cluster to the
cluster center (an arbitrary node in the cluster), who can then locally run the greedy algorithm
on that cluster and then inform the nodes in the cluster about the edges that have been chosen.
This will take only O(log n) communication rounds (since clusters have diameter O(log n)) and will
incur only an extra O(log n) factor in the number of edges (since the clustering can be divided into
O(log n) partitions).
In the CONGEST model we cannot apply this approach (even though we could find a similar
clustering) because we are not able to gather large induced subgraphs at the cluster centers (due to
the bound on message sizes). Instead, we show that the older fault-tolerant spanner construction
of [DK11] can be combined with the standard (non-fault-tolerant) spanner algorithm in the CON-
GEST model due to Baswana and Sen [BS07] to give a fault-tolerant spanner algorithm in CON-
GEST. This approach means that the size increases to O(kf21/kn1+1/k log n) (so we are a factor of
flog naway from the bounds of the polynomial-time greedy algorithm), but the number of rounds
needed is quite small despite the limitation on message sizes (O(f2(log f+ log log n) + k2flog n)
rounds).
2 Notation and Preliminaries
We will be discussing graphs G= (V , E) where n=|V|and m=|E|. Sometimes these graphs will
also have a weight function w:ER0. We will slightly abuse notation to let w(u, v) = w({u, v})
for all {u, v} ∈ E. For a (possibly weighted) graph G, we will let dG(u, v) denote the length of the
shortest (lowest-weight) path from uto v(if no such path exists then this length is ). For any
CV, we let G[C] denote the subgraph of Ginduced by C. For FVlet G\Fbe G[V\F],
and for FElet G\Fbe (V, E \F).
Definition 1. Let G= (V, E ) be a (possibly weighted) graph. A subgraph Hof Gis an f-
vertex-fault-tolerant (f-VFT) t-spanner of Gif dH\F(u, v)t·dG\F(u, v) for all FVwith
|F| ≤ fand u, v 6∈ F. A subgraph Hof Gis an f-edge-fault-tolerant (f-EFT) t-spanner of Gif
dH\F(u, v)t·dG\F(u, v) for all FEwith |F| ≤ f.
Throughout this paper, for simplicity we will only discuss the vertex fault-tolerant case since
that is the more difficult one to prove upper bounds for. The proofs for the edge fault-tolerant case
are essentially identical.
We first show an equivalent definition that will let us restrict which pairs of vertices we care
about.
Lemma 3. Let G= (V, E)be a graph with weight function wand let Hbe a subgraph of G. Then
His an f-VFT t-spanner of Gif and only if dH\F(u, v)t·w(u, v)for all FVwith |F| ≤ f
and u, v V\Fsuch that {u, v} ∈ Eand dG\F(u, v) = w(u, v)
4
Proof. The only if direction is immediately implied by Definition 1, since for any FVwith
|F| ≤ fand u, v V\Fsuch that {u, v} ∈ Eand dG\F(u, v) = w(u, v), we know from Definition 1
that dH\F(u, v)t·dG\F(u, v)t·w(u, v).
For the if direction, let FVwith |F| ≤ fand u, v V\F. Let P= (u=x0, x1,...,xp=v)
be the shortest path in G\Fbetween uand v. If p= 1 then P= (u, v), and thus dH\F(u, v) =
w(u, v) = dG\F(u, v). If p > 1, then we know that dG\F(xi1, xi) = w(xi1, xi) for all i
{1,2,...,p}, and thus
dH\F(u, v)
p
X
i=1
dH\F(xi1, xi)
p
X
i=1
t·w(xi1, xi)
=t
p
X
i=1
w(xi1, xi) = t·dG\F(u, v).
Hence His an f-VFT t-spanner of G.
The original greedy algorithm for fault-tolerant spanners was introduced and analyzed by [BDPW18],
with an improved analysis by [BP19], and is given in Algorithm 1. The part of this algorithm which
takes exponential time is the “if” condition, i.e., checking whether there is a fault set which hits
all stretch-(2k1) paths. For edge fault-tolerance, the algorithm is the same except that Fis an
edge set.
Algorithm 1 Greedy f-VFT (2k1)-Spanner Algorithm
function FT-GREEDY(G= (V, E, w), k, f )
H(V, , w)
for all {u, v} ∈ Ein nondecreasing weight order do
if there exists a set Fof at most fvertices such that dH\F(u, v)>(2k1)w(u, v)then
add {u, v}to H
end if
end for
return H
3 Unweighted Graphs
In this section we design a polynomial-time algorithm for the special case of unweighted (or unit-
weighted) graphs. We begin by designing a simple approximation algorithm for the Length-
Bounded Cut problem, and then show that this algorithm can be plugged into the greedy algo-
rithm with only a small loss.
3.1 Length-Bounded Cut
In order to design a polynomial-time variant of the greedy algorithm, we want to replace the
“if” condition by something that can be computed in polynomial time. While there are many
possibilities, there are two obvious approaches: we could try to compute the maximum tsuch that
there is a fault set of size fwhich hits all t-hop paths, or we could try to compute the minimum
5
fsuch that there is a fault set of size fwhich hits all t-hop paths. It turns out that this second
approach is more fruitful.
Consider the following problem, known as the Length-Bounded Cut problem [BEH+06].
The input is an unweighted graph G= (V, E ) with |V|=nand |E|=m, vertices u, v V(known
as the terminals), and a positive integer t. A length-t-cut is a subset FV\ {u, v}such that
dG\F(u, v)> t. The goal is to find the length-t-cut of minimum cardinality.
We are essentially going to design a t-approximation for this problem. But since we do not need
the full power of this approximation, in order to speed it up we will instead consider a gap decision
version of the problem. In the LBC(t, α) problem, the input is the same as in Length-Bounded
Cut but there is an additional input parameter α. If there is a length-t-cut of size at most α, then
we must return YES. If there is no length-t-cut of size at most αt, then we must return NO. For
intermediate values we are allowed to return either YES or NO.
Recall that breadth-first search (BFS) finds shortest paths in unweighted graphs in O(m+n)
time. So we can use BFS to check whether there is a path with at most thops from uto vin
O(m+n) time. This gives the following natural algorithm (Algorithm 2), which is essentially the
standard “frequency” approximation of Set Cover (or Hitting Set).
Algorithm 2 Algorithm for LBC(t, α)
F← ∅
for i= 1 to α+ 1 do
Run BFS to find a path Pof length at most tfrom uto vin G\Fif one exists.
if no such Pexists then
return YES
else
Add all vertices of P\ {u, v}to F
end if
end for
return NO
Theorem 4. Algorithm 2 correctly decides LBC(t, α) and runs in O((m+n)α)time.
Proof. By the running time of BFS, we know that each iteration of Algorithm 2 takes O(m+n)
time, and thus the total time is O((m+n)α) as claimed.
Suppose that there is a length-t-cut Fof size at most α. Then for every path Pwhich our
algorithm considers (and adds to F), it must be the case that |PF| ≥ 1 since Fmust hit all
paths of length at most t. Since we remove each path we consider (by adding it to F), this means
that there will be no more such paths after at most αiterations and thus the algorithm will return
YES as required.
Now suppose that every length-t-cut has size larger than αt. Since we add at most tvertices
to Fin each iteration, at the beginning of iteration α+ 1 the set Fhas size at most αt. Thus in
every iteration some path Pof length at most texists, so the algorithm will return NO.
To handle edge fault-tolerance, we need to slightly change the definition of LBC(t, α) to be
about edge sets rather than vertex sets, so in the algorithm Fis an edge set and we add the edges
of Prather than the vertices. But other than that trivial change, the algorithm and analysis are
identical.
6
3.2 Modified Greedy
Let G= (V, E ) be an undirected unweighted graph. We will modify Algorithm 1 by using our new
algorithm for LBC, Algorithm 2. For an EFT spanner algorithm, we simply use the edge-based
version of Algorithm 2.
Algorithm 3 Modified Greedy VFT Spanner Algorithm
function FT-GREEDY(G= (V, E), k, f )
H(V, , w)
for all {u, v} ∈ Ein arbitrary order do
if Algorithm 2 returns YES when run on input graph Hwith terminals u, v and t= 2k1
and α=fthen
Add {u, v}to H
end if
end for
return H
We first prove that this algorithm does indeed return a valid solution, despite the use of an
approximation algorithm to determine whether or not to add an edge (we prove this only for VFT
for simplicity, but the proof for EFT is analogous).
Theorem 5. Algorithm 3 returns an f-VFT (2k1)-spanner.
Proof. Let FVbe an arbitrary fault set with |F| ≤ fand {u, v} Ewith u, v 6∈ F. By
Lemma 3, we just need to show that dH\F(u, v)2k1 (since Gis unweighted) in order to prove
the theorem. Clearly this is true if {u, v} ∈ E(H). If {u, v} 6∈ E(H), then when the algorithm
considered {u, v}it must have been the case that Algorithm 2 returned NO. Theorem 4 then
implies that every length-(2k1)-cut on H(for u, v) has size larger than f. Thus Fis not a
length-(2k1)-cut in Hfor u, v, and so dH\F(u, v)2k1.
Now we want to bound the size of the returned spanner. To do this, a natural approach would
be to argue that the spanner it returns is a subgraph of the greedy ((2k1)f)-VFT spanner, since it
seems like whenever our modified algorithm requires us to add an edge it has found a cut certifying
that the greedy ((2k1)f)-VFT spanner would also have had to add that edge. Unfortunately, this
is not true since the modified algorithm might not add some edges that the true greedy algorithm
would have added, and thus later on our algorithm might have to actually add some edges that the
true greedy algorithm would not have had to add.
The next natural approach would be to try to use the analysis of [BP19] as a black box.
Unfortunately we cannot do this either, since the lemmas they use are specific to the true greedy
algorithm rather than our modification. However, it is straightforward to modify their analysis so
that it continues to hold for our modified algorithm, with only an additional loss of a factor of k.
We do this here for completeness. As in [BP19], we start with the definition of a blocking set, and
then give two lemmas using this definition. And also as in [BDPW18, BP19], we only prove this
for VFT, as the proof for EFT is essentially identical.
Definition 2 ( [BP19]).For any graph G= (V, E), we define BV×Eto be a t-blocking set of
Gif for all (v, e)B, we have v6∈ eand for any cycle Cin Gwith |C| ≤ t, there exists (v, e)B
such that v, e C.
7
Lemma 6. Any graph Hreturned by Algorithm 3 with parameters k, f has a (2k)-blocking set of
size at most (2k1)f|E(H)|.
It was shown in [BP19] that the graph Hreturned by the standard VFT greedy algorithm
with parameters k, f has a (2k)-blocking set of size at most f|E(H)|.2So our modified algorithm
satisfies the same lemma up to a factor of O(k). The proof is almost identical in our case; we
essentially replace all instances of fin their proof with (2k1)f.
Proof of Lemma 6. Let e={u, v}be some edge in E(H), and let Hbe the subgraph maintained
by the algorithm just before eis added to E(H) (so His a subset of the final H). Since ewas
added by Algorithm 3, when it was considered Algorithm 2 must have returned YES. Thus by
Theorem 4 there is some set FeV\ {u, v}with |Fe| ≤ f(2k1) such that dH\Fe(u, v)>2k1.
Now we can define the blocking set: let B={(x, e) : eE(H), x Fe}.
Since |Fe| ≤ f(2k1) for all eE(H), we immediately get that |B| ≤ |E(H)|f(2k1) as
claimed. So we now need to show that Bis a (2k)-blocking set. To see this, let Cbe any cycle
with at most 2kvertices in H, and let e={u, v}be the last edge of this cycle to be added to H.
Let Hbe the subgraph of Hbuilt by the algorithm just before eis added. Then C\eis a uv
path in Hof length at most 2k1, and thus there is some xC\ {u, v}that is in Fe. Thus
(x, e)B.
Now we know that the spanner returned by Algorithm 3 has a small blocking set. The next
lemma implies that any such graph must have a dense but high-girth subgraph.
Lemma 7. Let Hbe any graph on nnodes and medges (with f=o(n)) that has a (2k)-blocking
set Bof size at most (2k1)fm. Then Hhas a subgraph on O(n/(kf )) nodes and Ω(m/(kf )2)
edges that has girth greater than 2k.
Proof. Let Hdenote the induced subgraph of Hon a uniformly random subset of exactly n/(2(2k
1)f)nodes. Let B:= B(V(H)×E(H)), and let H′′ denote the graph obtained by removing
from Hevery edge contained in any pair in B. The graph H′′ will be the one we analyze.
The easiest property to analyze is the number of nodes in H′′ : there are precisely n/(2(2k
1)f)vertices in H′′, which is O(n/(kf )) as claimed.
The next easiest property of H′′ to prove is the girth. Let Cbe a cycle in Hwith at most 2k
nodes. Cis either in Hor it is not. If it is not in Hthen some vertex in Cis not in V(H), and
thus Cis not in H′′. On the other hand, if Cis in Hthen by the definition of Bthere is some
edge (x, e)Bso that eC, and also (x, e)B, and thus Cdoes not exist in H′′.
To analyze |E(H′′)|, we start with the following observations.
Each {u, v} ∈ E(H) remains in E(H) if u, v V(H). This happens with probability
n/(2(2k1)f)
n·n/(2(2k1)f)⌋ − 1
n1
(1 o(1)) 1
4((2k1)f)2
2In [BP19] the parameter “k” is used to denote the stretch, while for us the stretch is 2k1, and thus there
are slight constant factor differences between the statements as written in [BP19] and our interpretation of their
statements. But our statements about [BP19] are correct under this change of variables.
8
Each (x, {u, v})Bremains in Bif u, v, x V(H). This happens with probability
n/(2(2k1)f)
n·n/(2(2k1)f)⌋ − 1
n1·n/(2(2k1)f)⌋ − 2
n2
1
8((2k1)f)3
Now we can use these observations to compute the expected size of E(H′′):
E[|E(H′′)|]E[|E(H)| − |B|] = E[|E(H)|]E[|B|]
(1 o(1)) |E(H)|
4((2k1)f)2|B|
8((2k1)f)3
(1 o(1)) m
4((2k1)f)2(2k1)fm
8((2k1)f)3
(1 o(1)) m
4((2k1)f)2m
8((2k1)f)2
= (1 o(1)) m
8((2k1)f)2
= m
(kf )2
Note that the bounds on |V(H′′)|and on the girth of H′′ are deterministic. So there is some
subgraph which has those bounds and where the number of edges is at least the expectation, proving
the lemma.
This lemma allows us to prove the size bound.
Theorem 8. The subgraph Hreturned by Algorithm 3 has at most Okf 11/kn1+1/k edges.
Proof. If f= Ω(n) then the theorem is trivially true. Otherwise, by Lemmas 6 and 7 we know
that Hhas a subgraph Sof girth larger than 2kon O(n/(kf )) nodes and with |E(S)| ≥ |E(H)|
(kf )2
edges. But it has long been known that any graph with nvertices and girth larger than 2kmust
have at most O(n1+1/k) edges (this is the key fact used in the original non-fault-tolerant greedy
algorithm analysis [ADD+93]). Hence |E(S)| ≤ O((n/(kf ))1+1/k ). Therefore there are constants
c1, c2>0 such that for large enough n,
c1n
kf 1+1/k
≥ |E(S)| ≥ c2|E(H)|
(kf )2
=⇒ |E(H)| ≤ O(kf )11/k n1+1/k =Okf 11/kn1+1/k .
Theorem 9. The worst-case running time of Algorithm 3 is at most Omkf21/k n1+1/k.
Proof. Algorithm 3 has |E|=miterations, each of which consists of one call to Algorithm 2
with α=fon graph H. So the running time of each iteration (by Theorem 4) is at most
O((|E(H)|+n)f). Theorem 8 implies that |E(H)| ≤ O(kf 11/kn1+1/k ), and thus the total running
time is at most O(mkf 21/kn1+1/k ).
Theorems 5, 8, and 9 together imply Theorem 2 in the unweighted case.
9
4 Weighted Graphs
We now show that we can use the algorithm we designed for the unweighted setting even in the
presence of weights. Our algorithm is very simple: we order the edges in nondecreasing weight
order, but then run the unweighted algorithm on the edges in this order. We give this algorithm
more formally as Algorithm 4. Again, changing to edge fault-tolerance is straightforward: we just
use the edge version of Algorithm 2. So we prove this only for vertex fault-tolerance for simplicity.
Algorithm 4 Modified Greedy VFT Spanner Algorithm (Weighted)
function FT-GREEDY(G= (V, E, w), k, f )
H(V, , w)
for all {u, v} ∈ Ein nondecreasing weight order do
if Algorithm 2 returns YES when run on input graph H(with no weights) with terminals u, v
and t= 2k1 and α=fthen
Add {u, v}to H
end if
end for
return H
Theorem 10. Algorithm 4 returns an f-VFT (2k1)-spanner with at most O(kf 11/kn1+1/k )
edges in time at most O(mkf 21/k n1+1/k).
Proof. The running time is directly from Theorem 9, since the only additional step in the algorithm
is sorting the edges by weight, which takes only O(mlog m) additional time. The size also follows
directly from Theorem 8, since Algorithm 4 is just a particular instantiation of Algorithm 3 where
the ordering (which is unspecified in Algorithm 3) is determined by the weights. In other words,
Theorem 8 holds for an arbitrary order, so it certainly holds for the weight ordering.
The more interesting part of this theorem is correctness: why does this algorithm return an
f-VFT (2k1)-spanner despite ignoring weights? Let FVbe an arbitrary fault set with |F| ≤ f
and {u, v} ∈ Ewith u, v 6∈ Fand dG\F(u, v) = w(u, v). By Lemma 3, we just need to show that
dH\F(u, v)(2k1)w(u, v) in order to prove the theorem. Clearly this is true if {u, v} ∈ E(H).
So suppose that {u, v} 6∈ E(H). Then when the algorithm considered {u, v}it must have been
the case that Algorithm 2 returned NO, and hence by Theorem 4 every length-(2k1)-cut in H
(unweighted) for u, v has size larger than fand so Fis not such a cut. Thus at the time the
algorithm was considering {u, v}, there was some path Pbetween uand vin H\Fwith at most
2k1 edges. But since we considered edges in order of nondecreasing weight, every edge in Phas
weight at most w(u, v). Thus
dH\F(u, v)X
eP
w(e)X
eP
w(u, v) = |P|w(u, v)
(2k1)w(u, v),
as required.
10
5 Distributed Algorithms
In this section we give efficient randomized algorithms to compute fault-tolerant spanners of
weighted graphs in two standard distributed models: the LOCAL model and the CONGEST
model [Pel00]. Recall that in both models we assume communication happens in synchronous
rounds, and our goal is to minimize the number of rounds needed. In the LOCAL model each
node can send an arbitrary message on each incident edge in each round, while in the CONGEST
model these messages must have size at most O(log n) bits (or O(1) words, so we can send a con-
stant number of node IDs and weights in each message). Note that both models allow unlimited
computation at each node, and hence the difficulty with applying the greedy algorithm is not the
exponential running time, but its inherently sequential nature.
5.1 LOCAL
In the LOCAL model we will be able to implement the greedy algorithm at only a small extra
cost in the size of the spanner. Our approach is simple: we use standard network decompositions
to decompose the graph into clusters, run the greedy algorithm in each cluster, and then take the
union of the spanner for each cluster.
The following theorem is a simple corollary of the construction of “padded decompositions”
given explicitly in previous work on fault-tolerant spanners [DK11]. It also appears implicitly in
various forms in [LS93,Bar96,MPX13,MPVX15] (among others). In what follows, the hop diameter
of a cluster refers to its unweighted diameter.
Theorem 11. There is an algorithm in the LOCAL model which runs in O(log n)rounds and
constructs P1, P2,...,Psuch that:
1. Each Piis a partition of V, with each part of the partition referred to as a cluster. Let
C=
i=1Pibe the collection of all clusters of all partitions.
2. Each cluster has hop diameter at most O(log n)and contains some special node known as the
cluster center.
3. =O(log n)(there are O(log n)partitions).
4. With high probability (11/ncfor any constant c) for every edge eEthere is a cluster
C∈ C such that eC.
With this tool, it is easy to describe our algorithm. First we use Theorem 11 to construct the
partitions. Then in each cluster Cwe gather at the cluster center the entire subgraph G[C] induced
by that cluster. Each cluster center uses the greedy algorithm (Algorithm 1) on G[C] to construct
an f-VFT (2k1)-spanner HCof G[C], and then sends out the selected edges to the nodes in C.
Let Hbe the final subgraph created (the union of the edges of each HC)
Theorem 12. With high probability, His an f-VFT (2k1)-spanner of Gwith at most Of11/kn1+1/k log n
edges and the algorithm terminates in O(log n)rounds.
Proof. The round complexity is obvious from the round complexity and cluster hop diameter bounds
in Theorem 11.
11
The total number of edges added is at most
X
i=1 X
CPi
|E(HC)| ≤
X
i=1 X
CPi
f11/k|V(HC)|1+1/k
=f11/k
X
i=1 X
CPi
|C|1+1/k
f11/k
X
i=1
n1+1/k
=Of11/kn1+1/k log n,
where we used the size bound on the greedy algorithm from [BP19] and the fact from Theorem 11
that each Piis a partition of V.
To show correctness, consider some {u, v} ∈ Eand FVwith |F| ≤ fand u, v 6∈ Fso that
dG\F(u, v) = w(u, v). By Lemma 3, we just need to prove that dH\F(u, v)(2k1)w(u, v). Let
C∈ C be a cluster which contains both uand v, which we know exists (with high probability) from
Theorem 11. Let FC=FC. Then
dH\F(u, v)dHC\FC(u, v)
(2k1) ·dG[C]\FC(u, v) (definition of HC)
(2k1) ·w(u, v) ({u, v} ∈ E(G[C]\FC))
Thus His indeed an f-VFT (2k1)-spanner of G.
5.2 CONGEST
We unfortunately cannot use the approach that we used in the LOCAL model in the CONGEST
model, since we cannot efficiently gather the entire topology of a cluster at a single node. We will
instead use the fault-tolerant spanner of Dinitz and Krauthgamer [DK11], rather than the greedy
algorithm, and combine it with the non-fault-tolerant spanner of [BS07] which can be efficiently
constructed in CONGEST. This approach means that, unlike in the centralized setting or the
LOCAL model, we will not be able to get size-optimal fault-tolerant spanners.
The algorithm of [DK11] works as follows (in the traditional centralized model). Suppose that
we have some algorithm Awhich constructs a (2k1)-spanner with at most g(n) edges on any
graph with nnodes. The algorithm of [DK11] consists of O(f3log n) iterations, and in each iteration
every node chooses to participate independently with probability 1/f. For each iO(f3log n), let
Vibe the vertices who participate and let Gibe the subgraph of Ginduced by them. We let Hibe
the (2k1)-spanner constructed by Aon Gi. Then we return the union of all Hi.
The main theorem that [DK11] proved about this is the following.
Theorem 13 ( [DK11]).This algorithm returns an f-VFT (2k1)-spanner of Gwith Of3g((2n)/f) log n
edges with high probability.
Note that when g(n) = n1+1/k , this results in an f-VFT (2k1)-spanner with at most
O(f21/kn1+1/k log n), which is precisely the bound from [DK11].
12
Since the algorithm of [DK11] uses an arbitrary non-fault-tolerant spanner algorithm A, by
using a distributed spanner algorithm for Awe naturally end up with a distributed fault-tolerant
spanner algorithm. In particular, we will combine the algorithm of [DK11] with the following
algorithm due to Baswana and Sen [BS07].
Theorem 14 ( [BS07]).There is an algorithm that computes a (2k1)-spanner with at most
O(kn1+1/k )edges of any weighted graph in O(k2)rounds in the CONGEST model.
Combining Theorems 13 and 14 immediately gives an algorithm in CONGEST that returns
an f-VFT (2k1)-spanner of size at most O(kf 21/kn1+1/k ) that runs in at most O(k2f3log n)
rounds (with high probability). We can just run each iteration of the Dinitz-Krauthgamer algo-
rithm [DK11] in series, and in each iteration we use the Baswana-Sen algorithm [BS07]. Since there
are O(f3log n) iterations, and Baswana-Sen takes O(k2) rounds, this gives a total round complexity
of O(k2f3log n).
We can improve on this bound by taking advantage of the fact that each iteration of Dinitz-
Krauthgamer runs on a relatively small graph (approximately n/f nodes), so we can run some of
these iterations in parallel.
Theorem 15. There is an algorithm that computes an f-VFT (2k1)-spanner of Gwith Okf 21/kn1+1/k log n
edges of any weighted graph and which runs in O(f2(log f+ log log n) + k2flog n)rounds in the
CONGEST model (all with high probability).
Proof. In the first phase of the algorithm each vertex randomly selects which of the O(f3log n)
iterations in which to participate by choosing each iteration independently with probability 1/f.
So by a Chernoff bound, with high probability every node picks O(f2log n) iterations in which
to participate. Then each vertex sends its chosen iterations to all of its neighbors. Identifying
these iterations take O(f2log n·log(f3log n)) = O(f2log n·(log f+ log log n)) bits, and thus
O(f2(log f+ log log n)) rounds in CONGEST.
After this has completed we enter the second phase of the algorithm, and now every node knows
which iterations it is participating in and which iterations each of its neighbors is participating in.
With high probability (by a simple Chernoff bound), for every edge there are at most O(flog n)
iterations in which both endpoints participate. Thus if we try to run all O(f3log n) iterations of
Baswana-Sen (Theorem 14) in parallel, we have “congestion” of O(flog n) on each edge (at each
time step) since there could be up to that many iterations in which a message is supposed to be
sent along that edge at that time. Thus we can simply use O(flog n) time steps for each time step
of Baswana-Sen and can simulate all O(f3log n) iterations of the Dinitz-Krauthgamer algorithm
(note that each Baswana-Sen message needs to have a tag added to it with the iteration number,
but since that takes at most O(log(f3log n)) = O(log f+ log log n)O(log n) bits it fits within the
required message size). Hence the total running time of this second phase is at most O(k2flog n).
The size and correctness bounds are direct from Theorems 13 and 14, and the round complexity
is from our analysis of the two phases above.
6 Conclusion and Future Work
In this paper we designed an algorithm to compute nearly-optimal fault-tolerant spanners in polyno-
mial time, answering a question posed by [BDPW18,BP19]. We also gave an optimal construction
in the LOCAL model which runs in O(log n) rounds, and an efficient algorithm in the CONGEST
13
model that constructs fault-tolerant spanners which have the same size as in [DK11] rather than
the optimal size.
There are many interesting open questions remaining about efficient algorithms for fault-tolerant
spanners, as well as about the extremal properties of these spanners. Most obviously, the size we
achieve is a factor of kaway from the optimal size, due to our use of an O(k)-approximation
for Length-Bounded Cut. Can this be removed, either by giving a better approximation for
Length-Bounded Cut or through some other construction? While kis somewhat small since
spanners tend to be most useful for constant stretch (and never have stretch larger than O(log n)),
it would still be nice to get fully optimal size in polynomial time. Similarly, our distributed
constructions are extremely simple, and there is no reason to think that we actually need Ω(log n)
rounds in LOCAL or that we cannot get optimal size fault-tolerant spanners in CONGEST. It would
be interesting to design better distributed and parallel algorithms for these objects, particularly
since the greedy algorithm (the only size-optimal algorithm we know) tends to be difficult to
parallelize.
From a structural point of view, we reiterate one of the main open questions from [BDPW18]
and [BP19]: understanding the optimal bounds for edge-fault-tolerant spanners. The best upper
bound we have is the same O(f11/kn1+1/k ) that we have for the vertex case, while the best lower
bound is Ω(f1
2(11/k)n1+1/k) (from [BDPW18]). What is the correct bound?
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ResearchGate has not been able to resolve any citations for this publication.
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An L-length-bounded cut in a graph G with source s, and sink t is a cut that destroys all s-t-paths of length at most L. An L-length-bounded flow is a flow in which only flow paths of length at most L are used. We show that the minimum length-bounded cut problem in graphs with unit edge lengths is NP-hard to approximate within a factor of at least 1.1377 for L ≧ 5 in the case of node-cuts and for L ≧ 4 in the case of edge-cuts. We also give approximation algorithms of ratio min{L, n/L} in the node case and min{L, n2/L2,√m} in the edge case, where n denotes the number of nodes and m denotes the number of edges. We discuss the integrality gaps of the LP relaxations of length-bounded flow and cut problems, analyze the structure of optimal solutions, and present further complexity results for special cases.
Book
Aimed at an audience of researchers and graduate students in computational geometry and algorithm design, this book uses the Geometric Spanner Network Problem to showcase a number of useful algorithmic techniques, data structure strategies, and geometric analysis techniques with many applications, practical and theoretical. The authors present rigorous descriptions of the main algorithms and their analyses for different variations of the Geometric Spanner Network Problem. Though the basic ideas behind most of these algorithms are intuitive, very few are easy to describe and analyze. For most of the algorithms, nontrivial data structures need to be designed, and nontrivial techniques need to be developed in order for analysis to take place. Still, there are several basic principles and results that are used throughout the book. One of the most important is the powerful well-separated pair decomposition. This decomposition is used as a starting point for several of the spanner constructions. © Giri Narasimhan, Michiel Smid 2007 and Cambridge University Press, 2009. All rights reserved.