=Paper=
{{Paper
|id=Vol-2254/10000181
|storemode=property
|title=Decrease
energy consumption of transport telecommunication networks due to the
usage of stage-by-stage controlling procedure
|pdfUrl=https://ceur-ws.org/Vol-2254/10000181.pdf
|volume=Vol-2254
|authors=Gennady Linets,Sergey Melnikov,Svetlana
Govorova,Maria Lapina,Viktor Medenec
}}
==Decrease
energy consumption of transport telecommunication networks due to the
usage of stage-by-stage controlling procedure==
https://ceur-ws.org/Vol-2254/10000181.pdf
Decrease energy consumption of transport
telecommunication networks due to the usage of
stage-by-stage controlling procedure
Linets G.I. Melnikov S.V. Govorova S.V. Medenec V.V.
kbytw@mail.ru territoreer@yandex.ru mitnik2@yandex.ru alximik2012@mail.ru
Lapina M.A.
norra7@yandex.ru
North-Caucasus Federal University
Stavropol, Russian Federation
Abstract
A new approach to the development of advanced automatic monitoring
system and adaptive control of transport telecommu-nication network,
allowing to reduce energy consumption of switching centers during the
analysis and identification of faults and failures in equipment opera-
tion is offered in this paper. Energy reduction is achieved due to the
use of step-by-step procedure of finding out abnormal situations, errors
optimization of identification system states, the use of multi-agent and
permission of agents dependent on hierarchy level of transport telecom-
munication network.
1 Introduction
Today the energy efficiency reduction and introduction of energy-saving technologies into various spheres of
human activity is one of the key problems for modern civilization. Conducted studies in Green-technologies
sphere allow to make conclusion, that actually a great attention is paid to the given studies [Bia10, Gup03,
Kha17]. The scholars such as A. Bianzino, C. Chaudet, D. Rossi, and J. Rougier studied Green technologies
problems for wired communication networks upon following directions: information rate adaptation, proxy and
energy interfaces, and energy compatible applications. [Bia10].
Such authors like R. Bolla, R. Bruschi, F. Davoli, and F. Cucchetti studied the possibility of energy consump-
tion monitoring in the Internet [Bol11]. Aruna Prem Bianzino, Anand Kishore Raju and Dario Rossi suggested
to improve the energy efficiency of the Internet resources by optimizing energy consumption of routers, dependent
on the real load of served data traffic [Bia11]. Yi Zhang, P. Chowdhury, M. Tornatore and B. Mukherjee carried
out a review of energy-saving technologies condition as well as problems of energy efficiency standardization of
optical networks [Zha10].
C. Lange, D.Kosankowski, R. Weidmann, and A. Gladisch made a prediction of power consumption future dis-
tribution for broadband universal operator of cellular network [Lan11].
J. Chabarek, J. Sommers, P. Barford, C. Estan, D. Tsiang and S. Wright have proposed to take into account
Copyright c by the paper’s authors. Copying permitted for private and academic purposes.
In: Marco Schaerf, Massimo Mecella, Drozdova Viktoria Igorevna, Kalmykov Igor Anatolievich (eds.): Proceedings of REMS 2018
– Russian Federation & Europe Multidisciplinary Symposium on Computer Science and ICT, Stavropol – Dombay, Russia, 15–20
October 2018, published at http://ceur-ws.org
power consumption at the stage of transport telecommunication networks design and planning of access networks
[Cha08].
Carla Panarello, Alfio Lombardo, Giovanni Schembra, Luca Chiaraviglio and Marco Mellia [Pan10] introduced
the concept “green router” (Grouter), under which they meant a router with accumulation control functions along
with physical level of source/power, and a measuring device was added to [Lom11] in the router that estimates
the minimum accessible volume of served traffic and consumed energy at that.
However, these papers do not consider the problem of energy consumption reduction by transport telecom-
munication (TN) networks equipment owing to the more effective detection of abnormal situations arising in
event of faults and failures in the equipment that it is of priority importance for telecommunications practice. A
huge information traffic is generated by abnormal situations, which is to be processed by monitoring and control
system at a stated time in order to identify the emerged problem that requires maximum energy costs in its
traditional formulation.
Firstly, among the known methods of abnormal situations identification we should point out the methods based
on estimation of attribute values distribution density. Subjects of research are considered as implementations
of multidimensional random variables, distributed in attribute space by a definite law. They are based on
Bayesian scheme of taking decision with the highest accuracy of clarification, and are reduced to the definition
of plausibility ratio in the field of multi-dimensional space when separating boundaries are constructed between
them.
The main difficulties of in the using of these methods are [Sim99]:
• need of storing all training set to calculate estimations of local densities probability distribution;
• high response to non-representativity of training set.
2 Problem statement
In general, the task aimed to identify objects could be reduced to the verification of numerous hypotheses
H1 , H2 , . . . , Hi , . . . , Hn , where Hi is a hypothesis implying the object’s belonging to Class Ai . Let’s assume that
the a priori distributions of these hypotheses probabilities are set. It is known what’s the likelihood the object
Pn
P (Hi ) can belong to class Ai (or how often the object of this class is appeared). Moreover, P (Hi ) = 1, as
i=1
the object is to be pertained to a certain class. The conditional density of distribution is pi (x) = p(xi /Hi ).
Two hypotheses H1 = N and H2 = N̄ are used in the identification system under design at corresponding
to them a priori probabilities of situation, emerging in the network as a normal p1 = p(H1 ) = p(N ) one and an
abnormal p2 = p(H2 ) = p(N̄ ) . And also p1 + p2 = 1.
It is required to find a decision rule ensuring the top accuracy in the identification system. Using the Neyman-
Pearson criterion we fix the probability of ”false alarm” Pf.a. at stable level C and claim the minimum of pass
min
error Ppass of TN operating trouble. Then
n
Y
min
Ppass = p2 [1 − β̄(xoi )] (1)
i=1
n
Q
at restriction of Pf.a. = p1 α(xoi ) = C, const, where α(x0 ) are Type I errors; and β(x0 ) are Type II errors.
i=1
In order to improve the energy efficiency of transport networks it is necessary to determine the possible
degree of decline in the control information pc , which is to be transmitted between nodes to specify the type of
abnormality controlled conditions and using a MAS.
3 Problem solution
3.1 Defining of decision rule for identification system
During operation of TNs the status of which could be described by a large number of parameters, performance
monitoring of their working efficiency is to be carried out using stage-by-stage principle of classification.
At the first stage, the TN status is checked by the summarized index and if the abnormality is detected, a
stricter control is carried out at the second and next stages on which its real status is defined.
Figure 1: Density graph for feature distribution of xi Stage
At each of the stages the control system makes Type I α(xo i) and Type II β(xo i) errors (Fig. 1 ). Type I and
II errors (”false alarm” and ”abnormal situation” errors), are defined as follows:
Z∞
α(xoi ) = d(xi /N )dxi (2)
xoi
Zxoi
β(xoi ) = d(xi /N̄ )dxi (3)
−∞
The TN is characterized with the certain states of N and N̄ , under which we will mean normal and abnormal
operation in the process of its functioning which (in the monitoring system) are respectively displayed into the
normal A and abnormal¯state.
Apriori probabilities of the normal and abnormal states in TN are detected correspondingly by a priori proba-
bilities of the p1 and p2 states. For all the stages, with abnormal situation omission, were obtained resultant errors
n
Y
Ppass = p2 β1 + p2 β̄1 β2 + . . . + p2 β̄1 β̄2 . . . βn = p2 [1 − β̄(xoi )] (4)
i=1
and ”false alarm” errors:
n
Y
Pf.a = p1 α1 α2 . . . αn = p1 α(xoi )] (5)
i=1
where:β̄i = β̄(xoi ), αi = α(xoi ), p1 = 1 − p2 – a priori probability of a normal situation occurrence; p2 – a priori
probability of its absence.
The problem of thresholding xoi for each stage is currently central. Let’s make tradeoffs of thresholds.
Following the Neyman-Pearson criterion we will set the probability of false alarm at certain given C level. Then,
for the entire network, we get
n
Y
Pf.a = p1 α(xoi ) = C (6)
i=1
Having minimized the probability of abnormal situation omission we get
n
Y
min
Ppass = minxoi p2 [1 − β̄(xoi )] (7)
i=1
As a result, the decision rule ensuring the highest level of accuracy for the identification system is the Neyman-
Pearson criterion which is detected for the TN by expressions (6) and (7).
3.2 Method of errors optimization of identification system conditions monitoring and determi-
nation of optimal thresholds classification
3.2.1 Solution of condition monitoring identification problem on the example of a two-stage
procedure
Let us define the procedure of error detection by the feature x as the first stage of two-stage procedure, while
for the second stage we will detect the error by the feature y. Generally, the identification problem solution is
determined by Type I and Type II errors. Now let’s write down the functions for distribution density of the
¯ = f2 (x). Then the
feature x at TN problem-free functioning f (x/I) = f1 (x) and at trouble functioning – f (x/I)
errors of Type I and Type II of the detector (stage 1) are :
Z∞ Zx0
αo = f1 (x)dx βo = f2 (x)dx (8)
x0 −∞
Similarly, Type I and Type II errors for the recognizer (Stage 2) are defined:
Z∞ Zy0
αp = f1 (y)dy βp = f2 (y)dy (9)
y0 −∞
Transport network (system) has the following conditions: ”1” - the system is out of order, the failure was not
detected; ”2” – the system is operational, it was found as workable; ”3” - failure was detected and recognized
(abnormal situation presence); ”4” – the system is workable, false detection and recognition (false alarm); ”5” –
system is out of order, failure was detected but not recognized (abnormal situation omission); ”6” - the system
is in order, a false detection and correct recognition. In view of formulae (8) and (9), formulae (6) and (7) take
the form:
Z∞ Z∞
Pf.a. = p1 f1 (x)dx f1 (y)dy = C = const (10)
x0 y0
Z∞ Z∞
min
Ppass = min p2 (1 − f2 (x)dx f2 (y)dy) (11)
x0 y0
Since in the expression (10) the thresholds x0 and 0 are linked with one functional dependence x0 = φ(y0 ),
having differentiated (11) by 0 and set it to zero, we get:
Z∞ Z∞
dx0
· f2 (x0 ) · f2 (y)dy + f2 (y0 ) · f2 (x)dx = 0 (12)
dy0
y0 x0
At free-hand laws of x and y features distribution, particularly, at normal law, there is no possibility to obtain
an exact solution of the classification thresholds optimization problem. However, in certain cases, at Rayleigh
distribution laws, in particular, the solution could be obtained in its final form. Use a first level heading for the
references. References follow the acknowledgements.
Let’s set densities of features x and y distribution probability in the form of Rayleigh distribution laws, Fig.
2a, Fig. 2b. The Type I and Type II errors are in the form of:
Z∞ Zx0 Z∞ Zy0
α0 = f1 (x)dx; β0 = f2 (x)dx; αp = f1 (y)dy; βp = f2 (y)dy; (13)
x0 a y0 b
x2 (x−a)2 y2 (y−b)2
f1 (x) = xe− 2 ; f2 (x) = (x − a)e− 2 ; f1 (y) = ye− 2 ; f2 (y) = (y − b)e− 2 ; (14)
Figure 2: Density graph for feature distribution of xi Stage
Equations (10) and (11) are transformed into expressions:
p1
x20 + y02 = 2ln (15)
C
dx0
· (x0 − a) + (y0 − b) = 0 (16)
dy0
Having differentiated (15) by 0 by substituting the result in (16), we obtain optimal classification thresholds:
s s
2 ln p1 /C 2 ln p1 /C
x∗0 = y0∗ = (17)
1 + ( ab )2 1 + ( ab )2
p
Dependences in Fig. 3a, built according to the formula (17) are circumferences of the 2 ln p1 /C radius.
With the increase of a priori probability of network normal status p1 , the radius is increased subject to the
logarithmic law.
The dependence curves in Fig. 3b have clearly non-linear nature and show a tendency to get reduced along
with a growth in the degree of the classes crossing on both features. Apparently, a similar trend is observed in
other signs distribution laws.
Using (17) and (11) we get the minimum probability value of abnormal situation omission:
r
pass C p1
Pmin = p2 (1 − exp[ 2(a2 + b2 ) ln − 0.5(a2 + b2 ]) (18)
p1 C
Figure 3: Dependence curves: a) optimal threshold of detector from optimal threshold of recognizer; b) optimal
threshold of detector from a priori probability of network’s normal status
Now let’s compare the two- and one-stage procedures for detecting abnormal condition in TN by means of the
respective power ratio of the solution kc = β̄2 /β̄1 at the same
p false failure probability. Then for Rayleigh’s law
and the optimal threshold for the detector we get x∗0 = 2 ln p1 /C, where b = 0 (one-stage procedure) from
equation (18) we find out:
r
C p1
β̄1 = exp[a 2 ln − a2 /2] (19)
p1 C
Similarly, we define β̄2 for the two-stage control procedure:
r
C p1
β̄2 = exp[ 2(a2 + b2 ) ln − 0.5(a2 + b2 )] (20)
p1 C
Having set the obtained result in combination with (20) in the expression for Kc , we:
√ b2
r
p1 p 2
c = exp( ln [ 2(a + b2 ) − a 2] ) (21)
C 2
The dependence curves kc = f (C) (at a = 1 and different values b) illustrating the value of control errors
reduction at two-stage procedure and preset probability of normal operation p1 , can be seen in Fig. 4a and Fig.
4c.
The advantage of kc is defined to a large extent by a priori probability value of a system condition normal
condition p1 . The higher this probability is the higher is the advantage ofkc Fig. 4a and Fig. 4c. The physical
meaning of parameter b change expresses the information content of feature recognition (Stage 2). The advantage
of kc depends on information content of the second-stage features and is increased along with the parameter b.
The more kc the higher the information content of used features at the situation recognition in the network (see
Fig.4). kc is growing with the decrease of false alarm C probability, therewith kc is changed from one to tens of
times.
The advantage of kc depends also on features information content of the first-stage, which is grown along with
the parameter α increase (Fig. 5).
The value of the errors, therefore, depends on features information content, at both the first and the second
stages (see formula (17) and Fig. 5).
A stage-by-stage control procedure ensures the maximum accuracy in detecting abnormal situations in the
network because it uses independent recognition features at each of the stages and, therefore, is no worse than the
Bayesian procedure. At stage-by-stage control, the making decision on TN status is carried out with attracting
extra features so far as necessary. A number of stages switching is reduced in the wake of stage number increase.
The control is over when decision on TN on proper functioning is made.
Figure 4: Dependence curves: a) kc on C at p1 = 0.1 ; b) kc on C at p1 = 0.5 ; c) kc on C at p1 = 0.9 ; d) kc on
C at p1 = 0.1 and different parameters of a.
Figure 5: Dependence curves: a) kc on C at p1 = 0.5 and different parameters of a; b) kc on C at p1 = 0.9 and
different parameters of a.
4 Reduction of control information volume due to the usage of stage-by-stage
classification
Since the solution on TN normal functioning at the first stage may be taken based on the local state information
about the node condition (for example, used volume of buffer memory, the state of the outgoing channels) there
is no need to the information exchange with other network nodes. This leads to a reduction of the management
information circulating through the network.
Forasmuch as the information exchange is usually performed with the neighboring network nodes and followed
by the transfer of management information, so this also leads to the reduction of its volume as the information
is not brought to each remote switching node.
The value Pf.a. = p1 α1 (1+α2 +α2 α3 +. . .+α2 α3 . . . αn ) determines that part of the total information received
per unit, which is to be analyzed on the second and subsequent stages. It determines the degree of reduction of
information volume to be transmitted between the transport network nodes to specify the type of violation:
1
ρc = (22)
p1 α1 (1 + α2 + . . . α2 α3 . . . αn )
Thus, the degree of volume reduction of control information circulating through the network depends on the
TI error occurring at each stage. The ad-vantage regarding the reduction of management information volume
depends on the information content of features recognition at the second and next stages since the information
content of feature α1 at the first stage is a fixed value. It is defined by free buffer space capacity, the value
of which can be strictly checked by local information of each certain node. However, the increase of features
information content at later stages is linked with the measurements on the network, the capacity of which
determines the quality of decision-making in a gradual monitoring and leads to power consumption increase.
These measurements are required to improve the information content of features. They are linked with the need
to attract additional measuring resources and natural increase of analysis time.
At two-stage control procedure that part of information which is in the second stage is analyzed just the part
of information which is the probability of detector ”false alarm” is analyzed on the second stage:
Z∞
Pf.a. = p1 f1 (x)dx (23)
x0
The values (23) define that part of the total information flow that is to be analyzed at the second stage. It
defines directly the degree of of information volume reduction which is to be transmitted from node to node for
violation type specification.
At Rayleigh laws of features distribution the formula (22) takes the form:
ln pC1
ρc = 1/p1 exp(− ) (24)
1 + ( ab )2
5 Example
Let us suppose that power consumption, spent per volume unit of the processed management information, is a
constant fixed value. Then, by the value of management information volume reduction in switching nodes pc we
can consider power consumption reduction expended on processing of management information.
Figure 6: a)Reduction degree of volume control information depending on a given probability of ”false alarms”;
b)Odds of reducing errors due to two-stage procedure for a given a priori probability of the normal functioning.
Thus, the reduction degree level is equal to 3, this value is reached at C from 0.001 to 0.006 when the relative
degree of classes intersection b/a from 1.1 to 0.8 and a priori probability of the normal functioning p1 = 0.5.
When C = 0.0022 Fig. 6 (d point) the advantage of error kc reduction is not less than two times when the
management information volume reduction degree is equal to 3 Fig. 6 (d point).
Therefore, at gradual control procedure we can observe not only the advantage of power consumption but also
the classification error reduction of the object states under control.
6 Conclusion
The carried out studies allow to draw the following conclusions:
1. The reduction of power consumption by transport telecommunications network at the analysis and identifi-
cation of faults, malfunctions can be achieved owing to the use of gradual detection of abnormal situations. For
the analysis, only that part of features measuring tools information which belong to a given stage. At the first
stage according to local information which is contained in given switch node, the operating trouble presence is
defined, while on the second and next stages the degree and type of failure are clarified.
2. The problem of monitoring errors optimization and detection of classification optimal threshold for general
cases was solved and the example its application to two-stage procedure of abnormal situation detection at
Rayleigh features distribution laws.
3. The power consumption of automatic monitoring and adaptive control system depends on the type and form
of features x and y distribution density Fig. 3. Power consumption is the more than the less information content
features a (detector) and b (analyzer).
4. The degree of management information volume reduction owing to the application of a stage-by-stage prin-
ciple of abnormal situations detection in the network was estimated. It is defined by a given probability of a
”false alarm” and its value can be from 1.1 to 3 times. It was shown that the management information volume
reduction (hence also power consumption), when using abnormal situation gradual detection, depends on the
given probability of a ”false alarm” and correlations of features a and b information content. It is increased with
the decrease of “false alarm” given probability.The advantage of kc is determined by a priori probability value
of p1 system normal functioning (see Fig.4). The higher the probability p1 , the greater the advantage of kc .
5. The advantage kc depends on the features information content both the first and second stages, it grows with
the increase of parameters a and b. It was shown that the advantage is equal to values of two or more times due
to the application of two-stage procedure estimating it by coefficient kc , .
6. The use of multi-agent systems and vesting the agents with authorities, depending on the level of the trans-
port telecommunication network hierarchy, allows carrying out address screening of information features and
attracting additional features for analysis of origin reasons and abnormal situations location. Only required
part of complex system netmetric is used. Such approach allows to reduce power consumption of automatic
monitoring and adaptive control system due to the reduction of management information volume that is to be
to be transmitted between nodes to specify the deviation from norm of the network controlled parameter.
7. In order to reduce power consumption of modern TN it is advisable to carry out studies that are more funda-
mental: a) by the use of agent-oriented approach; b) the application of x and y features distribution spontaneous
laws in the control system and adaptive control.
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