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Distributed quantum entanglement sharing modelfor high-performance real-time system
Chi-Yuan Chen • Yao-Hsin Chou • Han-Chieh Chao
Published online: 3 May 2011
� Springer-Verlag 2011
Abstract Two processors jointly provide a real-time
service which can be completed by exactly one processor.
Assuming each processor is allowed to announce only a
one-bit information in a distributed way to decide which
one should process the job, inevitably some of the jobs will
get lost if only classical resources are used. In this paper,
we proposed the distributed quantum entanglement sharing
(DQES) model to share quantum entanglement with pro-
cessors. Assisted with DQES model, not only the system
dependability can be enhanced, but the faulty processor can
also be identified. We also presented some possible
applications such like database consistency, job schedul-
ing, system dependability, and reliable communication
protocols.
Keywords System dependability � Fault-identified �Quantum entanglement � Distributed system �Real-time system
1 Introduction
An example of this distributed system problems in real life
can be found in baseball. Assume Alice and Bob are two
outfielders in a baseball game. When the batter hits an
outfield fly ball, they run toward the ball and try to make a
catch. At the same time, depending on their confidence,
Alice and Bob announce their intention to catch the ball by
shouting ‘‘I’ll get it’’. However, when both of them want to
catch the ball, they will collide, fall down, and drop the
ball. Or, in order not to collide with each other, sometimes
neither of them will attempt to catch the ball and the ball
will drop. Worse yet, if the ball drops, they will blame each
other for having made the same decision. How can they
cooperate and successfully make the catch? Although this
is a scenario in baseball, similar situations happen in real-
time distributed systems.
A distributed system is defined as a collection of indi-
vidual components that communicate to achieve a common
goal. Various paradigms exist for building a high-perfor-
mance and reliable distributed system (Sedaghat et al.
2011). For example, in the memory sharing model, each
processor has its own computing power but share a com-
mon memory. They negotiate and communicate by writing
to and reading from the same memory in order to accom-
plish a given task jointly. Another approach is to allow a
communication channel to exist between each processor. In
this message passing model, all processors can perform
interprocess communication over the networks to provide a
given service.
Assume two processors want to jointly provide a real-
time service in a distributed environment. An incoming job
is completed if one and only one processor processes the
job. If each processor is allowed to announce only one-bit
information to decide which one to process the job,
C.-Y. Chen � H.-C. Chao (&)
Department of Electrical Engineering,
National Dong Hwa University, No. 1, Sec. 2,
Da Hsueh Rd, Shoufeng 97401, Hualien, Taiwan, ROC
e-mail: [email protected]
C.-Y. Chen
e-mail: [email protected]
Y.-H. Chou
Department of Computer Science and Information Engineering,
National Chi Nan University, No. 1, University Rd,
Puli 54561, Nantou, Taiwan, ROC
e-mail: [email protected]
H.-C. Chao
Department of Electronic Engineering and Institute of Computer
Science & Information Engineering, National Ilan University,
I-Lan, Taiwan, ROC
123
Soft Comput (2012) 16:427–435
DOI 10.1007/s00500-011-0727-y
inevitably some of the jobs will get lost if only classical
resources are used. To improve the system dependability,
the two processors can negotiate and decide which one to
take the job. This eliminates the collisions and hence
increases the system dependability.
However, things are different in a real-time distributed
system. A real-time system is loosely defined as a class of
computer systems that interact with the external world in a
pre-defined (usually limited) time period. In a distributed
system which relies on message exchange over a band-
width-limited communication channel, this is equivalent to
a limitation on the total amount of bits that can be
exchanged. As a result, in addition to study the problem
from a dependability point of view, the scenario described
above can also be studied from the perspective of com-
munication complexity (Yao 1979; Kushilevitz and Nisan
1997; Hromkovic 1997).
A typical two-party communication complexity problem
can be described as follows: Assuming X, Y and Z are finite
sets and f : X � Y is an arbitrary function, two physically
separated parties want to jointly evaluate f(x, y) (where
x 2 X; y 2 Y) under the condition that one party knows
only x and the other party knows only y. The communi-
cation complexity of the problem is defined as the number
of classical bits that have to be exchanged between the two
parties in order to evaluate f(x, y). In our baseball example,
x and y are the intention bits and f(x, y) is equivalent to
finding the decision of which one to process the job. It has
been shown that quantum resources such as superposition
and entanglement allow a dramatic improvement on the
communication complexity (Cleve and Buhrman 1997;
Buhrman et al. 1998, 1999, 2001; Raz 1999; Ambainis
et al. 1998; Brassard 2003).
In this paper, we propose a new paradigm for designing
a real-time distributed system and discuss how to enhance
the dependability of such a system with quantum resources.
The new model is based on a phenomenon called quantum
entanglement, which has been studied extensively in
quantum physics (Chou et al. 2007). Based on this model,
we show if these two processors share quantum entangle-
ment, not only the system dependability can be enhanced;
the faulty processor can also be identified.
2 Notations and preliminaries
2.1 Quantum bits and quantum gates
In a two-level quantum system, each bit can be represented
using a basis consisting of two eigenstates, denoted by j0iand j1i, respectively. Any state can be represented as a
linear combination of these two orthonormal eigenvectors
in a two-dimensional Hilbert space as
jwi ¼ aj0i þ bj1i
¼ cosh2j0i þ ei/ sin
h2j1i
ð1Þ
where a; b 2 C and jaj2 þ jbj2 ¼ 1: Note that, the angles hand / represent the relative length and relative phase
between the two probability amplitudes, respectively
(Fig. 1).
To distinguish the above system from the classical
binary logic, a bit in a quantum system is called a quantum
bit, or qubit.
Multiple qubits can also form a quantum system jointly.
For example, the space of a two-qubit system is the tensor
product of their own spaces. Hence, the joint state of qubit
a and qubit b is spanned by the computational basis
fj00i; j01i; j10i; j11ig; i.e.,
jwiab ¼ aj00i þ bj01i þ cj10i þ dj11i: ð2Þ
If these two qubits are separable, we have
jwiab ¼ jwai � jwbi¼ ðaaj0i þ baj1iÞ � ðabj0i þ bbj1iÞ¼ aaabj00i þ aabbj01i þ baabj10i þ babbj11i
ð3Þ
and the completeness of probability still holds. In general,
the space of an n-qubit system can be modeled as a
2n-dimensional complex vector space.
A quantum system can be manipulated by unitary
transformations called quantum gates. For example,
a phase-shift gate shifts the state for a degree of / (Fig. 2).
If we denote the phase-shift gate as P, then
Pdjwi ¼ Pdðcosh2j0i þ ei/ sin
h2j1iÞ
¼ cosh2j0i þ eið/þdÞ sin
h2j1i:
ð4Þ
A special case of the general phase-shift gate is called
the Z gate. It performs a p phase-shift and can be denoted
Fig. 1 Bloch sphere representation of any single qubit
428 C.-Y. Chen et al.
123
as Z ¼ Pp: Another example of quantum gates which will
be used shortly is the Hadamard (H) gate, which changes
j0i ! 1ffiffi
2p ðj0i þ j1iÞ
j1i ! 1ffiffi
2p ðj0i � j1iÞ:
(
ð5Þ
Note that this gate makes the eigenstates into a
superposition of j0i and j1i with equal probability
amplitudes. Similar to a single-qubit gate, a two-bit gate
manipulates the state of a two-qubit system. For example, a
controlled-not (CN) gate consists of one control qubit
x and a target qubit y. The basis of target bit will be
inverted only when the corresponding part of control bit is
in the state j1i: The CN gate changes the state as jx; yi !jx; x� yi: This is equivalent to the following state
transformation:
j00i ! j00ij01i ! j01ij10i ! j11ij11i ! j10i
ð6Þ
The symbols of Z and H gates are shown in Fig. 3a, b,
respectively.
2.2 Measurement, superposition, and entanglement
The single-qubit state described in Eq. 1 exhibits a unique
phenomenon in quantum mechanics called superposition.
When you measure the particle, the system is projected to
one of its basis, (i.e., either j0i or j1i). The overall probability
for each state is given by the absolute square of its amplitude.
That is, the probability of obtaining the post-measurement
state j0i is Pð0Þ ¼ jaj2 and probability of getting j1i state is
Pð1Þ ¼ jbj2: Note that jaj2 þ jbj2 should equal to one due to
the completeness axiom of the probability. For example, if a
qubit initially in the state j0i is measured along the z-axis,
according to quantum mechanics, the outcome will be found
in the state j0i for certain. However, if a Hadamard gate is
applied, the qubit will be in a ‘‘superposition state’’ (Eq. 5).
That is, after the measurement, there will be a 50%
(ð1=ffiffiffi
2pÞ2) of the chance that the qubit is found in the state j0i
and another 50% of the chance in j1i:Another interesting phenomenon in quantum mechanics
is entanglement (Horodecki et al. 2009). Imagine that
Alice and Bob share a two-qubit system in the state
jwþi ¼ 1ffiffiffi
2p ðj00i þ j11iÞab; ð7Þ
where a and b denotes Alice’s and Bob’s qubit. According to
quantum mechanics, if Alice takes a measurement on her
qubit a, the state of the qubit will ‘‘collapse’’ to j0i with
probability 1/2. Moreover, in this case Bob immediately
knows that the state of his qubit (the other qubit b) must be
j0i: In other words, once the measurement result of one qubit
is determined, the state of the other one is perfectly correlated
and can be instantaneously determined, no matter how far
away Alice and Bob are separated. Similarly, if the result of
Alice’s measurement is j1i; the other qubit will also be j1i:This non-classical correlation among multiple quantum
systems is called quantum entanglement, because they
cannot be written as separable states. The entangled state
shown in Eq. 7 can be implemented using the quantum
circuits shown in Fig. 4. Quantum entanglement has been
found to be extremely useful in some applications such like
superdense coding and quantum teleportation (Bennett et al.
1993). In addition, it is worth noting that a p phase-shift, (i.e.,
Z gate) performed by any party results in the same state,
since
Ppb jwþi ¼ 1
ffiffiffi
2p ðj0iaj0ib þ j1iaeipj1ibÞ
¼ 1ffiffiffi
2p ðj00i � j11iÞ
ð8Þ
Ppb jwþi ¼ 1
ffiffiffi
2p ðj0iaj0ib þ eipj1iaj1ibÞ
¼ Ppa jwþi:
ð9Þ
Fig. 2 A phase shift of degree d on the Bloch sphere
(a) (c)
(d)(b)
Fig. 3 The symbols of quantum gates (a) Z (b) H (c) CN(d) Measurement
Distributed quantum entanglement sharing model 429
123
Moreover, assuming Alice has performed a p phase-shift
on her qubit, Bob can reverse the state change by locally
applying a p phase-shift to his qubit (and vice versa), since
PpbðPp
a jwþiÞ ¼ jwþi ¼ Pp
aðPpb jwþiÞ: ð10Þ
Although it seems odd that Alice’s local operation can
reach a distant party (Bob), this property will be used
shortly in our protocol.
3 Problem and solutions
The baseball example described above can be formulated
as a dependability problem in a real-time distributed sys-
tem as follows: As shown in Fig. 5, two identical proces-
sors, Pa and Pb, jointly provide a real-time service in a
distributed environment. Requests that consume a variable
length of time come every t seconds and the job is done if
one and only one processor takes the job. The two pro-
cessors can exchange messages in order to have exactly
one processor to process the job. However, the bandwidth
of the channel is 1/t bps, so each of the processors is
allowed to announce at most one bit information to decide
which one to take the job.
Depending on its load and capacity, each processor has a
bit i (i 2 f0ðnoÞ; 1ðyesÞg) indicating its intention on whe-
ther to take the job. The probability of successfully com-
pleting the job is as follows.
When there is only one processor is currently processing
the job, its probability of success is
Ps ¼p i ¼ 1
q i ¼ 0:
�
ð11Þ
The parameter p is the probability of successfully com-
pleting the job when a party is confident (i = 1) in doing
this. Usually, p � 1: On the other hand, when a party is
unwilling or unable to take the job (i = 0) the probability
of successfully completing the job would be p [ q [ 0 if
the processor is forced to take the job. However, when
either processors or none of them takes the job, the suc-
cessfully completing the job is Ps = 0. In the following
paragraphs, we present two classical solutions and discuss
the dependability for each of them.
3.1 Classical solutions
3.1.1 Classical solution #1
For a given job, each of the processors announces its sin-
gle-bit intention, ia and ib. If ia � ib ¼ 1; then the processor
who has announced ‘1’ as its intention performs the job.
Otherwise, if ia � ib ¼ 0; then neither of them proceed.
Assuming the probability of all possible ia and ib are
equally likely, the overall probability of success is
1
4� 0þ 1
4� pþ 14� pþ 1
4� 0 ¼ p
2� 50%: ð12Þ
Obviously the probability of success is not optimized,
since there will be about 1/2 of the chance that a job gets
lost due to an collision, even when both processors are
willing to take it. That is, even if p; q � 1; the
dependability is still 50% only. To enhance the
dependability, the following classical solution can be used.
3.1.2 Classical solution #2
Each of the processors announces its single-bit intention
(ia, ib) and behaves according to these intention bits if and
only if ia � ib ¼ 1: In case of ia � ib ¼ 0; they make their
decision on whether to take the job randomly. According to
the protocol, the overall probability of success is
1
4
q
4þ q
4
� �
þ p
4þ p
4þ 1
4
p
4þ p
4
� �
¼ 5p
8þ q
8� 75%: ð13Þ
Although the dependability is improved for (p ? q)/8 to
at most 75% when p; q � 1; it seems inevitably some of
the jobs will get lost if we use these classical solutions. In
the following section, we present some quantum solutions
which are optimal in terms of the system dependability.Fig. 4 A quantum circuit showing how to construct an entangled
state
P P
Fig. 5 A diagram showing the formulated model
430 C.-Y. Chen et al.
123
3.2 Quantum solutions
The quantum solutions take advantages of physical
resources (for example, superposition and entanglement)
available only at or below nanometer scale to achieve a
given task. Here, we assume the two processors share an
entangled state and only one-bit communication is allowed
for each processor. We present two solutions as follows.
3.2.1 Quantum solution #1
This solution is shown in Fig. 6. Assuming Alice and Bob
share an entangled state
j/þi ¼ 1ffiffiffi
2p ðj01i þ j10iÞab: ð14Þ
This entangled state can be prepared with the quantum
circuit in Fig. 4 by replacing the initial state with jai ¼j0i and jbi ¼ j1i: When a job is announced, they perform
the following steps:
1. Both processors calculate their remaining capacity.
Each of the processors sets its intention bit i = 1 if it is
willing to take the job; otherwise i = 0.
2. Each processor announces its intention bit i. If ia 6¼ ib;
each processor processes the incoming job according
to its intention; otherwise go to step 3).
3. Each processor takes a measurement along the z-axis
(the M boxes) and sets the result to rn (n 2 fa; bg).Each processor processes the incoming job according
to its result rn.
Note that, in this protocol, the entanglement in Eq. 14 is
used as an arbitrator. If there is a collision on the intention
bits (ia � ib ¼ 0), they use the entanglement to decide who
should take the job. Note that the entanglement is shared
between the two processors only. Once the measurement is
done, nobody knows the result except the processors
themselves. If a job is lost due to an intention collision,
there will be no way to track down and find out who is
responsible for the loss. In other words, the two parties can
repudiate their misbehavior if they are dishonest. In the
following section, we will introduce another solution such
that the two parties cannot deny their misbehavior in case
of a collision.
3.2.2 Quantum solution #2
With the same resources (i.e. quantum entanglement),
another solution is shown in Fig. 7. The assumption is that
Alice and Bob share an entangled state
jw�i ¼ 1ffiffiffi
2p ðj00i � j11iÞab; ð15Þ
which can be generated by appending a Z gate to either
qubit in Fig. 4. When a job enters the system, they perform
the following steps.
1. Both processors calculate their remaining capacity.
Each of the processors sets its intention bit i = 1 if it is
willing to take the job; otherwise i = 0.
2. If in = 1, the processor performs a Z gate on its own
qubit. Otherwise (the intention bit in = 0), it does
nothing. This is shown in Fig. 7 as the Z-rotations in
the dashed boxes.
3. Both processors take a measurement along the x-axis.
This can be done by first applying an H gate and then
taking a measurement along the z-axis (the M boxes).
Each processor sets the outcome to rn and announces
the result.
4. If ra ¼ rb; each processor processes the incoming job
according to their intention. However, in case ra 6¼ rb;
each processor processes the incoming job according
to their results rn.
Following these steps, we verify the protocol as follows:
1. ia � ib ¼ 1: In this case, either ia = 0, ib = 1 or
ia = 1, ib = 0. As a result, either Pa or Pb (but not
both) applies a p phase-shift to its qubit. The state of
the entanglement becomes
jwi ¼ Pp 1ffiffiffi
2p ðj00i � j11iÞ
¼ 1ffiffiffi
2p ðj00i � eipj11iÞ
¼ 1ffiffiffi
2p ðj00i þ j11iÞ
ð16Þ
After the Hadamard gates, the results of the
measurement should be the same, since
ðH � HÞ 1ffiffiffi
2p ðj00i þ j11iÞab ¼
1ffiffiffi
2p j0i þ j1i
ffiffiffi
2p j0i þ j1i
ffiffiffi
2p
�
þ j0i � j1iffiffiffi2p j0i � j1i
ffiffiffi
2p
�
¼ 1ffiffiffi
2p ðj00i þ j11iÞ:
ð17ÞFig. 6 A diagram showing quantum solution #1
Distributed quantum entanglement sharing model 431
123
This means either ra = rb = 0 or ra = rb = 1.
According to the protocol, each processor processes
the incoming job according to its intention. So, either
Pa or Pb performs the job.
2. ia � ib ¼ 0: In this case, either ia = ib = 0 or
ia = ib = 1. As a result, either both Pa and Pb
apply the p phase-shift to their qubits or neither of
them performs the phase-shift. As described in Eq. 10,
the state of the entanglement will be the same as the
original entanglement. After the Hadamard gates,
the results of the measurement should be different,
since
ðH � HÞjwi ¼ ðH � HÞ 1ffiffiffi
2p ðj00i � j11iÞ
¼ 1ffiffiffi
2p j0i þ j1i
ffiffiffi
2p j0i þ j1i
ffiffiffi
2p
�
� j0i � j1iffiffiffi2p j0i � j1i
ffiffiffi
2p
�
¼ 1ffiffiffi
2p ðj01i þ j10iÞ:
ð18Þ
This means either ra = 1, rb = 0 or ra = 0, rb = 1.
According to the protocol, ra = rb, both processors
process the incoming job according to their measure-
ment results. So, either Pa or Pb performs the job.
With this solution, the announcements by these two
parties clearly indicate who should take the job, so the two
parties cannot deny their misbehavior in case of a collision.
The overall dependability, following the assumption in
the previous section, is enhanced to
1
4
q
2þ q
2
� �
þ p
4þ p
4þ 1
4
p
2þ p
2
� �
¼ 3p
4þ q
4� 100%; ð19Þ
and the dependability can be 100% if p; q � 1 which is
optimal and cannot be achieved classically.
3.3 Multiparty protocols
In three-party case, Alice, Bob, and Charlie should share
two kinds of the entangled qubits, three 1ffiffi
2p ðj01i þ
j10iÞab=bc=ca and one 1ffiffi
3p ðj100i þ j010i þ j001iÞabc: The
first kind of entanglement will be held between each of
C32 ¼ 3 links from every 2 out of 3 parties. That is, between
Alice/Bob, Bob/Charlie, and Charlie/Alice, respectively.
The second kind of entanglement is shared with all mem-
bers of the party, and each party will have its own qubit,
a, b, or c. See Fig. 8.
Accordingly, for the n-party cases, we will need n - 1
different kinds of entangled qubits. And for the k-th kind of
entanglement, there must be Cnk combinations of shared
links between all k members of each subgroup.
For the cases that all members are going to catch the ball
or none of them wants to take the job, they can both share
the entangled qubits as follows:
1ffiffiffi
np ðj100. . .0i þ j010. . .0i
þ j001. . .0i þ � � � þ j000. . .1iÞð20Þ
if each of them happened to have the same intention.
Furthermore, since
Cnk ¼ Cn
n�k ð21Þ
and
X
n
k¼0
Cnk ¼ Cn
0 þ Cn1 þ Cn
2 þ � � � þ Cnn ¼ 2n ð22Þ
The total number of qubits used in this protocol are
X
n
k¼2
k � Cnk ¼ 2 � Cn
2 þ 3 � Cn3 þ 4 � Cn
4
þ � � � þ n � Cnn
¼ n � 2n=2� n
¼ n � ð2n�1 � 1Þ
ð23Þ
One way to reduce the total kinds of shared entangled
qubits is to measure their qubit in advance. For example,
since
1ffiffiffi
3p ðj100i þ j010i þ j001iÞabc
¼ffiffiffi
1
3
r
ðj1iaj00ibc
þffiffiffi
2
3
r
j0ia1ffiffiffi
2p ðj10i þ j01iÞbc
ð24Þ
there will be about 67% chances, Alice will get 0 if she
measures her qubit first. And in that case, Bob and Charlie
will then share the remaining qubits in the form of
Fig. 7 A diagram showing quantum solution #2
432 C.-Y. Chen et al.
123
1ffiffi
2p ðj10i þ j01iÞ while qubits b and c are still entangled.
Moreover, after that Alice can announce her intention as
yes (1) as long as Bob and Charlie both have the same
intentions, even if she is not willing to take the job.
4 Model and applications
Based on the nano-phenomenon in quantum physics, we
propose a new paradigm for building real-time distributed
systems, namely the distributed quantum entanglement
sharing (DQES) model. With this model, the dependability
can be improved and the communication complexity is
diminished. As depicted in Fig. 9, the DQES Model is
composed of message passing, memory sharing, and
entanglement sharing model. We also presented some
possible applications based on DQES model in this section
(Chou et al. 2006).
4.1 Database consistency and system dependability
Alice and Bob are two LAN monitors in a distributed
intrusion detection system logging the traffic on a high-
speed network (Lee et al. 1999). Alice is physically sepa-
rated from Bob but they cooperate with each other as a
distributed system to defend the threats from outside the
network. As depicted in Fig. 10, there is a central manager
in headquarters and the purpose of the manager module is
to store all useful characteristics of the traffic passing
through Alice and Bob. This record can be analyzed later
and can be used to perform further reactions such as
marking, filtering, or tracebacking when encountering an
intrusion or DDOS (Distributed Denial of Service) attack.
Since this is a high-speed network, the traffic is so heavy
such that Alice and Bob do not have enough time (or
bandwidth) to negotiate who should log the record to the
database. With our entanglement sharing model, Alice and
Bob can just measure their qubits and announce only one-
bit information to decide which one should log the record.
Moreover, our scheme ensures that either Alice or Bob will
write down one and only one record for accurate statistical
analysis. Neither duplication nor omission is possible.
4.2 OS jobs scheduling and system dependability
Consider the following situation in our daily life: there are
two jobs for Alice and Bob and both of them can choose
whichever they want to do. However, sometimes both of
them want to take the same job, which means the other job
is taken by nobody. This is not acceptable to their boss.
With no partiality, they may decide the assignment by
tossing a coin. However, it is very difficult to perform a
remote coin flip in a distributed way. Nano-phenomenon
can help the aforementioned situation.
To do this, Alice and Bob can share the entangled qubits
and adopt our quantum solution. After the measurement,
Alice and Bob must be in opposite state with a random
behavior. That is, if Alice has state j0i; Bob must has j1i in
his hand which means he should take the job, and Alice has
to take the other one. In a system with multiple jobs
entering in a real-time fashion, they can announce only
one-bit information and resolve the contention without
repudiation. In general, this mechanism can be applied to
job scheduling in multi-processor real-time systems (Hsu
and Chen 2010).
The proposed protocol can also be used in fault-tolerant
systems. In many fault-tolerant systems, primary and
Fig. 8 Entangled qubits shared in a three-party quantum protocol
Distributed Shared Memory
Physical memory Physical memory Physical memory
QuantumChannel
EntanglementSharing Model
Memory Sharing Model
Message Passing ModelClassical Channel
Fig. 9 Three models of the distributed systems: message passing,
memory sharing, and entanglement sharing model
Fig. 10 The distributed intrusion detection system with entanglement
shared model
Distributed quantum entanglement sharing model 433
123
secondary (backup) servers are used to provide load bal-
ance. In some architecture the backup server become active
only when the primary server is down. Using our quantum
solution, the spare machine can run with the primary server
in parallel, instead of just standing by. This also prevents
the backup system from falling into a state of dilapidation
without being noticed. In addition, with no repudiation, one
can easily detect the malfunction part of the system and
then fix or replace it (Fig. 11).
4.3 Communication and network protocols
Alice and Bob are two servers that provide emergency
services to the nearby clients through a wireless channel.
They cooperate with each other to enhance the depend-
ability of the system. When somebody calls for help, one of
them, but not both, must respond immediately.
From the network point of view, this is what the MAC
(Medium Access Control) protocols do in the data link
layer. One possible way is to use the channel in turns, or
they can try using the CSMA/CD or CSMA/CA to resolve
the contention. However, these are time and bandwidth
consuming. Even worse, if all of the stations involved in
the collision attempt to retransmit their packets immedi-
ately after the jamming signal, another collision will occur.
To avoid this, a technique known as random back-off is
used in a distributed system. Using this technique, each of
the stations involved in a collision chooses to wait a time
ns before retransmitting. The value of n is a random integer
chosen independently by each station and bounded by a
constant L. Using all of these techniques, the stations
connected to the network are able to share the medium
without any centralized control or synchronization. How-
ever, these back-off/retransmit mechanisms not only delay
the response but also degrade the reliability of the emer-
gency system especially for those in great distance like
satellites.
Assisted with our Model, the time of back-off can be
saved. Even better, some major problems in wireless net-
works such as fading, collision masking, and hidden sta-
tions (terminals) all can be effectively eliminated. In our
scenario, one and only one of them will answer the request.
This is also similar to the well-known anycast service in
IPv6 protocols (Partridge et al. 1993; Hsuan et al. 2008).
A host transmits a datagram to an anycast address and the
network is responsible for providing best-effort service to
deliver the datagram to at least one, and preferably only
one, of the servers that supporting anycast. As described
above, with nanotechnology, our solution is suitable for
implementing the anycast service as well.
5 Conclusions
In this paper, we have proposed a new paradigm for
building real-time distributed systems based on the nano-
phenomenon in quantum physics. The system dependabil-
ity can be improved and the communication complexity
can be reduced with DQES model. To a level which is not
possible classically, we also showed that the reliability can
be enhanced if processors share quantum entanglement.
How to reduce the total resources used in a multiparty
environment is the objective of our future work.
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