o. biran,s. moran,s. zaks

A combinatorial characterization of the distributed tasks which are solvable in the

presence of one faulty processor

O. Biran, S. Moran,S. Zaks

Introduction

FLP showed that achieving a distributed consensus is impossible in the presence of one faulty processor.

Introduction

We will present a condition that is necessary and sufficient in order for a task to be solvable in the presence of a faulty processor.

Also we present a universal protocol which solves any task which is found to be solvable by our condition.

Definitions

The network is asynchronous and distributed.

There are N > 2 processors, V = {P1, P2, …, PN}.

We assume (w.l.o.g) that . iPidentityVP ii

The messages arrive with no error and in finite but unbounded and unpredictable time.

Definitions

One processor might be faulty (be defined later)

We assume that the network is complete (clique), but the results can be easily generalized to every biconnected network in which a failure of one processor cannot disconnect the network.

Adjacency Graphs

Let AN denote the set all vectors ā = (a1, a2, …, aN)

where A is an arbitrary set and

(a Cartesian multiplication of order N)

Aa i i

Let S be a set, when NAS

are adjacent if they differ in exactly one component.

Ss,s 21

Adjacency Graphs

The adjacency graph of S, G(S) = (S, ES) is an undirected graph adjacent are s,sEs,s 21S21

is called a partial vector (which means that the i component is undefined)

For a set of vectors, S, SssS ii

)s,,s,,s , ,(ss N1i1-i1i

i - Cliques

For each clique C in G(S), corresponds an integer 1 ≤ i ≤ N such that all the vectors in C differ from one another in exactly the i-th component.

Let C and i be a clique and its corresponding integer, than C is called an i-Clique. Notice that C defines a partial vector is

Maximal i - Cliques

A maximal i-clique is an i-clique which is not contained in any other i-clique.

Notice that every defines a maximal i-clique that includes and all the vectors that differs from in exactly the i-th component

is

s

s

Decision Tasks

Let A and B be arbitrary sets. Let f : A 2B be a function that assigns to each element a subset f(a) of B.

Cc

)(][A C

cfCf

Aa

Similarly,

Decision Tasks

Let X and D be sets of input_values and decision_values respectively.

ND

TXT 2:

A distributed decision task T is a function

where NT XX Is called the input set of task T

Decision Tasks

Similarly,

Each vector

][ TT XTD

Similarly, for decision vector

is called the decision set of T

TX ) x, x,(xx N21 is called aninput vector and it represents the initial assignment of the input value to processor Pi

Xxi

TD )d ,d ,(dd N21

Decision Tasks

A decision task T maps each input vector to a non-empty set of allowable decision vectors.

The adjacency graph G(XT) of the input set XT

is called the input graph of T.

Similarly, the adjacency graph G(DT) of the input set DT is called the decision graph of T.

Examples of Decision Tasks

ConsensusN

T XX ,11,1,,,00,0,)xT(Xx T

Strong consensusN

T XX

,11,1,,,00,0,)xT(Xx T

1,,1,10,,0,0, vTuTXvu T


Approximate consensusN

T QX

Mdmεddji

Dd,d,ddxT

x,x,xmaxM

x,x,xminm

iji

NN21

N21

N21

0given any for


Order preserving renaming (OPR) ji xxjiji N

T ΝX

jiji

i

NN21

ddxxji

Kd1i

Dd,d,dd

xT

NKK ,given any for

Protocols

Protocol for a given network is a set of N programs, each associated with a single processor.

Programs

Each program contains:

•Reading an input value

•Sending a message to a neighbor

•Receiving a message from a neighbor

•Performing a local computation

•Halting

Decision Protocols

A decision protocol is a protocol that when halts always writes an output value (to the associated element of DT

Executions

For a given network that is initialized with an input vector (x1, x2, …, xN) of XN , if each processor executes its own program, the sequence of operations performed by the processors is called an execution on the input vector.

Executions

Notice that for an input vector (x1, x2, …, xN) there can be more than one executions (due to the asynchronous nature of the system).

The set of all executions of protocol α on an input vector is denoted byx xE

Terminating Executions

A protocol in which all the processors eventually halts is called a terminating protocol.

N21 d,,d,dd

xe,D

The vector where di is the decision value of the processor pi in the execution e of protocol α is called the output vector of the execution e and is denoted by


is the set of all output vectors of all the terminating executions of the protocol α the input vector

xD

x

xTEeαα

α

xe,DxD

xTEα

is the set of all terminating executions of α on the input vector.


For a set S, we will define Dα[S] to be the union

Sx

αα xDSD

Notice that :

TTα

TTα

DXDαT

DXDαT

Solvability

The protocol α solves task T if :

)()(.2

)()(.1

Xx T

xTxD

xTExE

Solvability

Notice that :

T solves αsuch that α T

T must be computable

Solvability

Notice that :


Solvability

Notice that :


Knows all the available information and calculates

the desired result

Solvability

Notice that :


Solvability

Notice that :

T solves αsuch that α T Output vector

Faulty Processors

The processor P is faulty in an execution e if after a certain time, all the messages sent by P are not received. (fail-stop failure)

A striking processor

1-Solvability

The protocol α 1-solves task T if :

• If no processor is faulty than α solves T

• If in an execution e one processor is faulty then all the other processors eventually halt.

If for a decision task T such α exists, then T is

called 1-solvable

1-Solvability

Notice that the strong consensus decision task of FLP is not 1-solvable.

On the other hand, the weak consensus is clearly 1-solvable, using the trivial solving protocol.

Conditions for 1-Solvability

We shall present 2 basic conditions for a task T to be 1-solvable by protocol α.

The Connectivity Condition

Theorem MW -

MW are S. Moran and Y. Wolfstahl

Let T be a decision task that has a connected input graph and let α be a given protocol.

If α 1-solves T, then G(Dα[XT]) is connected.

The Connectivity Condition

Theorem 1 - Let T be a decision task. Let be such as G(C) is a connected sub graph of the input graph G(XT). Let α be a given protocol.

If α 1-solves T, then G(Dα[C]) is connected.

TXC

The Connectivity Condition - Proof

Let α be a protocol that 1-solves the task T:XTDT

We define a new task T’:CDα[C], such that XT’=C and . xTxT` Cx

Clearly α 1-solves T`. By applying theorem MW to T` we have G(Dα[XT`]) is connected.

DT

Restrictions

A task T` restriction of task T if XT`=XT and

xTxT` Xx ` T

Notice that if α is a protocol which (1-)solves T` then α also (1-)solves T.

XT

DT`T`

T

Tα

Let T be a task and α a protocol which solves T.

We denote by Tα the task induced by α.

xDxT Xx

XX

T

T

T

Note that Tα is a restriction of T.

Pointwise connected

A task T is pointwise connected if is connected. xTG Xx T

Corollary 1 - If a protocol α 1-solves a task T then Tα, the task induced by α and T, is pointwise connected.

Covering Clique

Let be a maximal input i-clique in G(XT), and B an i-clique in G(DT).

We say that B is a covering clique for (with respect to the task T) if :

ixC

ixC

ΦByTxCy i

The partial decision vector defined by a covering clique for is called covering partial vector for

ixC ixC

Open for business

i-Sleeping Execution

Let α be a protocol that 1-solves a task T. An i-sleeping execution of α is an execution in which all the messages sent by Pi are delayed until all other processors halt and decide.

We’re on a strike

The Sleeping Processor Condition

Theorem 2 - Let T be a decision task and α a protocol that 1-solves T. Then, in Tα there is a covering clique for each maximal input i-clique.

Sleeping Processor Condition - Proof

Let be a maximal input i-clique. Consider the i-sleeping-execution in α in which the input to Pj is xj for each j ≠ i.

Let be the partial output vector by the non sleeping processors.

We claim that the maximal i-cliquein G(D(Tα)) is a covering clique for

ixC

id

idCD ixC

Sleeping Processor Condition - Proof

Let yi be any value such that the vector N1ii1i21 x,,x,y,x,x,x y is a possible input

vector in . We must show that ixC yTD

For this, assume that is the actual input to α and that Pi is eventually awakened. Pi must eventually decide on a value di to obtain an input vector

y

yTDin clearly is d This .d,,d,,dd αni1

Sleeping Processor Condition

iE

i

xCy

iE

i

iE

xCT

iff xCfor clique covering a exists e that therNote

.yT xCT

thatsee todifficult not isIt .xCfor vectors

partial covering all ofset thedenotes xCTLet

i i

Theorem 2 – Example 1

Consider the OPR task of N = 3 and K = 4.

3,4,2,3,4,1,2,4,1,2,3,1xT

2,4,3,1,4,3,1,4,2,1,3,2xT

2,3,4,1,3,4,1,2,4,1,2,3xT

9,12,10,11,12,10,13,12,10X

3

2

1

321T

xxx


3,4,,2,4,,2,3,xT

2,4,,1,4,,1,3,xT

2,3,,1,3,,1,2,xT

hence 10,12, v wherevC clique-3input For the

33

32

31

33

2). Theorem(by solvable-1not is task the

thatmeans which vCT that seecan We 3E


Consider the OPR task of N = 3 and K = 5.

9,12,10,11,12,10,13,12,10X 321T xxx

4,5,,3,5,,3,4,,2,5,,2,4,,2,3,xT

3,5,,2,5,,2,4,,1,5,,1,4,,1,3,xT

3,4,,2,4,,2,3,,1,4,,1,3,,1,2,xT

hence 10,12, v wherevC clique-3input For the

33

32

31

33


2). Theorem(by solvable-1 IS task that the

means which ,4,2vCT that seecan We 3E

? ? ?

2 4 ?

2 4 1/3/5


Next we shall present two necessary andsufficient conditions for 1-solvability


clique. covering asuch

is dC that so d a outputs vinput on that algorithm

dcenterlize a is thereAlso, T`.in clique covering

a is therevC clique-iinput maximaleach For 2

connected. pointwise is T` 1

: following thesatisfying T`

T, ofn restrictio a exists thereiff solvable-1 is TA task

iii

i

Theorem 3 :

Theorem 3 – Proof

conditions 2 thesatisfies

thatT ofn restrictio T` solvable-1 is T

Only if

Theorem 3 – Proof

Condition 1

Let α be a protocol that 1-solves task T (by the assumption that T is 1-solvable).

Tα is a restriction of T (as we saw before).

Tα is pointwise connected (by Corollary 1).

Theorem 3 – Proof

Condition 2

Tα contains a covering clique for each maximal input i-clique (Theorem 2).

For each partial input vector the corresponding can be computed by simulating an i-sleeping-execution of α on the input , as described in the proof of Theorem 2.

ix

ix

id

Theorem 3 – Proof

conditions 2 thesatisfies

thatT ofn restrictio T` solvable-1 is T

If

Theorem 3 – Proof

Let T be a task which has a restriction T` which satisfies both conditions mentioned.

We will present a protocol which 1-solves T` and hence 1-solves T (as we saw before)

i-Anchor

.executions-sleeping-iin output arethat

ectorsdecision v thoseare anchors-i that means This

D.xT`d if x of

an is dtor output vecan Then, .xCfor clique

covering a be dCDlet andor input vectan be xLet i

i

i-anchor

i-Anchor – Example

In OPR with N = 3, K = 5, and input vector (10,20,30), and in a 2-sleeping-execution, P1 and P3 will output the partial covering vector (2, *, 4).

(This will happen since without knowing the value of P2, P1 and P3 can decide on 2 and 4 respectively and still allow P2 to decide on 1 / 3 / 5 which cover all the possible combinations)

i-Anchor – Example

In OPR with N = 3, K = 5, and input vector (10,20,30), and in a 2-sleeping-execution, P1 and P3 will output the partial covering vector (2, *, 4).

So (2, 3, 4) is a 2-anchor.

Theorem 3 – Proof

.S contains that xT`G

in TR treefinite a is therex of anchors-i of Sset finite

given afor hence connected, is xT`G 1condition By

.xCfor ,d vector covering partial a

outputs and xor input vect partial ainput an as getsthat

COMP.CLIQ algorithman is there2condition By

x

xx

ii

i

Theorem 3 – Proof

xx

xx

TRin vector arbitrary an is which rroot a and

above as TR treea outputs x of anchors-i of S

set finite a and xinput an on that COMP.TREE

algorithman exists therecomputable is T Since

The protocol assumes that each processor Pk contains a copy of the algorithms COMP.CLIQ and COMP.TREE described above.

Theorem 3 – the Protocol

Pk

xk

xk

xk

xk

xk

xk

STAGE ASending xk to all

xk


Pk

STAGE A Receiving first (N-1) initial values (including xk)


STAGE B Calculating partial vector and sending to all

Pk

ixixix

ixixix

ix


Pk

STAGE B Receiving first (N-1) partial vectors

Notice that all the partial vectorsAre not necessary identical


Pk

STAGE B Are all the partial vectors identical ?

?



Yes !Pk decides on its output value

according to the common partial vector

ii xCOMP.CLIQ d



No !

But notice that now Pk knowsthe entire input vector


x

x

TRIn

k

TRIn

s

j

xCOMP.CLIQCin x of anchors-i

N321x

AFATHERd

else

AFATHERd

identical were2)-(N messages x 1)-(N thefrom If

A,,A,A,A,xCOMP.TREETR


1

1 ,d SUGGEST,BROADCAST

ll

FALSEdecided


STAGE C of the protocol

in which all the processors will try to agree on a

common set of two adjacent vectors


messages `dSUGGEST are messages 1)-(N all

message `dDECIDE a is messages theof one IF

1 phase of messages 1)-(N RECEIVE

1

begin

do while

l-

ll

decidedNOT


dFATHERd

`dSUGGEST are messages thefrom 2)-(N if

else

`dBROADCAST

`dDECIDE

TRUEdecided


end

`dSUGGESTBROADCAST

end

dFATHERd

else


Lx denotes the maximal distance in the tree TRx from an i-anchor to the root rx.

Claim 1 : In each execution of the protocol there is an l ≤ Lx such that at least one processor DECIDEs in phase l.

Let l0 be the minimal l that satisfies Claim 1.


Let Pk be a processor that DECIDEs in phase l0.

Let d be the vertex in TRx on which Pk DECIDEs.

Claim 2 : If some processor Pj DECIDEs in phase l0 on a vertex d`, then d` = d.


Claim 3 : Exactly one of the following occurs :

a) At least two processors send at phase l0 a DECIDE(d) message.

b) All the (non-faulty) processors except Pk send at phase l0 a message SUGGEST(FATHER(d)).


Claim 4 : For j = 1 … N non-faulty processor Pj DECIDEs at phase l0 or at phase l0+1 on d or on FATHER(d).

(This comes from Claim 3 and STAGE C of the protocol).


If all the processors DECIDE on two adjacent vertices then the vector they output is one of these vertices.

This completes the proof of Theorem 3

Lower Bounds

After we have characterized the tasks that are 1-solvable we shall present lower bounds over the messages complexity It requires in order to solves such tasks.

Lower Bounds - FIFO

We assume that the system satisfies the FIFO discipline on each communication link.

Cleary, if a protocol 1-solves a task T, it must solve it also under this restrictive assumption.

Lower Bounds - FIFO

Also, if a task T is 1-solvable by a protocol that assumes the FIFO discipline, it is also 1-solvable by protocols that don’t assume it.

This is true this each processor can number the message it send.

Lower Bounds - FIFO

In conclusion, every lower bounds that assume this discipline are also applicable in cases in which this discipline is not assumed.

Lower Bounds – Lemma 1

Lemma 1 :Let α be a protocol that 1-solves a task T.Let x be in XT.Then if at most M messages are sent in any (FIFO) execution of α on x, then |Dα(x)| < (N+1)2M

Lower Bounds – Lemma 2

Lemma 2 :There exist tasks T such that for each arbitrarily large M there exists an input vector x such that the distance between any 1-anchor and any 2-anchor of x is greater than (N+1)2M

Lower Bounds – Theorem 4

Theorem 4 - For a given N ≥3, there is a 1-solvable

distributed task T for N processors that satisfies the following:

For every arbitrary constant M there is an input to T such that every protocol that 1-solves T must send, in the worst case, at least M messages on input .

x

x

Let x be an input vector whose existence is guaranteed by Lemma 2.


Theorem 4 – Proof – Let T be a task that satisfies Lemma 2.

Let M be given.

Then, by the proof of the ”Only if” of Theorem 3 we know that every protocol α that 1-solves T must satisfy that G(Dα(x)) is connected and it contains an i-anchor of x for i = 1, 2, …, N.

By Lemma 2 this implies that |Dα(x)| > (N+1)2M.

By Lemma 1 this implies that α may send more than M messages on input x.


o. biran,s. moran,s. zaks

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