Towards Network-Aware Composition of Big Data Services in the Cloud

Transcription

1 (IJACSA) Iteratoal Joural of Advaced Computer Scece ad Applcatos, Towards Network-Aware Composto of Bg Data Servces the Cloud Umar SHEHU Departmet of Computer Scece ad Techology Uversty of Bedfordshre Luto, UK Ghazafar SAFDAR Departmet of Computer Scece ad Techology Uversty of Bedfordshre Luto, UK Gregory EPIPHANIOU Departmet of Computer Scece ad Techology Uversty of Bedfordshre Luto, UK Abstract Several Bg data servces have bee developed o the cloud to meet creasgly complex eeds of users. Most tmes a sgle Bg data servce may ot be capable satsfyg user requests. As a result, t has become ecessary to aggregate servces from dfferet Bg data provders together order to execute the user's request. Ths tur has posed a great challege; how to optmally compose servces from a gve set of Bg data provders wthout affectg f ot optmzg Qualty of Servce (QoS). Wth the advet of cloud-based Bg data applcatos composed of servces spread across dfferet etwork evromets, QoS of the etwork has become mportat determg the true performace of composte servces. However curret studes fal to cosder the mpact of QoS of etwork o composte servce selecto. Therefore a ovel etwork-aware geetc algorthm s proposed to perform composto of Bg data servces the cloud. The algorthm adopts a exteded QoS model whch separates QoS of etwork from servce QoS. It also uses a ovel etwork coordate system fdg composte servces that have low etwork latecy wthout compromsg servce QoS. Results of evaluato dcate that the proposed approach fds low latecy ad QoS-optmal compostos whe compared wth curret approaches. Keywords Bg data; Servce composto; QoS; Geetc Algorthm; Network latecy; Cloud I. INTRODUCTION Servce Oreted Computg (SOC) s a framework that allows for teret applcatos to be bult by couplg web servces together. I SOC, each web servce represets a dfferet fuctoal aspect of a Servce-oreted applcato (SOA) [3]. Web servces are etwork-accessble objects that allow Bg data vedors to buld servce-oreted Bg data applcatos (SOA) whch share busess logc ad applcato servces wth other vedors order to meet growg cosumer eeds. A Bg data servce (BDS), also kow as Bg-data-as-a-servce (BDaaS), s a data tesve web servce that works o large scale ustructured or sem-structured datasets. They typcally perform tasks such as data storage, processg, cleag, extracto, modellg ad vrtualzato o large datasets. BDaaS cosst maly of three layers amely; the frastructure layer, platform layer ad applcato layer. The frastructure layer provsos the physcal resources requred to process large datasets. The platform layer houses the operatg systems ad vrtual maches that ru BDaaS applcatos. The applcato layer represets the models ad software used to process Bg data. Every BDS s characterzed by the ablty to provde some task as detfed by ts fuctoal ad o-fuctoal attrbutes [7]. The fuctoal attrbutes defe what the servce s capable of dog e.g. Mcrosoft Azure BDS [30] provdes cloud-based mache learg framework for aalyzg large scale datasets. No-fuctoal attrbutes o the other had determe how well a servce ca perform a gve task e.g. how log t wll take Mcrosoft Azure BDS to respod to a user request. No-fuctoal attrbutes are commoly referred to as QoS (Qualty of Servce). Examples of servce QoS attrbutes clude respose tme, cost, reputato, etc. They are ofte used as crtera selectg servces sutable for a user request especally stuato where there s more tha oe servce wth smlar fuctoalty. For stace, a Mcrosoft Azure BDS havg respose tme equal to 0 secods wll be more sutable to a user requrg prompt servce respose tha a smlar servce such as Amazo AWS BDS wth a respose tme of 30 secods. Thus servce QoS s used to dfferetate servces that are smlar terms of ther fuctoaltes. I SOC, fuctoally smlar servces are usually categorzed the same servce group ad referred to as caddate servces. A. QoS-Aware Web Servce Composto Recetly, the ablty of servces to aggregate ther fuctoaltes has gaed much atteto. Ths s as a result of creased complexty of user requests. Smple user requests may requre oly a sgle BDS to be completed. However, as user requests take more complex forms that are beyod the capabltes of a sgle BDS, aggregatg servce abltes s ecessary to carry out the request. Ths process s kow as servce composto. It combes servces order to buld a composte servce [8, 9] that s vewed by the user as a sgle servce. Wth a composte servce each costtuet BDS takes care of a specfc fuctoal aspect of the user s request. For stace suppose a user ssues a compoud request lke "Aalyse e-books dataset" cosstg of several sub requests at the task level such as Twtter feed aalyss, Natural laguage processg ad IOT devce log aalyss (as see Fg. ). A sgle BDS s ll suffcet to satsfy the compoud request, therefore servces from dfferet Bg data vedors for each sub request wll eed to be dscovered ad aggregated together accordg to ther QoS to complete the user request. QoSaware servce composto process s smlar prcple to the behavour of workflow maagemet system [8] whch a workflow dctates how data should be processes. A workflow processes data by usg dfferet patters that 7 P a g e

2 (IJACSA) Iteratoal Joural of Advaced Computer Scece ad Applcatos, trasform data to a ed result. Servce composto uses smlar patters foud workflow systems. The patters allow composto process to chael data flow from oe BDS to aother. Some major servce composto patters clude sequece, parallel, exclusve choce ad loop. Servce composto process begs by breakg dow a complex request to smaller fuctos or sub-requests orgazed accordg to oe of several patters. Depedg o the patter volved, servce QoS are the orchestrated to determe QoS for a composte servce. Usually there are several caddate servces that exst wth a servce group that ca execute a gve fuctoal aspect of the request. Servce Group A SA SA Twtter feed Aalyss Sub- RequestA Cloud Servce Group B SB SB Natural laguage processg Sub RequestB Servce Group C SC SC IOT devce log aalyss Sub RequestC BDS Level Fuctoalty Level Task Level be able to delver o the etwork performace eeds of the user eve f t has optmal servce QoS. Ths s because the optmal servce QoS oly represets applcato level performace of a composte servce but t does ot accout for ts etwork performace. The mpact of the etwork s usually quatfed usg a metrc such as etwork latecy []. The effect of etwork latecy o applcato performace s otceable cloud evromets where there s hgh degree of servce dstrbuto. Despte ths, curret studes do ot separate QoS of etwork from servce QoS. Hece, they may produce compostos that have sub-optmal performace whe voked by the user. A example s llustrated Fg.. The example shows several BDS deployed o dfferet clouds. Assumg each cloud cossts of two or more BDS ad s separated from other clouds by dfferet roud trp tmes (RTT). Also assumg a user request cossts of a sequece patter of the three tasks (t, t, ad t 3 ) Fg., wth each task havg a set of caddate servces ad ther respectve QoS scores for cost (P), respose tme (RT) ad executo tme (ET). Curret approaches wll ordarly pck the QoS optmal composte servce (hghlghted usg bold boxes Fg. 3) cosstg of servces ( S A -S B -S C ) wth respect to cost, executo tme ad respose tme. Compoud Request Aalyze Ebook dataset Fg.. Typcal BDS composto scearo Therefore choosg a servce from each servce group that maxmzes the QoS of composte servce whle satsfyg the user s costrats has become a research problem. The problem s also kow as a NP-Hard optmzato problem [8]. The problem has bee solved usg several techques such as Lear Iteger Programmg [3] ad Dyamc Programmg []. Although techques based o geetc algorthms [8] are usually used fdg ear optmal compostos polyomal tme. B. Servce Composto the Cloud More ad more BDS are creasgly beg deployed o the cloud wth the purpose of allowg Iteret users from aroud the globe to access ther fuctoaltes for aalysg large datasets. For stace orgazatos such as Amazo ad Mcrosoft offer publc cloud servces usg Amazo Web Servces (AWS) [3] ad Wdows Azure [30] cloud platforms respectvely. These servces are deployed o cloud data cetres va vrtual maches (VM) where cosumers ca access them from lterally ay part of the world. VMs eable the processg resources such as CPU, storage ad etwork resources eeded to properly ru cloud-based BDS. Tradtoally, servce provders deploy ther VMs across several cloud data cetres located dfferet geographcal areas to host ther BDS. Hece, each user wll experece dfferet etwork performaces depedg o the geographcal locato of the hosted servce. Thus, whe a user tres to voke a composte servce wth caddate servces spag dfferet cloud locatos, the composto may ot Cloud SC SA3 Cloud locato Web Servce User RTT dstace SB3 User A 54ms Fg.. BDS deploymet locatos SA Cloud Cloud 3 00ms SB SA SC SC3 SB User B I dog so, users may experece dfferet levels of performace for ths optmal soluto depedg o the RTT betwee clouds of partcpatg servces. t t t 3 P RT ET 4 S 6s0s P RT ET ET 6 A S 9s9s RT P 9s 7s B S C S 8 4s3s A S 8 7s39s B S 5 s5s C 8s S 5 9s A3 S 4 4s0s B3 S 3 s4s C3 Fg. 3. Sequece workflow patter wth servces ad ther QoS scores BDS havg shorter RTT wll cur lower latecy tha those further away from each other. Therefore user A may experece low etwork latecy for composte servce S A - S B -S C (.e. ed-to-ed etwork latecy for S A -S B -S C s 400ms + 00ms + 54ms + 500ms = 054ms), whle user B expereces hgh etwork latecy because of larger RTT (.e. 8 P a g e

3 (IJACSA) Iteratoal Joural of Advaced Computer Scece ad Applcatos, 500ms + 00ms + 54ms ms = 3654ms). Perhaps smlar composte servces lke S A -S B -S C (3087ms) or S A -S B -S C3 (3ms) may be better suted for user B sce they have lower etwork latecy (as see Fg. 3.). Ths work dffers from curret approaches that t separates QoS of etwork from servce QoS. Itegratg etwork latecy property to the QoS model wll allow us to fd composto who s QoS ot oly optmal at the applcato level, but also has earoptmal QoS of etwork from the user s perspectve. I ths paper, a etwork aware approach to servce composto whch optmzes etwork latecy ad servce QoS objectves such as cost, respose tme ad executo tme s proposed. It cossts of a ovel etwork model whch frst estmates etwork latecy betwee BDS the cloud. Estmato s ecessary as tradtoal latecy measuremet methods whch volve dstrbuto of RTT pgs to drectly measure RTT betwee servces are geerally slow ad computatoally expesve [4, 6]. Iformato from the etwork model s fed to a ovel etwork-aware composto techque based o geetc algorthm order to fd solutos that have optmal servce QoS wthout compromsg QoS of the etwork. The paper s orgazed as follows: I Secto II a aalyss of recet research efforts s preseted after whch the proposed approach s descrbed Secto III. Secto IV presets a dscusso of evaluato results. Fally, Secto V cocludes the paper. II. RELATED WORK A. QoS-Based Servce Composto QoS-aware servce composto problem has bee modelled as a NP-Hard problem [4]. Several classes of approaches have bee developed to address the problem. Earler studes devsed local optmzato methods to fdg optmal composte servces. These methods employ search techques to fd servces local to each subtask, the combes them to a composte servce that wll complete the user s request. Techques developed clude dyamc programmg [], learg depth frst search [0] ad smple addtve weghg methods []. Aother class of approaches wdely used are lear teger programmg techques [3, 4]. These techques use teger varables to search for optmal solutos wthout havg to costruct all possble combatos. Meta-heurstc (MH) approaches have bee developed to tackle the NP-hard problem. These approaches are based o evolutoary cocepts ature. Some major MH approaches clude Geetc [, ], partcle swarm optmzato methods [5] ad artfcal mmue algorthms [9]. All these classes of approaches use smlar QoS model whch does ot take QoS of etwork to cosderato. I comparso, the etwork-aware geetc algorthm corporates QoS of the etwork the QoS model as t tackles the NP- Hard problem. Geetc algorthm has bee chose because t shows great promse solvg costraed mult-objectve optmzato. It s also capable of producg a set of solutos whch o soluto s domat to the others, thereby gvg the user a wde rage of ear-optmal solutos to choose from. B. Network-Aware Servce Composto Several studes have dealt wth servce composto whle cosderg the mpact of QoS of the etwork. Authors [3] preset a etwork-based servce composto techque for compoet servces large scale overlay etworks. Smlarly, aother study [4] troduces etwork awareess composg doma servces mult-doma etworks. The authors try to optmze delay ad avalable badwdth. However, these studes do ot cosder servce composto the cotext of servces the cloud. A recet approach [] develop a servce composto techque that mmzes etwork latecy of composte servces the Cloud. The authors use a etwork model based o Eucldea dstace techque to estmate latecy of composte servces. Ther work s smlar wth ths study that they cosder etwork latecy. The ma dfferece s that they oly cosder QoS of etwork whle ths work cosders QoS of etwork alogsde servce QoS objectves. C. Network Coordate System Network coordate systems (NCS) are used to estmate latecy betwee odes a etwork []. Ther purpose s to reduce the delay observed from sedg physcal roud trp tme (RTT) packets betwee odes across the etwork path. They operate by predctg RTT measuremets for a fracto of odes o the etwork path usg techques such Eucldea dstace estmato (EDE) [5, 6, 7] ad matrx factorzato (MF) []. EDE embed etwork dstaces betwee odes as metrc spaces where kow etwork dstaces (RTT) are mapped to a two dmesoal Eucldea space order to predct ukow etwork dstaces. EDE s however susceptble to tragle equalty [] whch leads to accurate estmates. MF o the other had estmate umeasured etwork dstaces by factorzg dstace matrx cosstg of both kow ad ukow RTT values usg mathematcal cocepts such as gradet descet [9]. MF does ot use metrc spaces ad so s resstat to tragle equalty ad produces more accurate tha EDE. Curret EDE ad MF models adopt a cetralzed approach towards RTT estmato. The approach usually volves usg a cetral server to collectvely predct RTT values for all the odes the etwork path. Ths meas that f oe RTT value s accurately estmated, the the accuracy of other RTT values could be egatvely affected. I ths work, the problem s avoded by adoptg a ovel decetralzed MF approach wth the etwork model where each BDS takes charge of predctg RTT wth ts eghbourg servces depedetly of other servces o the cloud etwork. III. PROPOSED APPROACH A. Problem Formulato The problem ca be descrbed as follows: t, Gve a user request T that wll requre a set of tasks t to t T, t,, t, Where s the umber of tasks to complete user request. 9 P a g e

4 (IJACSA) Iteratoal Joural of Advaced Computer Scece ad Applcatos, Each task s assged a servce group (S) whch defes a set of caddate servces ( s j ) capable of performg the gve task (as see Fg. 4.), S s, s, s, k,.., j..k Where k s the umber of caddate servces the -th servce group. For each task, oly oe caddate servce wth ts servce group ca be boud to the task t to form a composte servce C. t t t s s s s s s S s 3 S s 3 s 3 S s k s k s k Fg. 4. Classfcato of caddate servces to servce groups ad tasks A composte servce ca be formed from the aggregato of caddate servces; s s, s, j..k,.. C, j, j j Where sj s the BDS boud to ts servce group S. Also, gve a set of QoS objectves (cost, executo tme, respose tme ad etwork latecy) that eed to be optmzed, the ed-to-ed QoS value of a composte servce ( Q (C) ) s calculated by combg dvdual QoS values of ts servces (oe per task) based o the followg expressos. I order to determe ed-to-ed cost of composte servce, cost for each servce ( P s ) ) are combed ( j P s j Q ( C) P( ) () Smlarly, both ed-to-ed respose tme ( RT s ) ) ad ( j executo tme ( ET s ) ) are aggregated respectvely Q Q ( j ( C) RT ( ) (), RT s j ( C) ET ( ) (3) ET s j As for ed-to-ed etwork latecy, RTT values are combed betwee each servce a gve composte servce Q NL ( C) NL( s (4), j, s, j ) Where NL s, s ) represets the roud trp tme (, j, j betwee each BDS the cloud. Q P, Q RT, Q ET ad Q NL represet ed-to-ed cost, respose tme, executo tme ad etwork latecy of a composte servce respectvely. Gve weghts w P, w RT, w ET ad wnl whch represet relatve mportace of QoS objectves from the user's perspectve. Where, 4 wm (5) m QoS objectves are ormalzed to ftess values usg the expressos Equatos (6) ad (7). Cost, respose tme ad executo tme are computed thus Maxm (S ) - Qm (s j ) Fm( C) wm (6) Maxm (S ) Mm (S ) m P, RT, ET j..k Network latecy ftess value for composte servce ( F NL ) s determed by a expresso Equato (6) whch ormalzes the ed-to-ed etwork latecy QoS ( Q ). QNL ( ) ( C F ) NL C w (7) NL H Where H s a costat whch ormalzes value of Q ( ) NL C the rage of [0 ]. The research problem becomes a costraed multobjectve optmzato problem where the am s to fd a set of composte servces wth ear-optmal ftess values, Subject to: Cbest M F( C) Selecto costrat: Oly oe caddate servce ca be selected per servce group. m Mmum ( q m ) ad maxmum ( q costrats: m max Qp qp qp NL max m ) QoS m max Q RT qrt q RT,, m max QET qet q ET B. Network Model I ths study, a etwork model for estmatg the RTT betwee BDS deployed o the cloud s adopted. The etwork model s composed of etwork coordate system (NCS) based o Matrx factorzato called LADMF (Learg-based Decetralzed Matrx Factorzato). Tradtoal MF techques measure RTT ( ) betwee a BDS ad a ds j sk subset of eghbors to buld dstace matrx D. These 0 P a g e

5 (IJACSA) Iteratoal Joural of Advaced Computer Scece ad Applcatos, measuremets are the used to predct RTT values ( d * s j s k ) for o-eghborg servces as see Fg. 5. I mathematcal terms, stadard MF fds estmates of row matrx X ad trasposed colum matrx Y that mmze the dfferece ( ) betwee measured RTT values D ad estmated values matrx D ew, s s 3 s k m (8) Where s the latecy predcto error; d S-S d S-S d S-S d S3-S... s s s k d S-Sk d S-Sk.. d Sk-S. RTT Matrx (D) before estmato d Sk-S D D ew ( ) (9) s s s k X = x... x k Trasformato of RTT to postoal coordates * Y j T = Measured RTT betwee d Sj-Sk servces Sj ad Sk RTT to be estmated betwee d Sj-Sk servces Sj ad Sk k-th servce s k Servce group Predcted RTT betwee d* Sj-Sk servces Sj ad Sk s y d s S-S d* S-Sk y s 3 s 3 d* S3-S. Fg. 5. Network dstace estmato usg matrx factorzato Also D ew s expressed as; D X Y y k s k s k s s k.. d Sk-S RTT Matrx (D ew ) after estmato T ew (0) Where X ad Y are postoal coordates of all BDS o a gve cloud etwork. the stadard MF techque s modfed by addg learg automata cocepts order to further mprove predcto accuracy of the estmato process. Istead of costructg a collectve matrx (D ew ) for all RTT estmates, LADMF decetralzes the process by allowg each BDS to estmate ts ow RTT values rrespectve of other servces. Ths s acheved by ecodg each servce as a learg automato (LA) [5]. LA coverts Equatos (0) ad (9) to Equatos () ad () respectvely, Where D X Y () T j j ew D j Dj ew X s postoal coordate of -th servce, postoal coordate of j-th eghbourg servce, whle.s the RTT betwee servces ad j. () Yj s D The effect s that each BDS wll cotrol ther ow path to RTT estmato wthout fluecg estmato path of other servces. Hece a accurate estmato of oe servce coordate wll ot affect accuracy of other servce coordates. j I LADMF X ad Yj are ecoded wth addtoal LA parameters as see Fg. 6. X = x. x m α β P α Learg Automata parameters Y j T = j y j. y jm α β Pα Fg. 6. Ecodg of posto vectors wth LA parameters Where α represets two alteratve update strateges ( ad ) employed updatg posto coordates X ad Y j : Also X =D Y (Y Y +( +J )I), T - (ew) j j j j Y =D X (X X +( +J )I) T - j(ew) j X =D Y (Y Y +( -J )I), T - (ew) j j j j Y =D X (X X +( -J )I) T - j(ew) j (3) Ω - Regularzato parameter that cotrols speed of update J ad J are costats I - Idetty matrx β represets feedback for every acto α. β = {β α, β α } P α s acto probablty whch s determed from feedback of estmato error. If feedback for acto α s good (β α = 0.e. s mproved) the acto probablty P α s rewarded whle P α s pealzed, P ( ew) P c( P ) 0.5, c 0 e 0.005* c P ( ew) P e( P ) Else f feedback s bad (β α =.e. s ot mproved) the reverse s the case, P ( ) ( ) c 0.5, ew P c P (5) e 0.005* c P ( ew) P e( P ) Actos are evaluated ad assged probabltes based o error feedback whch ths case s the estmato error ( (4) P a g e

6 m ). The acto wth the hghest probablty s selected as the ext acto. The process s cotued utl the estmato error s mmzed. LADMF algorthm s outled Algorthm. Afterwards, estmated RTT values are aggregated to determe ed-to-ed etwork latecy for a composte servce va Equato (4). Algorthm LADMF Algorthm Iput: D, max_ter, L, Ouput: Dew : [X, Y] = fucto LADMF(D) : { for( =: maxiter) 3: for(j =: max caddate servce) 4: X rad(x) 5: Y rad(y) 6: 7: w [D (X * Y T )] f ( s mmsed) 8: Dew X * Y T 9: retur 0: edf : edfor : edfor 3: } C. Network-Aware Servce Composto Algorthm A ovel etwork-aware servce composto techque based o o-domated sort geetc algorthm (NSGA) s preseted. Whe applyg geetc algorthm to servce composto problem, each geome represets a possble composte servce ad s ecoded form of array of umbers or gees, each gee tur represets a task ad ca be assged to ay oe of ts caddate servces (as see Fg. 7). State of the art NSGA tates optmzato process by buldg a tal geerato of geomes the sorts dvduals accordg to ther ftess value ad crowdg dstace. The best dvduals are placed a matg pool where they are altered by crossover ad mutato operators to geerate chldre that wll populate subsequet geeratos. The whole process s repeated utl optmzato s reached. The state of the art NSGA algorthm s ehaced order to be able to solve research problem. The mproved algorthm called s descrbed step by step as follows: GENOME(Composte servce) {Task } Gee S S {Task } Gee S S {Task 3} Gee 3 S 3 S 3 {Task } Gee S S (IJACSA) Iteratoal Joural of Advaced Computer Scece ad Applcatos, S S S 3 33 S S S 3 S 3 S 3 S 33 Fg. 8. Example of a composte servce ecoded as teger array BDS QoS scores are the radomly talzed wth ther boudary costrats. Wth the ad of LADMF algorthm, the QoS scores are ormalzed ad aggregated to values represetatve of composte servce ed-to-ed cost, respose tme, executo tme ad etwork latecy respectvely. Step. Rakg ad Sortg. uses a odomated sortg techque that raks dvduals to dfferet frots accordg to the degree that they domate other dvduals the populato. A composte servce C perfectly domates aother composte servce C f all four ftess values of C are lower tha the ftess values of C. Therefore C wll be placed a hgher rak (frot) tha j j C j. For each frot, dvduals are sorted ascedg order accordg to the magtude of ther ftess. Ths s used to establsh the crowdg dstace (CD) whch dcates the Eucldea dstace betwee dvdual the ftess value space. CD for a gve composte servce C s expressed as; F( C CD( C ) F Where ) F( C ) max F m (6) CD( C ) s the crowdg dstace for the -th dvdual. F ( C ) represets the ftess value of dvdual succeedg -th dvdual. F ( C ) represets the ftess value of dvdual precedg -th dvdual S 3 S S 3 Fg. 7. Structure of composte servce geome S Step.. Italzato of Populato. starts by radomly geeratg a tal populato from the BDS that are part of the cloud. I order for ths to be acheved, every servce s frst ecoded as a two dgt teger value. For example Fg. 8, a BDS s ecoded as "33" s the 3rd caddate servce capable of executg task 3. I the ext step oly oe caddate servce s arbtrarly selected per task. S 33 S 3 S 3 S F max ad F m represet the maxmum ad mmum ftess values populato Step 3. Touramet Selecto. A touramet selecto of the best dvduals that meet the user s satsfacto costrat s acheved to determe parets who wll take part crossover operato. The selecto process esures that oly dvduals wth best ftess, rak ad do ot volate user costrat are selected for crossover operato. Step 4. Crossover Operato. Crossover operato combes ay two parets to offsprg (chldre) that are qute dfferet from ther parets ad ca have superor P a g e

7 (IJACSA) Iteratoal Joural of Advaced Computer Scece ad Applcatos, propertes of both parets. Tradtoal crossover operato pcks arbtrary cut pots where gees aroud cut pots of oe paret are replaced wth gees of aother paret to costruct a set of chldre. employs a ovel two-pot crossover whch cuts parets at two o-radom cut pots. The two cut pots (oe per paret) are chose from pots o each paret where average etwork latecy s hgh. I order to determe whch pot o a paret costtutes poor average latecy, every BDS assged a average latecy score ( A L ) whch s the arthmetc sum of RTT values over all outgog paths dvded by the umber of outgog paths from a gve servce, AL ( s) / G QNL ( g) (7) g G Where A L (s) represets average latecy score mllsecods (ms) for servce s, G s umber of outgog paths from s, ad Q NL (g) s RTT value for a gve path. Oce average latecy scores are kow, the crossover operator selects a cut pot from each paret where A L s maxmum. After the cut pots are kow the the gees aroud those pots are terchaged betwee both parets. Ths esures that gees havg hghest A are terchaged wth gees havg lower A L. Fg. 9 depcts how crossover operato s performed. (a) Before crossover operato Chld { 6} { 4} {3 6} {4 3} Chld { } { 6} {3 } {4 4} (b) After crossover operato {5 3} {6 4} {5 5} {6 } Fg. 9. 's two pot crossover operato whe cut pot ad cut pot are ot the same Whe cut pots ad are the same for both parets the the crossover operato traslates to a sgle pot crossover. The mpact of the crossover operator s that chldre produced are low latecy versos of ther parets as demostrated by the results. Step 5. Mutato Operato. The fucto of mutato operato s to adjust a paret to ew offsprg that closely resemble ts paret wth the am of further mprovg paret ftess values ad dscourage trappg to local optma. The stadard mutato operator adjusts parets by usg a uform dstrbuto dex (DI) [3]. DI cotrols degree of smlarty betwee parets ad ther chldre. The value for DI flueces the dversty of offsprgs the populato. A ew L mutato operato s preseted. The operator uses a varable dstrbuto dex whose value depeds o a paret's crowdg dstace ad ftess value for etwork latecy. Each paret s gog to be mutated accordg to the value of ts dstrbuto dex whch s computed usg the followg expresso: mum par Where H CD( par ) (8) FNL ( par ) ( CD( par )) mumpar s the dstrbuto dex for the paret. F ( par NL ) represets the paret's ftess value for etwork latecy. CD par ) dcates the paret's crowdg dstace. ( H s a costat. The expresso Equato (8) wll force a strog mutato for poor qualty parets ad a weak mutato for good qualty parets. A large value for mum wll dcate par paret has good ftess ad crowdg dstace therefore offsprg s gees wll closely resemble the paret (.e. weak mutato), whle a small value for mum dcates paret par has poor ftess ad crowdg dstace hece gees of offsprg wll dffer greatly wth the paret (.e. strog mutato). Ths wll ultmately mprove the populato dversty of ew offsprg ad also crease the lkelhood of fdg the global soluto. After mutato operato s performed, parets are replaced by ewly formed off sprgs ad the whole process s repeated utl maxmum umber of geerato s reached. algorthm s outled Algorthm whle the uque crossover ad mutato operators are outled Algorthm 3 ad 4 respectvely. Algorthm Algorthm Iput: D, g,,, h, max_ter, o_states, state, actos_prob, rp_ev, w, J, J, Ouput: pop : Set evromet parameters : pop Radomly geerate populato 3: P Radomly geerate QoS values of solutos 4: pop[q, f ] Determe ed-to-ed QoS ad ftess of solutos 5: pop Perform o-domated sort (pop) 6: pop LADMF (Iput) 7: Whle (ge maxge) 8: { 9: pop touramet selecto (pop) 0: pop Crossover (pop) : pop Perform o-domated sort (pop) : chld_pop Mutato (pop) 3: combato_pop pop + chld_pop 4: combato_pop Perform o domated sort (combato_pop) 5: pop replacemet (combato_pop) 6: edwhle 7: } 3 P a g e

8 Stadard devato Network latecy (ms) Ftess (IJACSA) Iteratoal Joural of Advaced Computer Scece ad Applcatos, Algorthm 3 Crossover operato Iput: pop Ouput: Chld : For( = to popsze) : { 3: Radomly pck Paret ad Paret from pop 4: Compute Average latecy A L of Paret ad Paret 5: dex Fd cut pot of Paret wth poorest latecy 6: dex Fd cut pot of Paret wth poorest latecy 7: [Chld, Chld ] Crossover gees for each paret aroud dex ad dex 8: [Chld, Chld ] Determe ed-to-ed QoS ad ftess of chldre 9: Chld Add Chld ad Chld the chld populato. 0: edfor : } Algorthm 4 Mutato operato Iput: pop Ouput: Chld : For( = to popsze) : { 3: Compute Dst. Idex of pop() accordg to Equato (8) 4: Chld() Mutate gees of pop() accordg to DI 5: Chld() Determe ed-to-ed QoS ad ftess of chld 6: edfor 7: } IV. EVALUATION A. Setup Expermets were ru o a mache wth Itel Core 7 CPU (3.8GHz) ad wth 8GB memory. All the algorthms ad expermets are mplemeted MATLAB 03. A cloud etwork of BDS s smulated usg plaet lab merda dataset [7] to provde RTT measuremets betwee BDS. The dataset s chose because t s expesve to mplemet a physcally large cloud evromet. The dataset cotas symmetrc roud trp tme (RTT) measuremets betwee 740 peer-topeer odes. Also, a test workflow s geerated ad wll be used to evaluate algorthm. I the workflow, a set of thrtee tasks (t to t 3 ) s defed. For each task, t s assume that each servce group has equal umber of caddate servces for the sake of smplcty. The expermet s performed wth 0 caddate servces per task to smulate a large BDS cloud etwork. B. Results ad Dscusso To demostrate the effcecy of, ts ftess latecy ad populato dversty are compared agast other tradtoal algorthms such as Partcle swarm optmzato () [6] ad Geetc algorthms [5] ad S- NSGA [4] dfferet evrometal stuatos such as varatos umber of tasks, caddate servces ad dstrbuto dex. Gve the probablstc ature of the test algorthms, each algorthm s ru 50 tmes to obta average values for ftess, latecy ad stadard devato whch s ofte used to measure dversty of populato. a) Impact of Dstrbuto Idex: I ths expermet, a evaluato s doe to determe the mpact of dstrbuto dex o average ftess ad populato dversty of composte servces. Here, the populato sze ad maxmum geerato are set as 00 wth etwork sze of 60 servces. I Fg. 0 (a) (b) ad (c), t s observed that fds solutos wth better ftess, latecy dversty tha ad 80. also avods trappg local optma whle covergg after 40 geeratos. Ths result shows that mprovemets ftess, latecy ad populato spread ca be attrbuted to the proposed mutato ad crossover operators Geerato (a) Graph dcatg effect of dstrbuto dex o ftess Geerato (b) Graph showg effect of dstrbuto dex o latecy Geeratos (c) Graph showg effect of dstrbuto dex o populato dversty Fg. 0. Plot of Dstrbuto dex agast ftess, latecy ad dversty of populato b) Sze of Caddate Servce per Task I ths expermet, the umber of caddate servces per task s creased from 0 to 50 ad evaluate the mpact o etwork latecy, ftess, computato tme ad stadard devato of populato. I Fg. (a) ad (b), t s otced that a crease sze of caddate servces may ultmately lead to better qualty solutos for all test algorthms wth the excepto of whose qualty worses. It ca also be see 4 P a g e

9 Computato tme (secs) Network latecy (ms) Network latecy (ms) Ftess Ftess Stadard devato (IJACSA) Iteratoal Joural of Advaced Computer Scece ad Applcatos, that fds solutos wth the best ftess value ad etwork latecy whe compared to the other algorthms. I Fg. (c) the computato tmes of all four algorthms are compared. It s observe that oly has the hghest computato tme. Ths s as a result of the computatoal overhead geerated by the etwork model. has the lowest computato tme whch s about oe thrd of 's tme. Also computato tmes for both, ad crease slghtly wth the umber of caddate servces except for whose computato tme remas largely uchaged. Fg. (d) shows that creasg caddate servce sze does't fluece the dversty of populato for, ad. But, stadard devato s mproved slghtly. Also, shows the worst stadard devato whle has the best stadard devato amogst the test algorthms Caddate Servces per task (a) Graph showg mpact of umber of caddate servces o ftess Caddate servces per task (b) Effect of umber of caddate servces o etwork latecy Caddate servces per task (d) Effect of umber of caddate servces per task o populato dversty Fg.. Plot of caddate servce sze agast ftess, etwork latecy, computato tme ad stadard devato c) Sze of Tasks I ths expermet the umber of tasks are vared from 3 to 40 the the mpact of ftess, etwork latecy, computato tme ad stadard devato o the algorthms are determed. I Fg. (a) ad (b), t s observed that qualty of ftess ad etwork latecy degrades wth sze of tasks for all test algorthms. s see to produce the best qualty solutos terms of ftess ad latecy (ted wth ) whle produces worst qualty of solutos. I Fg. (c) a patter smlar to Fg. (c) s observed, the oly dfferece otced s that computato tme peaks at hgher values whe compared to graph Fg. (c). Lastly Fg. (d) shows that populato dversty creases learly wth sze of tasks (a) Graph showg mpact of umber of tasks o ftess Tasks Caddate servces per task (c) Effect of umber of caddate servces per task o computato tme Tasks (b) Graph showg mpact of umber of tasks o etwork latecy 5 P a g e

10 Stadard devato Computato tme (secs) (IJACSA) Iteratoal Joural of Advaced Computer Scece ad Applcatos, Tasks (c) Effect of umber of tasks o computato tme Tasks (d) Effect of umber of caddate servces per task o stadard devato Fg.. Plot of sze of task agast ftess, etwork latecy, computato tme ad stadard devato V. CONCLUSION I ths paper a ovel approach to etwork-aware ad QoS based servce composto the cloud s preseted. Cotrary to curret works, ths study separates QoS of etwork from servce QoS. It cossts of a etwork model whch s composed of a ovel etwork coordate system called LADMF. LADMF uses matrx factorzato to estmate the etwork latecy (Roud trp tme) betwee BDS o the cloud. LADMF uses learg automata to ecode servce postoal coordates wth addtoal learg parameters. Ths way the estmato process becomes decetralzed where every servce govers ts ow path to latecy estmato. The latecy formato s the passed to a ovel servce composto algorthm based o o-domated sort geetc algorthm called. 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