RESEARCH ON PERFORMANCE MODELING OF TRANSACTIONAL CLOUD APPLICATIONS

Transcription

1 Joural of Theoretcal ad Appled Iformato Techology 3 st October 22. Vol. 44 No JATIT & LLS. All rghts reserved. ISSN: E-ISSN: RESEARCH ON PERFORMANCE MODELING OF TRANSACTIONAL CLOUD APPLICATIONS XIAOYING WANG, 2 XUHAN JIA, 3 LIHUA FAN, 4 WEITONG HUANG Departmet of Computer Techology ad Applcatos, Qgha Uversty, Xg 86, Cha 2 Departmet of Computer Scece ad Techology, Tsghua Uversty, Bejg 84, Cha E-mal: wxy_cta@qhu.edu.c, jaxh@qhu.edu.c, falh@qhu.edu.c, 2 huagwt@tsghua.edu.c ABSTRACT As cloud computg has gaed a lot of atteto recetly, performace modelg of cloud applcatos would be very mportat for varous maagemet ssues, such as capacty plag ad resource provsog for the cloud provders. Ths paper coducts research o several performace modelg approaches of trasactoal cloud applcatos. Steady-state models are establshed for both sgle-ter ad mult-ter applcatos, ad the ehaced models are studed o the bass of basc models. Smulato expermets are desged ad performed to valdate the models we proposed, whch llustrates that the measured results ft the aalytc models very well. Keywords: Performace Modelg, Trasactoal Applcatos, Cloud Computg. INTRODUCTION Nowadays, cloud computg has become a emergg computg model by whch users ca ga access to ther applcatos from aywhere, through ay coected devce []. The cocept corporates frastructure as a servce (IaaS), platform as a servce (PaaS) ad software as a servce (SaaS) as well as Web 2. ad other recet techology treds [2] whch have the commo theme of relace o the Iteret for satsfyg the computg eeds of the users [3]. By provdg ubouded scale ad dfferetated qualty of servce, the cloud computg model ca help mprove busess performace ad cotrol the costs of delverg IT resources to other orgazatos. I cloud evromets, performace ssues are always cocered by users ad resource provders. The QoS (Qualty of Servce) [4] s oe of the most mportat metrcs, whch emphaszes ed-to-ed performace ad usually ca be quatfed by respose tme, rejecto rato, ad throughput ad so o. The, a clear ad feasble performace model for cloud applcatos s very mportat, whch ca help to estmate the possble acheved performace accordg to the put resource amout ad load formato. However, moder applcatos are ofte bult o complex archtecture or rug o multple server groups, whch makes the modelg work more dffcult. I ths paper, we attempt to coduct a seres of research o performace modelg of cloud applcatos. Several commo techques for dfferet scearos are troduced ad the drawbacks are dscussed ad mproved. Much pror work has corporated queug theory to aalyze the performace of Web applcatos. Here, we ehaced the queug models order to ft the executg system better. 2. RELATED WORK Performace modelg s wdely-used varous research areas related to QoS maagemet ad dyamc resource allocato. For stace, Bea et al. [5] tred to solve the resource maagemet problem of multple applcato evromets large-scale dataceters. Chadra et al. [6] studed o the dyamc resource allocato ssues o the bass of GPS (Geeral Processor Sharg) mode. Meascé et al. [7] used smlar settgs ad modfed the model accordg to dfferet resource allocato schemes prorty ad CPU sharg. Furthermore, more ad more recet work bega to dscuss complex applcato archtectures ad has proposed correspodg models for dfferet scearos. Meascé et al. [8] descrbed how to motor ad tue E-commerce stes to keep certa QoS levels, whch close queug etwork models are employed cosderg both CPU ad memory resources. Urgaokar et al. [9] proposed a flexble dyamc resource provsog strategy to deal wth varyg workload usg a G/G/ queug system to model each server. Doyle et al. [] proposed a 66

2 Joural of Theoretcal ad Appled Iformato Techology 3 st October 22. Vol. 44 No JATIT & LLS. All rghts reserved. ISSN: E-ISSN: model-based method to maage the resources amg at dfferet performace levels, whch maly cosders memory-related resources. I cotrast, ths paper we focus o the performace modelg of trasactoal cloud applcatos, ad the aalyss of tme-doma ad admsso cotrol effects s troduced to the orgal model for ehacemet. 3. OVERVIEW OF TRANSACTIONAL CLOUD APPLICATIONS 3. Archtecture Usually the cloud evromet, there are may physcal odes costtutg the uderlyg frastructure. Vrtual maches (s) servg for dfferet ters of dfferet cloud applcatos are deployed above these physcal odes. Notably, s servg for the same ter of a certa applcato ofte locate o dfferet physcal odes. Moreover, for a certa ter at oe applcato evromet, there may be more tha oe cofgured to serve the requests. Accordgly, Fg. shows a example routg archtecture of a typcal trasactoal cloud applcatos comprsed of three ters, cludg the web server ter, the applcato server ter ad the database server ter. At each ter, there s a router charge of dspatchg requests to all the s servg for ths ter accordg to certa kd of load balacg strategy. After a request s fshed at a certa ter, t wll be forwarded to the router at the ext ter or leave the evromet f t fshes at the last ter. Sce the capacty of s mght be chaged from tme to tme, the performace modelg of such applcatos should corporate the heterogeety feature to cosderato. Arrve Λ comg requests λ, λ,2 R λ,k λ 2, λ 2,2 R 2 λ 2,k 2 λ 3, λ 3,2 R3 Web Servers Applcato Servers Database Servers Depart Fg. A Example Archtecture of Cloud Applcatos 3.2 Performace Metrcs I the cloud evromet, the frastructure provder should guaratee some o-fuctoal requremets of the customers. SLAs (Servce Level Agreemets) are usually used for egotato betwee servce provders ad customers, whch specfy the QoS level ad the correspodg cost λ 3,k 3 model. Here, we dscuss two typcal QoS metrcs as follows. () Ed-to-ed Respose Tme (deoted as R) From the perspectve of customers, the total tme from whe the request set to the cloud servce to whe the request fshed ad results retured to the customer s called ed-to-ed respose tme. It cludes both the watg tme the queue ad the processg tme the servce odes. Sce most servces employ the frst-come-frst-serve schedulg strategy ad the workload testy vares sgfcatly, the respose tme s ofte dffcult to predct ad cotrol. The ed-to-ed respose tme s the most mportat metrc sce t drectly reflects users experece. Accordg to the workg mechasm of the cloud servce, the respose tme s usually decded by the processg ablty of the servers ad the curret workload stuato. (2) Throughput (deoted as X) The throughput s a metrc measurg how may trasactos could be fshed or how may operatos could be doe durg oe tme ut. Geerally, the throughput wll crease as the workload testy rses up. However, whe the resource utlzato reaches early %, the system wll be saturated wthout addtoal ablty to deal wth more requests. I ths case, the throughput mght drop dow due to the thrashg effect. 3.3 Operatoal Aalyss Queug operatoal aalyss s wdely used to aalyze the performace of complex systems, whch provdes a relatvely smple way to characterze the features ad statuses of the executg system quattatvely. The operatoal aalyss gves some basc equatos whch ca be used whe characterzg the system. Two typcal laws ofte used relatve research are as follows. () Utlzato Law Accordg to the utlzato law, the utlzato of a system (U) s equal to the product of total throughput (X) ad the servce demad (D), amely that U=X D. Here, the servce demad meas the average processg tme of each request. (2) Lttle s Law [] Accordg to Lttle s Law, the populato (N) the system s equal to the product of the system throughput (X) ad the ed-to-ed respose tme (R), amely that N=X R. 67

3 Joural of Theoretcal ad Appled Iformato Techology 3 st October 22. Vol. 44 No JATIT & LLS. All rghts reserved. ISSN: E-ISSN: PERFORMANCE MODELING FOR STEADY STATE 4. Sgle-ter Applcatos For smplcty, we start the performace modelg dscusso by examg the applcatos wth oly oe ter. Note that there are stll multple s wth dfferet capactes provdg servce for the applcato. Deote the umber of s as. Icomg requests arrve at the evromet cotuously. Deote the arrval rates of the requests asλ. The curret servce rates of the allocated s are deoted as (µ, µ 2,,µ ). Accordg to the queug theory, we ca use several methods to set up the approxmate performace model for ths scearo as follows. () Sgle M/M/ queue. Overall, the smplest way s to setup a sgle M/M/ queug model, where the capacty of the server s the summary of all capactes,.e. µ +µ 2 + +µ. The, the mea respose tme could be estmated as R = µ Λ. = (2) Multple M/M/ queues. From aother pot of vew, we ca separate the comg requests to several streams, makg the arrval rate to the th as λ. I ths case, the mea respose tme of the multple queues should be balaced, whch R = ( µ λ ), =,2,..., meas. Λ = λ = (3) Homogeous M/M/ queue. I order to use a M/M/ queue to approxmate the orgal system, we set the capacty of each server, deoted as µ, as the average of all the capactes. The, accordg to the aalytcal results of the M/M/ queug system, the mea respose tme ca be derved as ( ρ) R = + µ 2 ( ρ) ( ρ) ( ρ) µ! + () ( ρ)! =! µ = µ = Where ρ=λ/ (µ) s the system utlzato. (4) Heterogeeous M/M/ queue. O the bass of homogeeous M/M/ model, we attempt to explot further to set up more accurate models cosderg heterogeeous servg capabltes of the s. Frst, we assume that the o-demad scheduler wll always decde to sed the curret request to the fastest server. Wthout loss of geeralty, we assume the capactes of s (µ, µ 2,,µ ) are sorted descedg order. Durg the system executo, t mght trast from oe state to aother state, where each state s determed by the curret populato the system. Accordg to the Markov state flow equato, the put ad output speed of each state should be equal, amely that K m m = = p Λ = p µ, K m( m, ) (2) where p m deotes the possblty of the mth state. The, we ca get each p m by terato, as follows m (3) Λ pm = p, K = m( m, ) K m K µ j µ j = j = = where p s the possblty whe the queug system s totally empty. Sce the summary of p m should be due to the probablty law, we ca derve the value of p easly as (4) m Λ Λ p = + m m= µ j Λ µ µ j = j= = = j = The, all the values of p m (m=, 2, 3 ) could also be calculated. Deote q w as the average legth of the watg queue, ad q p as the umber of requests beg processed, the they ca be computed as w q = ( pm ( m ) ) m= (5) p q = ( ) + ( ) pm m pm m= m= Fally, the mea respose tme could be w p R = q + q. obtaed as ( ) Λ 4.2 Mult-ter Applcatos For mult-ter applcatos, we ca dvde the requests stayg the system as two categores accordg to ther states, ether beg processed or watg the queue at a certa ter. Sce there are multple processg uts the mult-ter system, we ca employ queug etwork [2] to establsh the aalytc performace model. Here we assume the comg requests are processed the order as they arrve at each ter. Assume there are M ters the applcato archtecture, ad the we ca set up a queug etwork wth M queues, correspodg to each ter respectvely. Deote Λ as the arrval 68

4 Joural of Theoretcal ad Appled Iformato Techology 3 st October 22. Vol. 44 No JATIT & LLS. All rghts reserved. ISSN: E-ISSN: rate of comg requests, ad s as the servce demad at the th ter. The, accordg to the mea value aalyss results [3], the expected mea respose tme of the queug etwork ca be M derved as s R =. The above dscussos = Λ s ths secto are sutable for the scearo that the system s steady states, whch meas that the arrval rate s equvalet to the departure rate durg a certa tme perod log eough. I ths case, the throughput X s equal to the arrval rateλ. 5. MODELING THE PERFORMANCE IN SATURATED SCENARIO Whe the arrval rate of the applcato requests keeps rsg ad becomes hgher tha the summarzed processg capablty of the etre system, the resources wll be fully utlzed ad the queue wll become loger ad loger. The system eters to a saturated scearo ad caot coverge to a equvalet state. I ths case, the steady-state performace model could t be drectly appled. Nevertheless, the workload testy vares sgfcatly realstc cloud evromets, ad thus the overload stuato occurs from tme to tme. Cosequetly, we have to cosder how to model the performace for systems the saturated scearo. 5. Tme-doma Aalyss I order to deal wth the saturated scearo, we dvde the tme perod to multple domas, ad deote the doma legth as T. Assume that there are q * requests watg the queue whe the ext doma s about to start, ad deote the curret tme as t *. Sce the system utlzato reaches early % the saturated scearo, the summarzed servce rates of the system should be µ +µ 2 + +µ. I ths case, the legth of the watg queue wll cotuously rse up, ad thus the average queue legth q of the ext examed perod should be q' = T * t + T * t q * + Λ µ t dt * T = q + Λ µ 2 = I the saturated stuato, the system throughput reaches the hghest, whch s equal to the summarzed servce rates of all s, amely that X = = = (6) µ. The, accordg to Lttle s Law, the expected mea respose tme the ext tme perod could be calculated as R = ( + q ) 5.2 Admsso Cotrol ' µ. = Whe the comg workload testy becomes heaver or some odes the system faled, there mght be suffcet resource capacty to deal wth the heavy load. Although some peaks oly last for a short tme, the accumulated requests would block the followg oes ad delay them a lot. To hadle such problems, moder systems ofte adopt admsso cotrol methods to refuse some of the comg requests. The am of dyamc admsso cotrol should be guarateeg the requests beg fshed meetg the target specfed SLAs. Hece, whe the populato of the system reaches a certa threshold, the ext comg request should be refused. To model the admsso cotrol effect, we should frst determe the maxmum umber of requests that the system ca hold. Operatoal aalyss could be leveraged here. Deote the maxmum resource utlzato specfed by the servce provder as θ, ad the target respose tme as r SLA. The, accordg to Lttle s Law, the maxmum permtted populato should be calculated as M max SLA P = θ max, r s max( s) (7) =.. M = where s deotes the servce demad of the multter applcato at the th ter ad θ ca be specfed by the servce provder to make surplus room for mateace cosderatos. 6. VALIDATION EXPERIMENTS I ths secto, we coducted a seres of expermets to evaluate the models we proposed. Durg the expermets, the put workloads are geerated from a real-world web trace (App ) ad a s load fucto (App 2) respectvely. For sgleter applcato expermets, the average requests szes of the two applcatos are 22.5 ad 35. respectvely. The tested system s comprsed of four s wth the capacty of (3, 6, 5, 4). For mult-ter applcato expermets, the tested cloud evromet s comprsed of three ters. Each ter has (5, 5, 7) odes ad the capactes of odes at dfferet ters are (35,9,6) respectvely. 6. Valdato of Steady-state Models Fg.2 shows the evaluato results of steady-state models. Fg.2 (a) shows the comparso of the 69

5 Joural of Theoretcal ad Appled Iformato Techology 3 st October 22. Vol. 44 No JATIT & LLS. All rghts reserved. ISSN: E-ISSN: results by the four dfferet modelg approxmato methods ad the realstc values measured, by examg App. It ca be observed that the heterogeeous M/M/ model we set up reaches closest to the measured values. Fg.2 (b) shows the comparso of respose tme for mult-ter applcatos, from whch we ca see that the results derved from the prevously establshed model could ft the measured results qute well. Respose Tme (secod) Respose Tme(secod) Tme (secod) (A) Sgle-Ter Applcato modeled(app ) modeled(app 2) measured(app ) measured(app 2) measured values sgle M/M/ multple M/M/ homogeeous M/M/ heterogeeous M/M/ Tme(secod) (B) Mult-Ter Applcatos Fg.2 Valdato Results Of Steady-State Models 6.2 Valdato of Models for Saturated Stuato Fg.3 shows the evaluato results of the establshed models for saturated stuatos. Fg.3 (a) llustrates the comparso of the respose tme of two sgle-ter applcatos saturated stuato, where the steady-state models become applcable. I cotrast, the tme-doma aalyss could help to predct the performace level whe the load becomes too heavy. As see, the modeled values are cocdet wth the measured respose tmes. Moreover, we also coducted expermets for mult-ter applcatos by examg the throughput metrc, ad the evaluato results are as show Fg.3 (b). Here, the admsso cotrol effects exhbt the fgure, especally durg the tme rego (3~8) ad (2~44). Due to suppresso of the excessve requests, the throughput could be cotrolled ad the system could be mataed a stable state. Also, the estmated throughput values depct as cosstet wth the results of practcal expermets, showg the feasblty ad the applcablty of our modelg approach. Respose Tme (secod) Throughput (reqs/s) TIme (secod) (A) Sgle-Ter Applcatos modeled(app ) modeled(app 2) measured(app ) measured(app 2) Tme (secod) modeled(app ) modeled(app 2) measure(app ) measure(app 2) (B) Mult-Ter Applcatos Fg.3 Valdato Of Models For Saturated Stuato 7. CONCLUSION AND FUTURE WORK Ths paper descrbes the geeral archtecture of trasactoal cloud applcatos ad some commo QoS metrcs. The, the performace modelg methods of such applcatos are studed detal based o the queug theory. O the bass of steady-state model, we ehace the model further to deal wth the saturated scearo. Results of evaluato expermet llustrate the effectveess ad the feasblty of our aalytc model. As possble future work, we ted to explot deeper to the specfc detals to the applcato 7

6 Joural of Theoretcal ad Appled Iformato Techology 3 st October 22. Vol. 44 No JATIT & LLS. All rghts reserved. ISSN: E-ISSN: performace modelg ad also wder across other types of cloud applcatos. ACKNOWLEDGMENTS Ths research s supported part by Natoal Natural Scece Foudato of Cha (No ) ad Tsghua Tecet Jot Laboratory for Iteret Iovato Techology (No. 2-). REFRENCES: [] IBM. (29). Seedg the Clouds: Key Ifrastructure Elemets for Cloud Computg. Avalable: ftp://ftp.software.bm.com/commo/ss/sa/wh// ow322use/oiw322usen.pdf [2] J. M. Wlls, "Who Coed the Phrase Cloud Computg?," IT Maagemet ad Cloud Blog, December, vol. 3, 28. [3] Wkpeda. Cloud Computg. Avalable: [4] X. Wag, Y. Xue, L. Fa, et al., "Research o Adaptve QoS-Aware Resource Reservato Maagemet Cloud Servce Evromets," 2 IEEE Asa-Pacfc Servces Computg Coferece (APSCC), 2, pp [5] M. N. Bea ad D. A. Measce, "Resource allocato for autoomc data ceters usg aalytc performace models," Proceedgs of the 2d IEEE Iteratoal Coferece o Autoomc Computg (ICAC'25), Seattle, WA, 25, pp [6] A. Chadra, W. Gog, ad P. Sheoy, "Dyamc Resource Allocato for Shared Data Ceters Usg Ole Measuremets," Proceedgs of ACM Sgmetrcs 23, Sa Dego, CA, 23. [7] D. A. Meascé ad M. N. Bea, "Autoomc Vrtualzed Evromets," Proceedgs of IEEE Iteratoal Coferece o Autoomc ad Autoomous Systems(ICAS'6), Slco Valley, CA, USA, 26, p. 28. [8] D. A. Meascé, D. Barbará, ad R. Dodge, "Preservg QoS of e-commerce stes through self-tug: a performace model approach," the 3rd ACM coferece o Electroc Commerce, 2, pp [9] B. Urgaokar, G. Pacfc, P. Sheoy, et al., "A aalytcal model for mult-ter teret servces ad ts applcatos," the 25 ACM SIGMETRICS teratoal coferece o Measuremet ad modelg of computer systems, 25, pp [] R. P. Doyle, J. Chase, O. Asad, et al., "Model- Based Resource Provsog a Web Servce Utlty," The 4th USENIX Symposum o Iteret Techologes ad Systems, Seattle, Washgto, USA, 23. [] J. McKea, "A Geeralzato of Lttle's Law to Momets of Queue Legths ad Watg Tmes Closed, Product-Form Queueg Networks," Joural of Appled Probablty, vol. 26, pp. 2-33, 989. [2] P. J. Deg ad J. P. Buze, "The Operatoal Aalyss of Queueg Network Models," ACM Computg Surveys (CSUR), vol., pp , 978. [3] F. Baskett, K. M. Chady, R. R. Mutz, et al., "Ope, Closed, ad Mxed Networks of Queues wth Dfferet Classes of Customers," Joural of the ACM, vol. 22, pp ,