Jou nal o Compu e Technology and So wa e
ISSN: 2998-2383
Vol. 3, No. 5, 2024
Tempo al Con as i e Rep esen a ion Lea ning o Unsupe ised
Anomaly De ec ion in High-Dimensional Cloud En i onmen s
Sibo Wang
Rice Uni e si y, Hous on, USA
coldb [email protected]
Abs ac : This pape p oposes an unsupe ised anomaly de ec ion me hod based on con as i e lea ning o add ess challenges
in cloud compu ing en i onmen s, such as high da a dimensionali y, complex s uc u e, and lack o labels. The me hod segmen s
aw ime se ies moni o ing da a in o subsequences using a sliding window mechanism and applies a ious da a augmen a ion
s a egies o cons uc posi i e and nega i e sample pai s, guiding he model o lea n disc imina i e embeddings wi hou
supe ision. A empo al a en ion mechanism is in eg a ed o cap u e key dynamic ea u es in he sequence, enhancing he model's
abili y o ep esen long- e m dependencies and local luc ua ions. Anomaly sco es a e calcula ed by measu ing simila i ies in he
embedding space, enabling an e icien de ec ion p ocess wi hou he need o labels. The me hod is e alua ed on a cloud
moni o ing da ase ac oss di e en augmen a ion s a egies, pa ame e se ings, and empo al modeling con igu a ions.
Expe imen al esul s show ha i ou pe o ms se e al ecen ly published unsupe ised models in F1 Sco e, AUC, and KS Sco e,
demons a ing i s e ec i eness and enginee ing adap abili y in handling high-dimensional dynamic da a wi hin cloud pla o m
scena ios.
Keywo ds: unsupe ised lea ning; ime se ies modeling; embedding ep esen a ion; sys em moni o ing
1. In oduc ion
In oday's da a-d i en echnological ecosys em, cloud
compu ing has become he ounda ion o suppo ing complex
applica ions and la ge-scale da a p ocessing asks. As
en e p ises inc easingly pu sue digi al and in elligen
ans o ma ion, he wo kloads on cloud pla o ms con inue o
g ow. Sys em s uc u es a e becoming mo e complex, and he
un ime en i onmen is becoming mo e dynamic. This high
deg ee o i ualiza ion and dis ibu ion b ings no only highe
demands o scalabili y and lexibili y bu also exposes he
pla o m o mo e po en ial secu i y and s abili y isks.
Abno mal e en s such as pe o mance bo lenecks, ne wo k
a acks, and esou ce misalloca ions can signi ican ly impac
se ice quali y and e en lead o business in e up ions.
The e o e, e icien ly iden i ying and loca ing anomalies in
la ge-scale and dynamic en i onmen s has become a co e issue
in ensu ing he s able ope a ion o cloud pla o ms.
Howe e , anomaly de ec ion in cloud compu ing p esen s
signi ican challenges[1]. Fi s , he moni o ed da a is high-
dimensional and o igina es om mul iple sou ces, including
logs, me ics, and acing da a, esul ing in s ong he e ogenei y
and high dimensionali y. Second, due o equen sys em
e olu ion and apid componen upda es, he bounda y be ween
no mal and abno mal s a es is o en unclea , and abno mal
pa e ns a e di icul o de ine in ad ance. Addi ionally, a la ge
po ion o he da a lacks labeled anno a ions, making adi ional
supe ised de ec ion me hods less applicable. Agains his
backg ound, i is c ucial o ind a me hod ha can ex ac
essen ial ea u es and iden i y hidden anomaly pa e ns wi hou
elying on labels[2].
In ecen yea s, unsupe ised lea ning me hods ha e
a ac ed conside able a en ion in he ield o anomaly
de ec ion. Thei main ad an age lies in he abili y o disco e
s uc u al ea u es and beha io al pa e ns om da a wi hou
he need o manual labeling. Among hem, con as i e
lea ning, as a ep esen a i e unsupe ised ep esen a ion
lea ning s a egy, guides he model o lea n disc imina i e
ea u e ep esen a ions by cons uc ing posi i e and nega i e
sample pai s. I demons a es s ong ea u e ex ac ion
capabili ies and b oad ans e abili y. Con as i e lea ning
enhances he model's sensi i i y o s uc u al o seman ic
di e ences, he eby imp o ing i s abili y o dis inguish
abno mal s a es. Applying his app oach o unsupe ised
anomaly de ec ion in cloud en i onmen s shows g ea po en ial
in add essing challenges such as high dimensionali y and label
sca ci y.
Mo eo e , da a in cloud compu ing scena ios exhibi clea
empo al p ope ies and dynamic e olu ion. Sys em
pe o mance me ics, ne wo k a ic, and use beha io aces
o en con ain complex empo al dependencies and local
luc ua ion pa e ns. E ec i ely modeling hese empo al
s uc u es has a di ec impac on anomaly de ec ion
pe o mance. In eg a ing con as i e lea ning wi h empo al
modeling s a egies may enhance he abili y o cap u e anomaly
ends, luc ua ion signals, and pe iodic changes. This can
imp o e he model's obus ness and gene aliza ion in dynamic
en i onmen s[3]. The e o e, con as i e lea ning s a egies ha
inco po a e bo h empo al awa eness and s uc u al
disc imina ion a e a p omising di ec ion o anomaly de ec ion
in cloud compu ing.
In summa y, he e is an u gen need o an e icien
algo i hmic amewo k ha can pe o m unsupe ised
modeling while ex ac ing key ea u es om high-dimensional,
mul imodal, and empo al da a. Unsupe ised anomaly
de ec ion me hods based on con as i e lea ning ha e eme ged
in esponse o his demand. These me hods align wi h he
cu en da a-d i en pa adigm and p o ide new echnical
suppo o imp o ing in elligen moni o ing and ope a ional
secu i y o cloud pla o ms. Thei de elopmen is o g ea
signi icance o ensu ing he eliabili y, s abili y, and se ice
quali y o cloud compu ing sys ems[4].
2. Rela ed wo k
Wi h he con inuous ad ancemen o cloud compu ing
echnologies, mo e en e p ises and o ganiza ions a e mig a ing
hei co e se ices o he cloud. This shi helps hem espond
o apidly changing ma ke demands and inc easing da a
p ocessing p essu e. Cloud pla o ms o e elas ic scalabili y,
au oma ed esou ce scheduling, and mul i- enan sha ed
a chi ec u es, which signi ican ly imp o e compu ing
e iciency and ope a ional lexibili y[5]. Howe e , hese
ad an ages also in oduce g ea e sys em complexi y and
un ime unce ain y. F equen da a in e ac ions and dynamic
changes among he e ogeneous componen s make he pla o m
mo e ulne able o a ious abno mal e en s. In high-
concu ency and high- a ic p oduc ion en i onmen s, sudden
pe o mance deg ada ion, se ice ailu es, and a ack beha io s
pose se ious h ea s o sys em s abili y. The e o e, building
e icien and accu a e anomaly de ec ion mechanisms in cloud
en i onmen s is c ucial o ensu ing business con inui y and
da a secu i y. I also se es as a undamen al suppo o
enhancing esou ce u iliza ion and se ice quali y.
Moni o ing sys ems in cloud pla o ms ypically collec
la ge olumes o mul i-dimensional ime se ies da a, such as
CPU usage, memo y consump ion, ne wo k a ic, and se ice
call chains[6]. This da a is gene a ed con inuously densely and
dynamically. In such en i onmen s, anomalies o en appea as
a e and sub le shi s o sudden s a e changes, lacking clea
bounda ies om no mal pa e ns and easily o e whelmed by
noise. Addi ionally, he ypes o anomalies a e highly di e se
and may esul om miscon igu a ions, ex e nal a acks,
wo kload su ges, o ha dwa e ailu es. These ac o s in oduce
high unce ain y. T adi ional ule-based me hods o supe ised
lea ning models ace signi ican limi a ions in p ac ice. In
pa icula , when da a labeling is expensi e and abno mal
samples a e sca ce, building classi ie s o eg esso s ha ely
on labeled da a becomes imp ac ical. Unde condi ions wi hou
labels and weak s uc u e, au oma ically disco e ing po en ial
anomaly signals om aw da a becomes a co e challenge in
cu en esea ch.
In ecen yea s, unsupe ised lea ning has eme ged as a key
app oach o add ess hese issues. Among hem, con as i e
lea ning, which lea ns disc imina i e ea u es by modeling
ela ionships be ween samples, has shown s ong adap abili y
and ep esen a ional powe in anomaly de ec ion asks. By
designing app op ia e mechanisms o cons uc posi i e and
nega i e sample pai s, con as i e lea ning can guide models o
cap u e seman ic simila i y and di e ence wi hou equi ing
label supe ision. This leads o mo e e ec i e embedding
ep esen a ions. In cloud en i onmen s, whe e da a o en
con ains pe iodic luc ua ions, local noise, and s uc u al
edundancy, con as i e lea ning helps iden i y a ypical
pa e ns hidden in dynamic complexi y by emphasizing
s uc u al consis ency and dis ibu ion bounda ies. Mo e
impo an ly, his me hod o e s good scalabili y and
ans e abili y[7]. I can adap o di e en pla o m
a chi ec u es and da a cha ac e is ics, imp o ing bo h s abili y
and eliabili y in eal-wo ld applica ions while main aining
algo i hm gene alizabili y[8].
3. A chi ec u al App oach
The ne wo k a chi ec u e illus a es an unsupe ised
anomaly de ec ion me hod based on con as i e lea ning. The
p ocess includes sliding window segmen a ion, da a
augmen a ion, and embedding lea ning h ough an encode . A
dual-b anch s uc u e cons uc s posi i e and nega i e sample
pai s o guide he model in lea ning disc imina i e
ep esen a ions wi hou labels. A empo al a en ion mechanism
is in oduced o cap u e key dynamic in o ma ion wi hin he
sequences. Finally, anomaly sco es a e es ima ed h ough
simila i y compu a ion, aligning wi h he me hod p oposed in
he pape . The model a chi ec u e is shown in Figu e 1.
Figu e 1. Con as i e Lea ning o Unsupe ised De ec ion in Cloud Anomalies
This pape p oposes an unsupe ised anomaly de ec ion
me hod based on con as i e lea ning, which aims o
au oma ically lea n disc imina i e ep esen a ions om high-
dimensional, mul i-sou ce, and ime-se ies cloud compu ing
moni o ing da a o achie e e ec i e iden i ica ion o abno mal
beha io s. Fi s , le he inpu aw moni o ing da a be
},...,,{ 21 T
xxxX
, whe e each
d
Rx
ep esen s a
d
-
-dimensional obse a ion ec o a he ime s ep
. In o de o
cap u e he local pa e n and global s uc u e o he da a, a
sliding window mechanism is used o di ide he sequence in o
subsequence agmen s
},...,,{ 11
liii xxxS
o leng h
l
,
which a e used as model inpu o ea u e modeling. The
p ocess can be o malized as:
1,...,2,1],:[ lTiliixSi
Each subsequence is hen mapped o a low-dimensional
embedding space h ough he encode
)(
o ob ain he
la en ep esen a ion
m
ii RS z )(
. In o de o pe o m
unsupe ised con as i e lea ning, a posi i e and nega i e
sample pai gene a ion s a egy needs o be designed. Fo each
o iginal subsequence
i
S
, a posi i e sample iew
)( ii STS
is cons uc ed h ough he da a enhancemen
unc ion
)(T
, and nega i e samples
)( ijS j
a e
andomly selec ed om o he subsequences. The enhanced
subsequences a e inpu in o he encode espec i ely o ob ain
he posi i e sample ep esen a ion
)( ii S z
and he
nega i e sample ep esen a ion
)( ii S z
. Subsequen ly,
a con as loss unc ion based on empe a u e scaling is used o
op imize he seman ic dis ance ela ionship in he embedding
space. The loss unc ion is as ollows:
N
jji
ii
i zzsim
zzsim
L
1)/),(exp(
)/),(exp(
log
Among hem,
),( sim
ep esen s he cosine simila i y
unc ion, and
is he empe a u e pa ame e , which con ols
he sensi i i y o he simila i y dis ibu ion.
In he encode s uc u e design, a ime-sensi i e neu al
ne wo k module is used o model he dynamic e olu ion
cha ac e is ics wi hin he subsequence. Conside ing he
pe iodici y and local dis u bance cha ac e is ics o cloud
compu ing moni o ing da a, he model in oduces a ime-
sequence a en ion mechanism in he embedding space lea ning
o emphasize he key ime poin s. Suppose he inpu
subsequence is embedded as
],...,,[ 21
hhhH
, and each
m Rh
, hen he con ex is calcula ed h ough he a en ion
mechanism as:
l
l
kk
T
T
hz
hq
hq
1
1
,
)exp(
)exp(
Whe e
q
is a lea nable que y ec o ,
ep esen s he
impo ance weigh o he
h ime poin , and inally
z
agg ega es he key dynamic in o ma ion in he empo al
s uc u e.
A e comple ing he con as i e lea ning aining, he
model ob ains he po en ial ep esen a ion abili y o he inpu
sequence dis ibu ion s uc u e. In o de o pe o m anomaly
sco ing, his pape de ines anomaly measu emen unc ions
based on econs uc ion e o and embedding simila i y. Gi en
a subsequence
i
S
o be de ec ed, which is ep esen ed by
i
z
, and looking o he closes e e ence ep esen a ion
z
in he aining se , i s anomaly sco e is de ined as:
),(1)( ii zzsimzSco e
This sco e measu es he deg ee o de ia ion o he cu en
sample om he no mal mode in he embedding space, he eby
achie ing label- ee anomaly de ec ion. The en i e me hod
amewo k o ms a closed-loop s uc u e be ween da a
enhancemen , ea u e lea ning, and anomaly measu emen ,
wi h good adap abili y and obus ness, and is sui able o
complex, high-dimensional, and dynamic anomaly de ec ion
needs in cloud pla o m en i onmen s.
4. Da ase & Expe imen al Analysis
4.1 Da ase
This s udy uses he AWS CloudWa ch Da ase , which is
widely adop ed in eal-wo ld cloud compu ing en i onmen s,
as he expe imen al da a sou ce. The da ase consis s o
mul iple moni o ing me ics, including CPU u iliza ion,
ne wo k a ic, and disk ead/w i e a es. I co e s he
ope a ional s a es o i ual machines, con aine s, and se ice
laye s. The da a exhibi s high dynamism and mul i-
dimensional cha ac e is ics, e ec i ely e lec ing complex
beha io al pa e ns in cloud pla o ms.
The da ase is sampled a he minu e le el and spans
sys em ope a ion da a o e a ious pe iods. I shows clea
empo al s uc u es and pe iodic luc ua ions. Se e al
syn he ic anomalies a e injec ed in o he da a, such as se ice
conges ion, esou ce exhaus ion, and ne wo k in e up ion.
These a e sui able o alida ing he model's abili y o
dis inguish be ween no mal and abno mal pa e ns using
con as i e lea ning.
Conside ing he eal applica ion scena ios o cloud
pla o m da a, his da ase does no ely hea ily on labels,
which aligns well wi h he objec i es o unsupe ised anomaly
de ec ion asks. I s high dimensionali y, he e ogenei y, mul i-
scale p ope ies, and dynamic e olu ion a e closely ma ched
wi h he design o he p oposed model in e ms o inpu
s uc u e, augmen a ion s a egy, and empo al modeling. This
p o ides a s ong ounda ion o e alua ing he me hod's
applicabili y in eal sys ems.
4.2 Expe imen al Resul s
This pape i s conduc s a compa a i e expe imen , and he
expe imen al esul s a e shown in Table 1.
Table1: Compa a i e expe imen al esul s
Model
F1
Sco e
P ecision
Recall
AUC
KS Sco e
TimeConAD
(Ou s)
0.872
0.891
0.854
0.938
0.682
Anomaly
T ans o me [9
]
0.841
0.859
0.826
0.915
0.641
DONUT+[10]
0.773
0.799
0.748
0.875
0.578
GDN[11]
0.802
0.813
0.791
0.891
0.605
MO-
GAAL[12]
0.741
0.782
0.708
0.862
0.561
The expe imen al esul s show ha he p oposed
TimeConAD model ou pe o ms o he baseline me hods ac oss
mul iple e alua ion me ics. In pa icula , i achie es
signi ican ly highe sco es in F1 Sco e, P ecision, and Recall,
which a e co e indica o s o measu ing de ec ion accu acy and
co e age. This demons a es ha he me hod o e s s onge
disc imina ion and obus ness when dealing wi h he challenges
o dynamic changes, missing labels, and complex anomaly
ypes in cloud compu ing scena ios. By in oducing a
con as i e lea ning mechanism, TimeConAD can e ec i ely
lea n he unde lying s uc u al di e ences be ween no mal and
abno mal pa e ns unde unsupe ised condi ions, leading o
supe io anomaly de ec ion pe o mance.
Compa ed wi h mains eam me hods such as Anomaly
T ans o me and GDN, TimeConAD achie es mo e no able
imp o emen s in AUC and KS Sco e. AUC e lec s he model's
global de ec ion abili y, while KS Sco e measu es he
dis ibu ional di e ence be ween no mal and abno mal samples.
Bo h a e c i ical in anomaly de ec ion asks. TimeConAD
cons uc s subsequences using a sliding window, gene a es
mul i- iew inpu s h ough da a augmen a ion, and applies
empo al a en ion o ex ac key sequen ial ea u es. This
enables he model o cap u e sub le dis u bances and local
shi s in anomaly pa e ns, signi ican ly enhancing o e all
de ec ion e ec i eness.
Fo s uc u e-awa e models such as GDN and MO-GAAL,
al hough hey show ad an ages in s uc u al lea ning, hei
gene aliza ion abili y is limi ed when applied o high-
dimensional ime se ies da a wi hou labels. TimeConAD
add esses his issue by in eg a ing ep esen a ion alignmen and
anomaly dis ibu ion modeling h ough unsupe ised
con as i e lea ning. I lea ns high-quali y sequence
embeddings wi hou elying on labeled da a. This mechanism is
pa icula ly sui able o moni o ing da a in cloud pla o ms ha
a e equen ly upda ed and lack accu a e anno a ions, showing
s ong adap abili y o eal-wo ld scena ios.
This pape also expe imen s on he sensi i i y o da a
enhancemen s eng h o con as i e lea ning e ec s. The
expe imen al esul s a e shown in Figu e 2.
Figu e 2. Sensi i i y analysis o da a augmen a ion in ensi y on
con as i e lea ning e ec s
The expe imen al esul s show ha da a augmen a ion
s a egies wi h a ying s eng hs ha e a signi ican impac on
he pe o mance o con as i e lea ning in anomaly de ec ion
asks. As he augmen a ion s eng h inc eases om weak o
s ong, he model's pe o mance in he F1 Sco e, AUC, and KS
Sco e ini ially imp o es and hen sligh ly declines. This
sugges s ha mode a e pe u ba ions help he model lea n mo e
disc imina i e ea u es be ween no mal and abno mal s a es. In
pa icula , he model pe o ms bes when he augmen a ion
s eng h is se o “S ong,” wi h all e alua ion me ics eaching
hei highes le els. This indica es ha he posi i e sample
iews gene a ed a his s age e ec i ely guide he embedding
space o lea n mo e dis inguishable ep esen a ions.
The imp o emen s in AUC and KS Sco e indica e ha
augmen a ion no only inc eases he dis ance be ween di e en
classes in he ep esen a ion space bu also enhances he
model's sensi i i y o anomaly bounda ies. This is especially
impo an in cloud pla o m moni o ing da a, whe e no mal
luc ua ions occu equen ly. P ope augmen a ion helps he
model dis inguish be ween noisy no mal samples and mildly
abno mal ones, he eby imp o ing de ec ion accu acy and
obus ness. In con as , o e ly s ong augmen a ion may dis o
key ea u es o he o iginal sequence, esul ing in
ep esen a ion shi s and a sligh d op in de ec ion pe o mance.
The changes in he KS Sco e e eal ha augmen a ion
s eng h di ec ly a ec s he sepa abili y o posi i e and
nega i e sample dis ibu ions. S onge augmen a ion
ein o ces he consis ency among posi i e samples and
inc eases he dis ibu ion gap om nega i e samples. This
imp o es he model's disc imina i e powe du ing dis ibu ion
lea ning. Howe e , when he augmen a ion exceeds a
easonable h eshold, i may dis up he o iginal empo al
s uc u e and weaken he abili y o con as i e loss o en o ce
seman ic bounda ies. This can educe he p ecision o anomaly
de ec ion.
This pape also conduc s compa a i e expe imen s on he
impac o sample iew gene a ion s a egies on obus ness. The
expe imen al esul s a e shown in Figu e 3.
Figu e 3. Analysis o he impac o sample iew gene a ion
s a egy on obus ness
The expe imen al esul s indica e ha di e en sample iew
gene a ion s a egies ha e a signi ican impac on model
obus ness in unsupe ised con as i e lea ning. Among hese
s a egies, Masking and Combined augmen a ion achie e he
bes pe o mance, especially in he F1 Sco e and KS Sco e,
which e lec anomaly de ec ion accu acy and sample
dis ibu ion sepa abili y. This sugges s ha masking local
empo al segmen s o applying mul iple pe u ba ions helps he
model be e cap u e sub le di e ences be ween no mal and
abno mal s a es, enhancing gene aliza ion in high-dimensional
ime se ies da a.
The imp o emen in AUC shows ha he Masking s a egy
guides he model o ocus on key s uc u es and long- e m
dependencies. I inc eases he di e si y o posi i e samples
while p ese ing hei seman ic in eg i y. This is especially
impo an o moni o ing da a in cloud pla o ms, which o en
con ain pe iodic luc ua ions and local anomalies. In con as ,
s a egies such as C opping and Pe mu a ion in oduce hea y
dis u bances o empo al s uc u es. This may cause he loss o
ea u e in o ma ion and weaken he model's abili y o iden i y
bounda y cases, educing o e all obus ness.
Ji e ing achie es mode a e pe o mance in he F1 Sco e
and AUC, bu i s KS Sco e is ela i ely low. This indica es ha
al hough i enhances inpu di e si y, i has a limi ed abili y o
inc ease he dis ibu ion gap be ween posi i e and nega i e
samples. As a esul , i s uggles o o m clea anomaly
bounda ies. Pe mu a ion, which dis up s he o iginal ime o de ,
in oduces s ong pe u ba ions bu b eaks causal ela ionships
in he sequence. This leads o weake ep esen a ion quali y in
he embedding space and deg ades pe o mance in ime-
dependen anomaly de ec ion asks.
This pape also analyzes he impac o he numbe o
empo al a en ion laye s on sequence modeling capabili ies.
The expe imen al esul s a e shown in Figu e 4.
Figu e 4. A en ion Laye Dep h E alua ion
The expe imen al esul s show ha he numbe o empo al
a en ion laye s has a clea impac on he model's abili y o
cap u e sequen ial pa e ns. When he a en ion mechanism is
se o h ee laye s, he model achie es he bes pe o mance in
F1 Sco e, AUC, and KS Sco e. This indica es ha a mode a e
inc ease in a en ion dep h enhances he model's pe cep ion o
complex empo al s uc u es. I allows he model o be e
cap u e long- e m dependencies and sub le luc ua ions in
moni o ing da a, imp o ing i s abili y o dis inguish abno mal
beha io s. In cloud pla o m en i onmen s, sys em s a es o en
show nonlinea dynamics. Th ee laye s o a en ion s ike a
good balance be ween ep esen a ion capaci y and he isk o
o e i ing.
When he numbe o a en ion laye s is small, such as only
one laye , he model's abili y o cap u e empo al dependencies
is limi ed. I canno ully model mul i-scale ime pa e ns,
esul ing in o e all lowe pe o mance. In pa icula , he KS
Sco e e eals he model's di icul y in es ablishing clea
bounda ies be ween no mal and abno mal sample dis ibu ions.
This can lead o unce ain anomaly judgmen s in high-
equency cloud scena ios, educing he sys em's
esponsi eness o po en ial isks.
As he numbe o laye s inc eases o ou and i e, model
pe o mance begins o decline. Al hough deepe a en ion
s uc u es can heo e ically lea n mo e complex ep esen a ions,
he lack o supe ision in unsupe ised asks may cause
aining ins abili y. This inc eases he isk o o e i ing. In
anomaly de ec ion wi hou labels, deepe a en ion s acking
may cause he model o ocus mo e on local noise a he han
key pa e ns. This ha ms he quali y o ep esen a ions and
educes gene aliza ion.
The o e all end shows ha empo al a en ion
mechanisms play an impo an ole in modeling dynamic
sequences. Howe e , he numbe o laye s should be ca e ully
balanced based on he ask cha ac e is ics. Fo high-
dimensional, dynamic, and weakly s uc u ed moni o ing da a
in cloud compu ing, using h ee a en ion laye s p o ides a
good ade-o be ween exp essi e powe and aining s abili y.
This suppo s mo e obus seman ic lea ning wi hin he
con as i e lea ning amewo k.
Finally, his s udy in es iga es he e ec o lea ning a e
se ings on he s abili y o he aining p ocess, as shown in
Figu e 5.
Figu e 5.The impac o lea ning a e se ing on he s abili y
o he aining p ocess
The expe imen al esul s show ha he lea ning a e has a
signi ican impac on bo h aining s abili y and inal model
pe o mance. When he lea ning a e is se o 0.001, he model
achie es peak alues in F1 Sco e, AUC, and KS Sco e. This
indica es ha his se ing allows e ec i e g adien p opaga ion
while a oiding oscilla ion o ailed con e gence caused by
la ge s ep sizes. These esul s con i m ha a mode a e lea ning
a e helps he embedding space o m g adually in an
unsupe ised con as i e lea ning amewo k, which enhances
he model's abili y o dis inguish anomalies.
When he lea ning a e is oo low, such as 0.0001, he
model emains s able bu con e ges slowly. This esul s in
weak disc imina i e powe o he lea ned embeddings. In high-
dimensional and dynamic moni o ing da a, a low lea ning a e
may ail o cap u e sub le anomaly pa e ns in ime, leading o
poo bounda y cons uc ion. This e ec is especially isible in
he KS Sco e, which sugges s he model canno e ec i ely
sepa a e he dis ibu ion o posi i e and nega i e samples.
In con as , a high lea ning a e, such as 0.01, speeds up
ea ly aining bu o en causes ins abili y in he loss landscape.
This makes he op imiza ion pa h uns able. In con as i e
lea ning, such ins abili y can blu he ep esen a ions o
posi i e and nega i e samples, dis up ing he s uc u e o he
embedding space. As a esul , he model's abili y o de ec
abno mal sequences declines. Bo h he F1 Sco e and AUC
show a downwa d end in he expe imen s, con i ming ha an
excessi ely high lea ning a e damages he model's obus ness
and weakens i s applicabili y o eal-wo ld cloud da a.
Pe o mance analysis unde di e en lea ning a e se ings
e eals ha he lea ning a e di ec ly in luences how he
embedding space is shaped du ing aining. Fo high- equency,
spa se-dis ibu ion moni o ing da a in cloud en i onmen s,
choosing a lea ning a e ha ensu es bo h aining s abili y and
e ec i e con as i e ea u e ex ac ion is key o achie ing
e icien con e gence and accu a e unsupe ised anomaly
de ec ion.
5. Conclusion
This s udy add esses key challenges in anomaly de ec ion
wi hin cloud compu ing en i onmen s by p oposing an
unsupe ised me hod based on con as i e lea ning. The
app oach in eg a es sliding window segmen a ion, di e si ied
da a augmen a ion, and a empo al a en ion encoding module.
I e ec i ely handles he modeling o high-dimensional,
he e ogeneous, and unlabeled moni o ing da a. By cons uc ing
posi i e and nega i e sample pai s o guide ea u e lea ning, he
model can au oma ically acqui e disc imina i e ep esen a ions
unde unsupe ised condi ions, enabling accu a e de ec ion o
complex anomalous beha io s. Expe imen al esul s
demons a e s ong pe o mance ac oss mul iple key me ics,
con i ming he p ac icali y and obus ness o he p oposed
amewo k in eal-wo ld cloud scena ios.
S a ing om he goal o ensu ing sys em s abili y, he
s udy designs an anomaly de ec ion amewo k wi h s uc u al
gene aliza ion and empo al modeling capabili ies. I pe o ms
well in en i onmen s wi h equen changes and limi ed labeled
da a. The model does no ely on p ede ined ules o manual
ea u e ex ac ion, making i applicable o a ious ypes o
moni o ing da a, including se ice me ics, esou ce usage, and
ne wo k condi ions. This p o ides a p ac ical echnical pa h o
in elligen ale ing and anomaly diagnosis in eal ope a ions.
The in oduc ion o con as i e lea ning u he enhances he
model's ans e abili y, suppo ing c oss-pla o m and c oss-
scena io deploymen . This adds lexibili y and e iciency o he
de elopmen and scaling o anomaly de ec ion algo i hms.
The p oposed me hod has b oad applica ion po en ial in he
cloud compu ing domain. I can suppo esou ce scheduling a
he in as uc u e le el and ex end o beha io modeling,
secu i y analysis, and pe o mance bo leneck iden i ica ion in
mic ose ice a chi ec u es. In eme ging a chi ec u es such as
edge compu ing, con aine o ches a ion, and se e less
compu ing, sys em beha io s a e mo e complex, and anomaly
pa e ns a e mo e di e se. The con as i e lea ning model
p oposed he e shows s ong s uc u al compa ibili y and da a
adap abili y. I p o ides heo e ical suppo and enginee ing
ounda ions o building u u e au onomous ope a ions and
in elligen moni o ing sys ems.
Fu u e esea ch may u he explo e he me hod's scalabili y
in ede a ed en i onmen s, mul i-sou ce pla o ms, and online
lea ning scena ios. Fo example, in eg a ing inc emen al
ep esen a ion upda es, g aph-s uc u ed in o ma ion, o mul i-
ask lea ning modules could imp o e adap abili y and mul i-
objec i e ecogni ion. Inco po a ing seman ic modeling
echniques such as la ge language models in o he anomaly
de ec ion pipeline may enhance he model's abili y o
unde s and complex sys em beha io s. This could p omo e he
e olu ion om s a ic anomaly de ec ion o causal analysis and
in elligen decision-making, suppo ing he con inued
de elopmen o cloud compu ing and in elligen ope a ions.
Re e ences
[1] Xu, H., e al. (2022). "CUTS: Con as i e Unsupe ised Time Se ies
Rep esen a ion Lea ning ia Time-Based Nega i e Sampling."
P oceedings o Neu IPS 2022.
[2] Zhou, H., e al. (2021). "In o me : Beyond E icien T ans o me o
Long Sequence Time-Se ies Fo ecas ing." P oceedings o AAAI 2021.
[3] Eldele, E., e al. (2021). "Time-Se ies Rep esen a ion Lea ning ia
Tempo al and Con ex ual Con as ing." IJCAI 2021.
[4] Lap e , N., e al. (2015). "Gene ic and Scalable F amewo k o
Au oma ed Time-se ies Anomaly De ec ion." P oceedings o KDD 2015.
[5] Liu, J., e al. (2023).Mul i-scale Associa ion Disc epancy o Time-Se ies
Anomaly De ec ion.P oceedings o he 29 h ACM SIGKDD Con e ence
(KDD 2023).
[6] Yamaguchi, Y., e al. (2021).Imp o ing Va ia ional Au oencode s o
Unsupe ised Anomaly De ec ion in Time Se ies.Pa e n Recogni ion
Le e s, Volume 147.
[7] Zhang, H., e al. (2022).Robus G aph-based Anomaly De ec ion in
Mul i a ia e Time Se ies.IEEE T ansac ions on Neu al Ne wo ks and
Lea ning Sys ems (TNNLS).
[8] Chen, C., e al. (2021).Imp o ed Gene a i e Ad e sa ial Ne wo ks o
Unsupe ised Anomaly De ec ion in Time Se ies.Knowledge-Based
Sys ems, Volume 229.
[9] Xu, Y., e al. (2022).Anomaly T ans o me : Time Se ies Anomaly
De ec ion wi h Associa ion Disc epancy.In e na ional Con e ence on
Lea ning Rep esen a ions (ICLR 2022).
[10] Ren, H., e al. (2019).Time-Se ies Anomaly De ec ion Se ice a
Mic oso .P oceedings o he 25 h ACM SIGKDD Con e ence (KDD
2019).
[11] Deng, A., e al. (2021).G aph Neu al Ne wo k-Based Anomaly De ec ion
in Mul i a ia e Time Se ies.P oceedings o he AAAI Con e ence on
A i icial In elligence (AAAI 2021).
[12] Liu, Y., e al. (2020).Gene a i e Ad e sa ial Ac i e Lea ning o
Unsupe ised Ou lie De ec ion.IEEE T ansac ions on Neu al Ne wo ks
and Lea ning Sys ems (TNNLS).
