Automated Real Time Performance Management for...
27
© Nokia Siemens Networks / University of Tuebingen IWAS07 / Bandh / 2007-06-18 Automated Real Time Performance Management for Mobile Networks Tobias Bandh*, Georg Carle*, Henning Sanneck + , Lars-Christoph Schmelz + *University of Tübingen, + Nokia Siemens Networks
Transcript of Automated Real Time Performance Management for...
©N
okia
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rsity
of T
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ngen
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andh
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07-0
6-18
Aut
omat
ed R
eal T
ime
Perf
orm
ance
Man
agem
ent f
or
Mob
ile N
etw
orks
Tobi
as B
andh
*, G
eorg
Car
le*,
Hen
ning
San
neck
+ , La
rs-C
hris
toph
Sch
mel
z+
*Uni
vers
ity o
f Tüb
inge
n, + N
okia
Sie
men
s N
etw
orks
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Mot
ivat
ion
Per
form
ance
man
agem
ent i
n to
days
mob
ile n
etw
orks
:
•O
fflin
e –
No
poss
ibilit
ies
to a
cces
s re
cent
dat
a•
Larg
e de
lays
–ty
pica
lly >
15 m
inut
es•
No
real
aut
omat
ion
–C
ombi
natio
n of
ope
rato
r kno
wle
dge
and
hand
iwor
k
Goa
ls•
Intro
duct
ion
of a
n au
tom
ated
, per
form
ance
man
agem
ent s
yste
m fo
r mob
ile
netw
orks
that
exp
loits
real
tim
e ca
pabi
litie
s•
Dire
ct a
cces
s to
rece
nt p
erfo
rman
ce d
ata
•P
aral
lel m
onito
ring
of a
larg
e nu
mbe
r of n
etw
ork
elem
ents
•Lo
ng-te
rm o
bjec
tive:
Aut
onom
ous
perfo
rman
ce m
anag
emen
t•
Gai
n kn
owle
dge
abou
t upc
omin
g cr
itica
l situ
atio
ns to
be
able
to a
void
them
be
com
ing
criti
cal
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Age
nda
•Mot
ivat
ion
–W
hat i
s it
all a
bout
?
•Bas
ics
–In
trodu
ctio
n in
to p
erfo
rman
ce m
anag
emen
t•S
yste
m D
esig
n–
Set
up o
f the
pre
sent
ed s
yste
m•E
valu
atio
n–
Com
paris
on o
f diff
eren
t per
form
ance
man
agem
ent s
chem
es•C
oncl
usio
n
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Intr
oduc
tion
PM is
bas
ed o
n da
ta o
rigin
atin
g fr
om th
ree
diffe
rent
tim
esca
les
•Lo
ng T
erm
: Dat
a co
llect
ed o
ver a
ver
y lo
ng p
erio
d of
tim
e (w
eeks
/mon
ths)
•M
id T
erm
: Dat
a of
sev
eral
day
s is
ana
lyse
d•
Sho
rt Te
rm: 1
5 M
inut
es to
sev
eral
hou
rs
•U
sual
ly n
o re
al ti
me
data
is u
sed!
Why
not
? Ex
istin
g lim
itatio
ns th
at a
void
the
usag
e of
real
tim
e da
ta•
Proc
essi
ng p
ower
•Ba
ndw
idth
•H
uman
lim
itatio
ns
How
to o
verc
ome
thes
e lim
itatio
ns?
•P
M -
Dat
a se
lect
ion
–pr
ovid
e on
ly “n
eede
d”da
ta•
Pre
proc
essi
ngan
d co
mpr
essi
on o
f dat
a al
read
y at
the
sour
ce•
Dyn
amic
ada
ptat
ion
of P
M d
ata
rate
s•
Sel
ectiv
e O
pera
tor N
otifi
catio
ns, i
mpr
oved
GU
I, re
conf
igur
atio
n pr
opos
als
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Bas
ics
Bas
ic P
M T
asks
•M
onito
ring
of m
easu
rem
ent r
esul
ts a
nd d
eriv
ed v
alue
s•
Ada
ptat
ion
of m
easu
rem
ent j
obs
•R
econ
figur
atio
n of
net
wor
k el
emen
ts•
Ver
y co
mpl
ex in
terc
onne
cted
cau
se a
nd e
ffect
dep
ende
ncie
s
Polic
y Ba
sed
Per
form
ance
Man
agem
ent i
n M
obile
Net
wor
ks•
Pol
icy
appr
oach
is v
ery
pow
erfu
l and
flex
ible
•Fe
w a
bstra
ct p
olic
ies
can
suffi
ce fo
r fin
e gr
ain
man
agem
ent o
f ala
rge
num
ber o
f net
wor
k el
emen
ts•
Man
ual P
M-ta
sks
that
can
be
trans
form
ed in
to p
olic
ies
are
auto
mat
ion
cand
idat
es•
Pol
icy
desi
gn h
as to
car
e ab
out c
ontra
dict
ing
polic
ies
•P
olic
y ev
alua
tion
take
s ca
re o
f the
abo
ve n
amed
cau
se a
nd e
ffect
inte
rcon
nect
ions
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Syst
em D
esig
n
Inte
grat
ion
of th
e Po
licy
Bas
ed S
ubsy
stem
into
the
com
mon
PM
Arc
hite
ctur
e:P
olic
y B
ased
EM
S S
ubsy
stem
!
•Mea
sure
men
t Dat
a of
reco
nfig
urab
le m
easu
rem
ent j
obs
is g
athe
red
and
anal
ysed
. •C
urre
nt s
ituat
ion
is a
sses
sed
in c
ase
of a
crit
ical
eve
nt, t
rigge
r cou
nter
activ
e m
easu
res!
Her
e: n
otify
the
hum
an o
pera
tor
•Mai
n In
tent
ion:
Red
uce
reso
urce
con
sum
ptio
n –
incr
ease
am
ount
of i
nfor
mat
ion
avai
labl
e to
the
hum
an o
pera
tor i
n cr
itica
l situ
atio
ns a
nd re
duce
info
rmat
ion
whe
n it
is n
ot “
need
ed”.
•B
asic
ass
umpt
ion:
Con
figur
able
mea
sure
men
t job
s ar
e av
aila
ble!
•R
eal t
ime
data
: Dat
a w
ith a
min
imal
del
ay b
etw
een
mea
sure
men
t and
av
aila
bilit
y at
the
EM
S D
omai
n
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Inte
grat
ion
into
com
mon
PM
Arc
hite
ctur
e
•Pol
icy
syst
em
cent
rally
loca
ted
in
EM
S D
omai
n•R
ecei
ves
PM
Dat
a w
hich
is a
naly
zed
•Rec
onfig
urat
ions
ca
n be
trig
gere
d•O
pera
tor i
s no
tifie
d in
crit
ical
situ
atio
ns
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Syst
em D
esig
n
•Inte
grat
ion
of th
e Po
licy
Bas
ed S
ubsy
stem
into
the
com
mon
PM
Arc
hite
ctur
e:P
olic
y B
ased
EM
S S
ubsy
stem
!
•Mea
sure
men
t Dat
a of
reco
nfig
urab
le m
easu
rem
ent j
obs
is g
athe
red
and
anal
ysed
. •C
urre
nt s
ituat
ion
is a
sses
sed
in c
ase
of a
crit
ical
eve
nt, t
rigge
r cou
nter
activ
e m
easu
res!
Her
e: n
otify
the
hum
an o
pera
tor
•Mai
n In
tent
ion:
Red
uce
reso
urce
con
sum
ptio
n –
incr
ease
am
ount
of i
nfor
mat
ion
avai
labl
e to
the
hum
an o
pera
tor i
n cr
itica
l situ
atio
ns a
nd re
duce
info
rmat
ion
whe
n it
is n
ot “
need
ed”.
•B
asic
ass
umpt
ion:
Con
figur
able
mea
sure
men
t job
s ar
e av
aila
ble!
•R
eal t
ime
data
: Dat
a w
ith a
min
imal
del
ay b
etw
een
mea
sure
men
t and
av
aila
bilit
y at
the
EM
S D
omai
n
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Bas
ic, C
entr
aliz
ed A
ppro
ach
The
polic
y ba
sed
EM
S S
ubsy
stem
gat
hers
and
ana
lyse
s al
l inf
orm
atio
n or
igin
atin
g fro
m th
e ne
twor
k el
emen
ts.
Func
tiona
lity
can
easi
ly b
e de
scrib
ed:
Valu
e Pr
edic
tion:
Sta
te fo
r non
crit
ical
situ
atio
ns –
min
imal
am
ount
of i
nfor
mat
ion
Thre
shol
d M
onito
ring
Stat
e:C
ritic
al s
ituat
ions
are
indi
cate
d –
info
rmat
ion
leve
l is
incr
ease
dA
larm
Sta
te:M
axim
al a
mou
nt o
f inf
orm
atio
n is
gen
erat
ed, o
pera
tor i
s no
tifie
d
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Evol
utio
nary
, Dis
trib
uted
App
roac
h
This
app
roac
h fo
llow
s th
e pr
edic
ted
evol
utio
n of
mob
ile n
etw
orks
tow
ards
a
flatte
ned
hier
arch
y w
ith e
nhan
ced
netw
ork
elem
ents
at t
he e
dge
Func
tiona
lity
is d
istri
bute
d fro
m c
entra
l ent
ities
to th
e ne
twor
k el
emen
tsP
roce
ssin
g ca
paci
ty a
t the
net
wor
k el
emen
ts is
use
d to
furth
erly
redu
ce re
ssou
rce
cons
umpt
ion
at c
entra
l ent
ities
In th
is a
ppro
ach
Valu
e Pr
edic
tion
and
Thre
shol
d M
ontit
orin
g st
ates
are
sh
ifted
to th
e ne
twor
k el
emen
ts
No
mes
sage
is tr
ansm
itted
unt
il a
mea
sure
men
t val
ue c
ross
es a
pre
defin
ed
thre
shol
dA
fter t
his
notif
icat
ion
the
cent
ral e
ntity
can
cho
ose
to g
ain
cont
rol o
n th
e m
easu
rem
ent j
obs
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Evol
utio
n
Per
form
ed o
n N
EP
erfo
rmed
on
NE
Per
form
ed o
n N
EP
erfo
rmed
at E
MS
(Cen
tral e
ntity
for a
ll N
Es)
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orks
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rsity
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Evol
utio
nary
, Dis
trib
uted
App
roac
h
This
app
roac
h fo
llow
s th
e pr
edic
ted
evol
utio
n of
mob
ile n
etw
orks
tow
ards
a
flatte
ned
hier
arch
y w
ith e
nhan
ced
netw
ork
elem
ents
at t
he e
dge
Func
tiona
lity
is d
istri
bute
d fro
m c
entra
l ent
ities
to th
e ne
twor
k el
emen
tsP
roce
ssin
g ca
paci
ty a
t the
net
wor
k el
emen
ts is
use
d to
furth
erly
redu
ce re
ssou
rce
cons
umpt
ion
at c
entra
l ent
ities
In th
is a
ppro
ach
Valu
e Pr
edic
tion
and
Thre
shol
d M
ontit
orin
g st
ates
are
sh
ifted
to th
e ne
twor
k el
emen
ts
No
mes
sage
is tr
ansm
itted
unt
il a
mea
sure
men
t val
ue c
ross
es a
pre
defin
ed
thre
shol
dA
fter t
his
notif
icat
ion
the
cent
ral e
ntity
can
cho
ose
to g
ain
cont
rol o
n th
e m
easu
rem
ent j
obs
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Eval
uatio
nTh
e m
ain
focu
s is
on
the
relia
billi
ty in
det
ectin
g cr
itica
l situ
atio
ns a
nd th
e im
pact
on
the
netw
ork
links
by
the
mes
sage
s tra
nsfe
rred
•C
an a
ll cr
itica
l situ
atio
ns w
ith a
min
imal
del
ay b
e de
tect
ed?
•W
hich
con
figur
atio
ns o
f the
mea
sure
men
t job
s ca
n be
sup
porte
d?
Eva
luat
ion
is b
ased
on
a m
odifi
ed tr
acef
ile fr
om a
3G
net
wor
k•
One
KP
I with
thre
e S
ubK
PIs
was
cho
sen
(Han
dove
rFai
lure
Rat
e, In
tra N
odeB
, In
ter N
odeB
, Int
er R
NC
)C
ompa
rison
of 4
PM
-Sch
emes
•C
onve
ntio
nal P
M:3
0 m
in G
P, t
rans
mis
sion
of t
he fu
ll se
t of c
ount
ers
•R
TPM
1: F
ixed
1 m
inut
e G
P, 4
act
ive
mea
sure
men
t job
s, a
ll va
lues
are
alw
ays
sent
•R
TPM
2: V
aria
ble
GP
(1, 5
, 10
min
utes
), ce
ntra
lly c
ontro
lled
by p
olic
y ba
sed
EM
S s
ubsy
stem
•R
TPM
3:V
aria
tion
of R
TPM
2 bu
t usi
ng th
e di
strib
uted
app
roac
h, h
igh
rate
m
essa
ges
are
requ
este
d as
soo
n as
the
thre
shol
d is
cro
ssed
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Eval
uatio
n –
KPI
Cha
nges
Sev
en s
ituat
ions
w
here
the
KP
I va
lue
us a
bove
th
e th
resh
old
of
20%
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Eval
uatio
n
The
mai
n fo
cus
is o
n th
e re
liabi
llity
in d
etec
ting
criti
cal s
ituat
ions
and
the
impa
ct o
n th
e ne
twor
k lin
ks b
y th
e m
essa
ges
trans
ferr
ed•
Can
all
criti
cal s
ituat
ions
with
a m
inim
al d
elay
be
dete
cted
?•
Whi
ch c
onfig
urat
ions
of t
he m
easu
rem
ent j
obs
can
be s
uppo
rted?
Eva
luat
ion
is b
ased
on
a m
odifi
ed tr
acef
ile fr
om a
3G
net
wor
k•
One
KP
I with
thre
e S
ubK
PIs
was
cho
sen
(Han
dove
rFai
lure
Rat
e, In
tra N
odeB
, Int
er
Nod
eB, I
nter
RN
C)
Com
paris
son
of 4
PM
-Sch
emes
•C
onve
ntio
nal P
M:3
0 m
in G
P, t
rans
mis
sion
of t
he fu
ll se
t of c
ount
ers
•R
TPM
1: F
ixed
1 m
inut
e G
P, 4
act
ive
mea
sure
men
t job
s, a
ll va
lues
are
alw
ays
sent
•R
TPM
2: V
aria
ble
GP
(1, 5
, 10
min
utes
), ce
ntra
lly c
ontro
lled
by p
olic
y ba
sed
EM
S
subs
yste
m•
RTP
M3:
Var
iatio
n of
RTP
M2
but u
sing
the
dist
ribut
ed a
ppro
ach,
hig
h ra
te m
essa
ges
are
requ
este
d as
soo
n as
the
thre
shol
d is
cro
ssed
Slid
e 1
5
tb7
D
o w
e ne
ed t
his
slid
e?To
bias
Ban
dh, 6
/6/2
007
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Eval
uatio
n
Mea
sure
d V
alue
s pe
r eva
luat
ed P
M s
chem
e:•
Tota
l num
ber o
f mes
sage
s w
ithin
24
hour
s•
Num
ber o
f mes
sage
s se
nt p
er h
our (
min
, avg
, max
)•
Num
ber o
f det
ecte
d cr
itica
l situ
atio
ns•
Del
ay b
etw
een
occu
rren
ce a
nd d
etec
tion
of a
crit
ical
situ
atio
n (m
in, a
vg,
max
)•
Num
ber o
f pre
dict
ed b
ut n
ot o
ccur
red
thre
shol
d vi
olat
ions
Ass
essm
ents
:•
Det
ectio
n R
elia
bilit
y•
Impr
ovem
ents
in m
essa
ge re
duct
ion
•Im
pact
on
com
mun
icat
ion
links
©N
okia
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men
s N
etw
orks
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nive
rsity
of T
uebi
ngen
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andh
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Res
ults
Det
ectio
n R
elia
bilit
y an
d D
elay
•Con
vent
iona
l PM
m
isse
s fo
ur c
ritic
al
situ
atio
ns a
nd h
as a
lo
ng d
elay
•Pol
icy
base
d PM
ha
s a
fast
er
dete
ctio
n w
ith a
hi
gher
relia
bilit
y
©N
okia
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men
s N
etw
orks
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rsity
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uebi
ngen
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Res
ults
Num
ber o
f mes
sage
s se
nt p
er K
PI
•Pol
icy
cont
rolle
d P
M tr
ansm
its
notic
eabl
e le
ss
mes
sage
s co
mpa
red
to th
e fix
ed 1
min
ute
sche
me
•Evo
lutio
nary
ap
proa
ch d
oes
not
trans
mit
any
mes
sage
s fo
r 15
hour
s•F
ixed
1 m
inut
e sc
hem
e ha
s le
ss
mes
sage
s bu
t tra
nsfe
rs a
lway
s an
y da
ta a
vaila
ble
©N
okia
Sie
men
s N
etw
orks
/ U
nive
rsity
of T
uebi
ngen
IWA
S07
/ B
andh
/ 20
07-0
6-18
Res
ults
Impa
cts
on C
omm
unic
atio
n Li
nks
Bas
ic S
cena
rio:
•R
NC
Dom
ain
com
pris
ing
1000
cel
ls w
ith a
bsol
utel
y id
entic
al b
ehav
ior
•33
% o
f the
2 M
Bit/
s O
&M
link
are
ava
ilabl
e fo
r PM
•10
bas
ic K
PIs
are
mon
itore
d, e
ach
of th
em h
as 3
Sub
KP
Is
1.Fi
xed
1 m
in G
P: 1
00%
of t
he li
nk u
sed
for b
asic
mon
itorin
g2.
Pol
icy
Con
trolle
d: 7
9% li
nk u
sage
for b
asic
mon
itorin
g3.
Pol
icy
Con
trolle
d, m
in G
P in
crit
ical
Situ
atio
n 10
sec
onds
: onl
y<2
5 %
of t
he
basi
c K
PIs
can
be
mon
itore
d4.
As
2, b
ut a
dditi
onal
Sub
KP
Is a
re a
ctiv
ated
: 33%
of t
he c
ells
can
be
mon
itore
d
Wor
st C
ase
Sce
nario
s du
e to
iden
tical
cel
l beh
avio
r
©N
okia
Sie
men
s N
etw
orks
/ U
nive
rsity
of T
uebi
ngen
IWA
S07
/ B
andh
/ 20
07-0
6-18
Con
clus
ion
and
Futu
re W
ork
•Rea
l-tim
e da
ta c
ombi
ned
with
a p
olic
y ba
sed
syst
em p
rovi
des
a re
al e
nhan
cem
ent
for p
erfo
rman
ce m
anag
emen
t in
mob
ile n
etw
orks
•A s
ingl
e po
licy
can
mat
ch a
larg
e nu
mbe
r of s
ituat
ions
•Pol
icie
s in
trodu
ce fl
exib
ility
into
PM
•Pol
icie
s ca
n be
def
ined
offl
ine
whi
ch re
duce
s th
e nu
mbe
r of e
rror
sou
rces
co
mpa
red
to o
nlin
e re
actio
n in
crit
ical
situ
atio
ns•P
olic
y ba
sed
syst
ems
free
the
hum
an o
pera
tor f
or o
ther
task
s
•Des
igni
ng o
ptim
al p
olic
ies
is a
har
d ta
sk•N
o on
e ca
n as
sess
any
pos
sibl
e cr
itica
l situ
atio
n•P
olic
ies
have
to b
e re
fined
Futu
re W
ork
•Enh
anci
ng th
e sy
stem
by
incl
udin
g ad
ditio
nal k
now
ledg
e in
to p
olic
ies
and
the
pred
ictio
n al
gorit
hm (s
easo
nal t
rend
s)•D
ynam
ic a
dapt
atio
n of
the
polic
ies
base
d on
obs
erve
d op
erat
or b
ehav
ior a
fter
rais
ed a
larm
s•S
elf h
ealin
g …
©N
okia
Sie
men
s N
etw
orks
/ U
nive
rsity
of T
uebi
ngen
IWA
S07
/ B
andh
/ 20
07-0
6-18
Que
stio
ns?
http
://co
mm
ons.
wik
imed
ia.o
rg/w
iki/I
mag
e:Tu
ebin
gen_
Nec
karfr
ont.j
pg
©N
okia
Sie
men
s N
etw
orks
/ U
nive
rsity
of T
uebi
ngen
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/ B
andh
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07-0
6-18
Bac
kup
©N
okia
Sie
men
s N
etw
orks
/ U
nive
rsity
of T
uebi
ngen
IWA
S07
/ B
andh
/ 20
07-0
6-18
PM N
etw
ork
Arc
hite
ctur
e
3G N
etw
ork
LTE
Net
wor
k
©N
okia
Sie
men
s N
etw
orks
/ U
nive
rsity
of T
uebi
ngen
IWA
S07
/ B
andh
/ 20
07-0
6-18
Key
-Per
form
ance
-Indi
cato
rs -
KPI
s
•KP
Is c
ombi
ne lo
wle
vel i
nfor
mat
ion
into
mor
e m
eani
ngfu
l dat
a–
Sev
eral
cou
nter
val
ues
are
used
to c
alcu
late
one
KP
I val
ue
•KP
Is a
re m
ostly
ope
rato
r def
ined
•KP
Is c
an b
e us
ed to
ass
ess
the
stat
e of
the
netw
ork
•Typ
ical
KP
Is–
Cal
lDro
pRat
eKP
I–
Cal
lSet
upS
ucce
ssR
ate
–D
ownl
inkC
ellL
oad
–IS
HH
OO
utFR
pCel
l(au
chcs
und
ps)
–N
oofa
bnor
mal
RR
CR
el–
Out
goin
gHar
dHan
dove
rFai
lure
Rat
e–
Rab
Est
abTy
peFR
(auc
hcs
und
ps)
–R
eloc
Out
FR–
RR
Cdr
opR
ate
–S
oftH
ando
verD
ropR
ate
–B
ranc
hAdd
ition
Failu
reR
ate
(auc
hin
tra N
odeB
, int
ra R
NC
, int
er R
NC
)
©N
okia
Sie
men
s N
etw
orks
/ U
nive
rsity
of T
uebi
ngen
IWA
S07
/ B
andh
/ 20
07-0
6-18
Del
ay
•Th
e de
lay
used
her
e co
nsis
ts o
f thr
ee p
arts
:
1.G
ranu
larit
y P
erio
d2.
Tran
smis
sion
del
ay3.
Pro
cess
ing
dela
y
Del
ay
GP
Tran
smis
sion
Pro
cess
ing
Eve
ntO
pera
tor
Not
ifica
tion
©N
okia
Sie
men
s N
etw
orks
/ U
nive
rsity
of T
uebi
ngen
IWA
S07
/ B
andh
/ 20
07-0
6-18
