Dynamic Control of Data Streaming and Processing in a Virtualized Environment

Transcription

1 > REPLACE THIS LINE WITH YOUR PAPER IDENTIFICATION NUMBER (DOUBLE-CLICK HERE TO EDIT) < 1 Dynamc Control of Data Streamng and Proceng n a Vrtualzed Envronment Junwe Cao, Senor Member, IEEE, Wen Zhang, Member, IEEE, and We Tan Abtract Performance of data treamng applcaton co-determned by both networkng and computng reource, and therefore they hould be co-cheduled and co-allocated n an ntegrated and coordnated way. Dynamc control of reource chedulng and allocaton requred, becaue unlateral redundancy n ether networkng or computng reource may reult n the overprovon of t and the other may become a bottleneck. To avod reource hortage a well a overprovon, n th paper, a vrtualzed platform utlzed to mplement data treamng and proceng. In th platform, fuzzy logc controller are degned to allocate CPU reource; teratve bandwdth allocaton appled and proceng- and torage-aware to guarantee on-demand data provonng. Expermental reult how that our approach lead to hgher applcaton performance a well a hgher reource utlzaton, compared wth other reource chedulng and allocaton method. Index Term vrtualzaton, data treamng, reource chedulng and allocaton, fuzzy logc control. Note to Practtoner Data treamng ha become an mportant paradgm n many bune and centfc applcaton, uch a fraud detecton n bankng ndutry and gravtatonal-wave obervaton n atronomy. In th paper, we apply vrtualzaton to provde better upport for thee applcaton, leveragng t advantage n reource on-demand allocaton. A novel dynamc control method propoed o that CPU and bandwdth can be co-cheduled and co-allocated nce for data treamng applcaton thee reource are tghtly coupled from the performance perpectve. In th control method, fuzzy logc control appled for CPU allocaton and an teratve algorthm adopted for proceng-, congeton- and torage-aware bandwdth allocaton. We ue fuzzy control becaue t doe not rely on the mathematcal modelng of an object and can mplement human expert heurtc knowledge va IF-THEN rule. Therefore t derable n our data treamng cenaro, due Manucrpt receved July 10, 2011; reved Oct 16, Th work upported by Natonal Scence Foundaton of Chna (grant No ) and Mntry of Scence and Technology of Chna under Natonal 973 Bac Reearch Program (grant No. 2011CB and No. 2011CB302805) and Natonal 863 hgh-tech Program (grant No. 2011AA040501). Junwe Cao wth Reearch Inttute of Informaton Technology and Tnghua Natonal Laboratory for Informaton Scence and Technology, Tnghua Unverty, Bejng , Chna (correpondng author, phone: ; fax: ; e-mal: jcao@tnghua.edu.cn). Wen Zhang wa wth Natonal CIMS Engneerng Reearch Center, Tnghua Unverty, Bejng , Chna. He now wth Chongqng Mltary Delegate Bureau, General Armament Department of PLA, Chongqng , Chna. We Tan wth IBM T. J. Waton Reearch Center, Hawthorne, New York, USA (e-mal: wtan@u.bm.com). to the varable couplng and heavly nonlnear nature of the ytem. I. INTRODUCTION ata treamng and proceng ha become more mportant Dn many bune and centfc applcaton. Such applcaton requre effcent tranmon of data from/to dtrbuted ource. It often not feable to tore the entre data before ubequent proceng becaue of the lmted torage and hgh volume of data to be proceed. Mot applcaton run contnuouly and requre effcent ue of computatonal reource to carry out proceng n a tmely manner [1]. An example the LIGO (Laer Interferometer Gravtatonal-wave Obervatory) [2] whch am at the detecton of gravtatonal wave emtted from pace ource. LIGO data analy tream terabyte of data per day from obervatore for real-tme proceng ung centfc workflow [3]. Th a typcal cenaro n many centfc applcaton where data proceng contnuouly conducted over remote tream a f data were alway avalable from local torage. In our prevou work [6], we have already demontrated that, n data treamng applcaton, unlateral redundancy of ether CPU or bandwdth doe not necearly lead to hgh throughput; on the other hand, hortage of ether may alo negatvely mpact the throughput. Th reveal the tght couplng between CPU and bandwdth from the performance perpectve. Therefore t requred to allocate computng and networkng reource n order to reach a balance between hgh throughput and hgh reource utlzaton. Vrtualzaton technology whch can dynamcally provon vrtual machne (VM) [5], a natural choce for reource management [4] n th cenaro. The contrbuton of th work twofold. Frt a new co-chedulng framework propoed on a vrtualzed platform, o that CPU and bandwdth can be co-allocated for LIGO tream proceng. Such co-chedulng addree the mpact of the nterplay between CPU and bandwdth on the ytem performance. Second, a fuzzy-logc baed, cloed-loop feedback control [7] method deved for the aforementoned co-chedulng framework. Ung th method we control VM CPU allocaton by confgurng VM dynamcally accordng to reource utlzaton, and teratvely allocate bandwdth by cloely watchng proceng and torage tatu. The ret of th paper organzed a follow: Secton 2 formulate the data treamng and proceng problem, and

2 > REPLACE THIS LINE WITH YOUR PAPER IDENTIFICATION NUMBER (DOUBLE-CLICK HERE TO EDIT) < 2 Secton 3 and 4, fuzzy allocaton of CPU reource and teratve bandwdth allocaton are dcued, repectvely. Expermental reult are llutrated n Secton 5 to how performance evaluaton of our approach ung a gravtatonal wave data analy applcaton. Secton 6 dcue related work and Secton 7 conclude the paper. II. DATA STREAMING AND PROCESSING A. Data Streamng Applcaton Many extng centfc applcaton requre data treamng and proceng n a real tme manner. Data ource can be large-cale mulator or obervatore, wth megabyte of data generated per econd and terabyte of data aggregated per day. Data are uually treamed to remote proceng node for varou analye. For thee proceng node, t not feable to tore all data nce new data contantly arrve and conume local tore pace. Therefore, after data are proceed and become obolete, they need to be removed for newly arrval data. Th typcal cenaro reult n a tghtly coupled relatonhp among computng, torage and networkng reource. For example, a LIGO gravtatonal wave data analy applcaton read n two data tream from two remote LIGO obervatore (one n Wahngton State and the other n Louana State) and calculate correlaton coeffcent that can be ued to characterze mlarty of two data curve. If two gnal from two obervatore occur multaneouly wth mlar curve, t would be lkely that a gravtatonal wave canddate detected. LIGO data are archved n pecally formatted bnary fle,.e., Gravtatonal Wave Framefle (gwf). Each fle compoed wth 16 or 256 of data from multple channel of an obervatory. A LIGO data analy applcaton uually nvolve multple data tream (ere of mall data fle) from multple obervatore. LIGO data are archved n LIGO data grd [43] node whch cannot provde enough computng reource for drectly local data analy. LIGO tre to beneft from open computng reource uch a the Open Scence Grd (OSG) [44]. Whle abundant computng reource are avalable n OSG, no LIGO data are avalable on OSG node. It become crucal to tream LIGO data from LIGO data grd node to OSG reource for large-cale proceng. Data treamng and proceng become eental for LIGO data analy [38][39]. LIGO data analy applcaton are developed ung dfferent operatng and programmng envronment. In order to acheve fner-graned reource chedulng and hgher utlzaton of computng reource, multple applcaton have to further hare one phycal machne, and thu vrtualzaton become the enablng technology. A vrtualzed envronment can hot multple uch data treamng applcaton. One ngle vrtual machne (VM) provde a predctable and controllable run-tme envronment for each applcaton. All computng, torage and networkng reource on a vrtualzed platform can be hared among multple VM (and ultmately multple applcaton), a llutrated n Fg. 1. Whle our prevou work wa focued on reource harng among multple phycal machne [1], n th work, a vrtualzed envronment deployed, whch brng new challenge on dynamc control. Fg. 1. An llutraton of reource harng n a vrtualzed envronment among multple data treamng applcaton. The local torage play a key role correlatng networkng and computng reource. If no data avalable, computng reource wll be dle. If the allocated local torage full for an applcaton, data treamng cannot be carred out and networkng reource cannot be utlzed. At any tme t, for a data treamng applcaton, f the amount of data n local torage, denoted a Q (t), hgher than a certan level (e.g., a block a explaned later), data proceng trggered. Q (t) co-determned by both data provonng and proceng nce new data wll be treamed to local torage whle proceed data wll be cleaned up afterward. The amount of output data (e.g. tattcal value) uually mnor and gnored when we calculate local torage. The amount of data n torage vare over tme and can be decrbed ung the followng dfferental equaton: Q t tranpeed t d t, where ( t) Q Reource n a Vrtualzed Envronment Bandwdth Dk CPU Vrtual reource for app 1 Vrtual reource for app 2 Vrtual reource for app Vrtual reource for app n Q ( 0 ) 0 (1), tranpeed (t) and d (t) tand for the dervatve of Q (t), agned tranferrng bandwdth and proceng peed for data tream. If there are data avalable n the local torage, an ndcator, denoted a Ready for the applcaton, et to be 1, otherwe Ready 0. So d (t) can be decrbed a: 0, Ready 0 d () t > 0, Ready 1 B. Performance Metrc For data treamng applcaton, data throughput the mot mportant performance metrc. Meanwhle reource utlzaton hould be alo condered. Real Proceng Speed (RPS): the actual data proceng peed gven by d (t) Theoretc Proceng Speed (TPS): the data proceng peed the allocated CPU reource can generate f there were alway uffcent data provoned, denoted a

3 > REPLACE THIS LINE WITH YOUR PAPER IDENTIFICATION NUMBER (DOUBLE-CLICK HERE TO EDIT) < 3 procpeed (t,c (t)), where C (t) tand for the allocated CPU reource for applcaton at tme t. Relatonhp between procpeed (t,c (t)) and C (t) mut be determned wth ytem dentfcaton and t obvou that procpeed (t,c (t)) a non-decreang functon of C (t), where C (t) manly refer to a proporton of CPU cycle a explaned later. Real Throughput (RTP): gven a data provonng cheme, the actual amount of data proceed n a gven perod of tme. Theoretc Throughput (TTP): the amount of data proceed n a gven perod of tme f there were alway enough data provonng. Schedulng CPU, torage and bandwdth reource carred out perodcally to deal wth dynamc nature of reource and applcaton, and each perod referred to a a chedulng perod. Suppoe the length of a chedulng perod M, and for the h th chedulng perod, the followng formula are traghtforward: d 0, Re ady procpeed t, C () t () t d t, C () t From (1): TTP, h ( 1) hm M 0 { ( ), Re ady 1 procpeed ( t, C () t ) ( h 1) RTP hm, h d ( t, C () t ) dt ( h 1) M hm. ( () () ( h 1) M ) RTP h, tranpeed t Q t dt hm tranpeed t dt+ Q h M Q hm t h M dt () (( 1) ) Defne utlzaton of computng reource (UC n hort) a RTP, h UC, h TTP (2), h.e., UC hm tranpeed () t dt+ Q ( h 1) M Q hm t ( h 1 ) M, h hm (3) procpeed () t dt t ( h 1 ) M, denotng to what extent the allocated compute reource utlzed. RTP,h can be defned n another form a: RTP, h procpeed () t dt (4) Ω, h where Ω,h tand for the tme fragment when proceng gong on and then utlzaton can be redefned n another way a: Ω h UC, h M (5) Note that (5) mple that TPS,h a contant n a chedulng perod wth gven CPU reource. UC can be defned alo a the rato of RPS to TPS. The problem to allocate proper amount of CPU reource to generate RPS approachng TPS a much a poble gven the data upply cheme. It obvou that redundant CPU reource wll make a TPS much larger than RPS, whch mple underutlzaton of computng reource. If avalable bandwdth lmted, RPS wll be zero at mot tme wth redundant CPU cycle for lack of data to proce. Th dependency between data provon and proceng make t neceary to allocate compute reource on demand o a to make RPS a cloe to TPS a poble. III. CPU ALLOCATION WITH FUZZY CONTROL CPU allocaton mplemented ung a fuzzy control approach on top of vrtualzaton technology, where the vrtualzaton provde an olated run-tme envronment and the fuzzy control addree approprate reource confguraton n a vrtualzed envronment. A. Vrtualzaton wth Xen Recent progre on vrtualzaton technology make t poble for reource olaton and performance guarantee for each data treamng applcaton. Vrtualzaton provde a layer of abtracton n dtrbuted computng envronment, and eparate phycal hardware wth operatng ytem, o a to mprove reource utlzaton and flexblty. Xen [36], an open ource hypervor, ue to buld the vrtualzed envronment. Wth Xen, confguraton of VM can be dynamcally adjuted to optmze performance. The CPU of a VM called vrtual CPU, often abbrevated a VCPU. The quota of phycal CPU cycle a VCPU wll get determned by two parameter,.e., cap and weght. The cap value defne the maxmum percentage of the CPU that can be ued by the VM; when multple VM compete for one CPU, ther weght value defne ther proporton n gettng the hared CPU. For example, a VCPU wth a weght of 128 can obtan twce a many CPU cycle a one whoe weght 64, whle 50 a a cap value ndcate that the VCPU wll obtan 50% of a phycal CPU cycle. In th work, cap adjuted dynamcally accordng to the meaured utlzaton and pre-defned fuzzy rule a decrbed below. B. Fuzzy Control A fuzzy control ytem [37] baed on fuzzy logc related wth fuzzy concept that cannot be expreed a true or fale but rather a partally true. A fuzzy logc controller (FLC) depcted n Fg. 2; t cont of an nput tage, a proceng tage, and an output tage. Some bac concept are gven below to help contruct an elementary undertandng of fuzzy logc controller and ther mechanm. Unvere of dcoure the doman of an nput (output) to (from) the FLC. Input and output mut be mapped to the unvere of dcoure by quantzaton factor (Ke and Kec n Fg. 2) and calng factor (Ku n Fg. 2), repectvely, whch help to mgrate the fuzzy control logc to dfferent problem wthout any modfcaton.

4 > REPLACE THIS LINE WITH YOUR PAPER IDENTIFICATION NUMBER (DOUBLE-CLICK HERE TO EDIT) < 4 Fg. 2. A fuzzy logc controller. The nput varable n a fuzzy control ytem are n general mapped nto fuzzy et, where an nput varable may be mapped nto everal fuzzy et wth correpondng truth value determned by the memberhp functon. Th proce called fuzzfcaton. All the rule that apply are nvoked, ung the memberhp functon and truth value obtaned from the nput, to determne the reult of the rule. Th reult n turn wll be mapped nto a memberhp functon and truth value controllng the output varable. Thee reult are combned to gve a pecfc anwer by a procedure known a defuzzfcaton. Lngutc varable decrbe the nput and output() of a fuzzy controller. Thee lngutc varable are a natural way reemblng human thought to handle uncertante. Lngutc varable nvolved n th work nclude the nput of the fuzzy controller, UC and UC and the output of the fuzzy controller, a proportonal factor (PF). Lngutc value are ued to decrbe charactertc of the lngutc varable. Very low, low, medum, hgh and very hgh are the lngutc value for UC, whle thoe for UC and PF are NB, NM, NS, ZE, PS, PM and PB, where N, P, B, M, S and ZE are abbrevaton of negatve, potve, bg, medum, mall and zero, repectvely, and the combnaton of them jut take on a degree of truth. Dfferent from clacal mathematc, n fuzzy world, th repreented a a contnuou value between 0 to 1, and 0.5 ndcate we are halfway certan. The mappng from a numerc value to a degree of truth for a lngutc value done by the memberhp functon. Lngutc rule form a et of IF preme THEN conequent rule to map the nput to output() of a fuzzy controller,.e., to gude the fuzzy controller acton. Thee rule are defned n term of lngutc varable, dfferent from the numercal nput-or-output of the clacal controller. A lngutc rule for example : IF UC hgh AND UC NB THEN PF NB. Rule-bae hold a et of IF-THEN rule a a part of the controller, dctatng how to acheve PF accordng to the fuzzfed lngutc value of UC and UC. Memberhp functon quantfy the certanty an UC and UC value to be aocated wth a certan lngutc value. Except for the memberhp of lngutc value very low for UC, we ue ymmetrc trangle of an equal bae and 50% overlap wth adjacent MF. Unlke tradtonal et theory, n fuzzy et theory underlyng fuzzy control theory, et memberhp not bnary but contnuou to deal wth uncertante. Thu, a fuzzy nput or output may belong to more than one et maxmum two adjacent et n our MF wth dfferent certanty value. Inference mechanm n Fg. 2 determne whch rule wll be appled at the k th amplng pont, baed on the fuzzfed UC and UC. To compute the certanty value of the preme n the correpondng IF preme THEN conequent rule(), we take the mnmum between the certanty value of UC and UC, nce the conequent cannot be more certan than the preme. Fuzzfcaton to tranform prece value of nput nto fuzzy et wth correpondng memberhp functon, whch ndpenable for fuzzy nference. Output of fuzzy nference mut be tranformed nto a clear value by defuzzfcaton. C. Lngutc Varable and Fuzzy Rule A for the FLC propoed n th paper, the nput are the oberved reource utlzaton UC a defned n (2) or (5) and the dervatve of t, UC. Although t ha two nput, eentally t a ngle nput controller nce UC can be derved from UC, UCUC(k)-UC(k-1). The output of the fuzzy controller for applcaton n the h th chedulng perod a proportonal factor, denoted a PF,h. At the h th chedulng perod, gven nput UC and UC, uppoe the relevant fuzzy et of output form a et denoted a M,h wth memberhp denoted a M,h (u), where u U,h and U,h the unvere of dcoure, then the output can be calculated a PF M u udu M u du (6) h, h, h, Uh, Uh, Suppoe the ntal cap of each applcaton are C,0, n the h th chedulng perod, the cap wll be ( PF 1) CapScale, 1 C, h C, h 1 +, h 1 h (7) where CapScale the varyng cale of the allocated cap. PF,h adjuted every chedulng perod, o t adaptve to the varyng tuaton. Relatonhp between the allocated cap and procpeed a lnear model, a decrbed later n Secton V. p q procpeed() h a l procpeed( h l) + bmcap( h m) (8) l 1 m 1 Trangular memberhp functon are adopted for nput UC and UC and output PF, a hown n Fg. 3. Table I provde a ummary of fuzzy rule. A low utlzaton mple that the allocated CPU quota hould be decreaed to releae redundant compute reource wthout reducng the ultmate throughput; meanwhle extremely hgh utlzaton ndcate that more CPU reource are requred to ncreae proceng effcency. When the utlzaton very low, low or medum, the generated PF hould be le than 1 whle the very hgh utlzaton requre a PF lager than 1. To avod ocllaton, UC hould alo be pad attenton. When the utlzaton far away from the ettled pont (80% here), the adjutment can be bg. Then the PF n the 1 t, 2 nd and 3 rd column n Table I wll be NB (negatvely bg) whle n the lat column t PB (potvely bg). When the utlzaton fall to the hgh area, more careful adjutment requred, a hown n the 4 th column n Table I. For example, when UC NS, PF alo NS; and when UC ZE, PF alo ZE, whch mean that no adjutment requred o a to keep a table tatu. From Table 1, t can be een that thee fuzzy rule are mple but robut. It can guarantee a rapd convergence to the ettled

5 > REPLACE THIS LINE WITH YOUR PAPER IDENTIFICATION NUMBER (DOUBLE-CLICK HERE TO EDIT) < 5 pont wthout table tate error, whch a requred charactertc for control ytem a hown n expermental reult ncluded n Secton V very low low medum hgh very hgh Input varable UC A FLC receve UC and UC and output the cap of CPU for each applcaton and then procpeed determned ung (8). An teratve bandwdth allocaton (IBA) mplemented a decrbed n Secton IV to decde tranpeed. UC at the next chedulng perod obtaned wth (2) or (5). In uch a way, the control ytem work and dynamc reource allocaton mplemented for vrtualzaton. Fg. 4. Dynamc control of CPU and bandwdth reource co-chedulng and co-allocaton. Fg. 3. Trangular memberhp functon of nput UC and UC and output PF. Lngutc value of UC nclude very-low, low, medum, hgh and very-hgh, ndcatng CPU utlzaton. Both nput UC and output PF adopt trangular memberhp functon wth lngutc varable of NB, NM, NS, ZE, PS, PM and PB. The unvere of dcoure of UC fall to the cope of -0.4 to 0.4, whch baed on our emprcal obervaton. It alo the cae for PF where the unvere of cope et to 0.6 to 1.4. UC PF NB NM NS ZE PS PM PB TABLE I. FUZZY RULES UC very low low medum hgh very hgh NB NB NB NB NM NS ZE PS PM PB D. Reource Co-chedulng and Co-allocaton Dynamc control of reource co-chedulng and co-allocaton for data treamng applcaton llutrated n Fg. 4. CPU and bandwdth reource are co-allocated by the actuator ACT. The tranfer functon G from allocated CPU and bandwdth reource to utlzaton UC not avalable, for the two nput are tghtly coupled wth each other. Fortunately, th tranfer functon not ndpenable for our fuzzy allocaton cheme. PB IV. ITERATIVE BANDWIDTH ALLOCATION Snce data are treamed to local torage through network, multple data tream need to hare the total bandwdth of the vrtualzed envronment, denoted a I. The ndvdual data tream, called eon, denoted a, form a et S. Each eon wll be agned wth a bandwdth x (.e., tranpeed n Secton II), where x X, X [b, B ] and b >0, B <. b tand for the leat bandwdth requred for eon, whle B the hghet bandwdth of the connecton from the correpondng data ource to eon. Seon ha a utlty functon U (x ) that aumed to be concave, contnuou, bounded and ncreang n the nterval [b, B ]. We try to maxmze the um of the utlte of all the eon, mantanng farne among them. The problem can be decrbed a follow. max U ( x ) S S.t. x I x X Varable U and L are the pre-defned upper and lower bound of data volume n torage for eon to control the paue and reumng of data tranfer,.e., when the data volume n torage of reache the upper bound, data tranfer halted and when th volume reache the lower bound, tranfer reumed. By th mean data tranfer may be ntermttent rather than contnuou, o a to be torage aware, to avod data overflow whle guaranteeng data provonng. Accordng to the amount of data n torage, there are two poble tranfer tate for each at any tme,.e., actve and nactve. All actve eon form a et, called S A, and t obvou that th et varyng becaue tranfer tate of eon are changng. At every amplng tme k, tatu of tranfer can be determned by data amount n torage and the prevou tatu:

6 > REPLACE THIS LINE WITH YOUR PAPER IDENTIFICATION NUMBER (DOUBLE-CLICK HERE TO EDIT) < 6 tatu, k 1, amount, k 1 < U, tatu, k 1 1 0, amount, k 1 U 0, amount, k 1 > L, tatu, k 1 0 1, amount, k 1 L, where tatu,k and amount,k tand for tatu of tranfer and data amount n torage for eon at the k th amplng tme, repectvely. The ntal condton tatu,0 1 and amount,0 0. We only allocate bandwdth for actve tranmon, o the bandwdth contrant become: x I. S A An teratve optmzaton algorthm propoed a follow. Whle S A ( k + 1) x otherwe, ( [ x k + k ) ( k ) α U ' k x ] f x X S A β ( k ) k ρ [ x ] f x x k ( k+ 1 ) X 0, S S A A > I ρi Here, x (k) the bandwdth for eon S at the k th amplng tme. {α k } and {β k } are two potve equence, β k (0, 1). [ ] X denote a projecton on the et X : y mn B, max b y '( ) U the ub-gradent of [ ] ( ) X, ( k ) () U x U and ' ( k ) U x S A U () x And ρ the o-called afety coeffcent to avod bandwdth exce, where ρ (0, 1). Th allocaton algorthm alo proceng-aware, nce tatu,k and amount,k are affected by proceng,.e., S A aocated wth data proceng. On the other hand, the allocated bandwdth wll alo change UC to trgger the FLC to adjut allocaton of CPU reource. In th way, bandwdth and CPU reource are co-cheduled and co-allocated. Parameter n th teratve bandwdth allocaton cenaro, uch a α k, β k and ρ can be adjuted accordng to dfferent allocaton prncple, uch a relatve farne and the-mot-needed-the-frt. The mot appled utlty functon logarthmc: U ( x ) w ln ( 1+ x ), where w the weght of eon x and a larger w mple a bgger quota n the total avalable bandwdth. Due to the couplng of data provonng and proceng, the relatonhp between proceng effcency and allocated CPU or bandwdth heavly non-lnear and hard, f not mpoble, to be expreed wth a cloed-form formula. It dffcult to apply tradtonal feedback control n abence of a prece model, and that why n Secton III we propoed to ue fuzzy control a an alternatve, due to t model-free nature. ( k ) V. PERFORMANCE EVALUATION A. Experment Confguraton VM are et up on a HP DL580G5 erver wth 4 Intel CPU contanng 16 Xeon E7310 core and 8 GB memore for LIGO data treamng applcaton. Data tem are treamed to the VM from remote data ource. LIGO data tream are compoed wth numerou mall data fle, each contanng obervatonal data acqured n 16 econd. Here 1,188 par of LIGO data fle from two obervatore wth the total amount of 4,354 MB are ued n the experment a ample data. In the followng experment, thee data are utlzed perodcally when needed. To reveal the mathematcal relatonhp between the proceng peed and the allocated computng reource (manly the quota of CPU), ytem dentfcaton carred out. The makepan durng whch all the data par are proceed are hown n Fg. 5, from whch t eem that allocated memory ha lttle mpact on the makepan for th applcaton. Makepan () Fg. 5. Makepan wth dfferent cap and memory ze. Three VM are etup wth 512 MB, 256 MB and 128 MB of memory, repectvely. The allocated cap for each VM range from 5% to 100%. The proceng peed hown n Fg. 6 wth the old lne. Polynomal curve fttng appled to generate a mathematcal functon from the cap to proceng peed, ung the Leat Square Method (LSM). Proceng Speed (M/) Meaured Ftted Cap (%) Fg. 6. Proceng peed wth dfferent cap. Memory ze for each VM et to 128 MB. Fg. 5 and 6 nfer that once the allocated cap exceed 50%, the ame ncreae n CPU Cap wll not gan o obvou beneft a t doe n the range of 5%-50%. In our experment, applcaton can be dvded nto two categore: lght and heavy one. In lght applcaton, data

7 > REPLACE THIS LINE WITH YOUR PAPER IDENTIFICATION NUMBER (DOUBLE-CLICK HERE TO EDIT) < 7 tem are proceed wth mall amount of computaton whle heavy applcaton perform more complex data proceng and relatvely larger amount of computatonal reource are requred. Heavy and lght group of applcaton are ued a benchmark, each group wth 5 applcaton. Total avalable bandwdth I, et to 5, 10 and 15 Mbp, hared among all applcaton. Three value of CapScale, 3, 5 and 8, are evaluated. Our allocaton algorthm evaluated for 100 chedulng perod. Each perod lat for 100 econd n thee experment. Performance metrc nclude the output of the FLC (PF), the utlzaton of each allocated computatonal reource (a defne n (1), UC), the allocated cap for each applcaton and reource uage (US) of CPU and bandwdth. Note that the uage of CPU mean the um of allocated cap for each applcaton, whch dfferent from the utlzaton defned n (1). B. Reource Allocaton and Utlzaton Fg. 7 and 8 llutrate the proportonal factor (PF), allocated reource (UC), cap value (Cap) and reource uage (US) value of heavy and lght applcaton, repectvely. A hown n Fg. 7 and 8, lght and heavy applcaton get approprate amount of CPU reource ung our approach, where the total bandwdth 5 Mbp. In thee cae, requred CPU reource of each applcaton far le than a whole phycal CPU, becaue the allocated cap of them are under 20% or even 10%. The total CPU utlzaton alo far le than 100% app1 app2 app3 app4 app5 (c) (d) Fg. 7. Performance of heavy tak. (a) Proportonal factor; (b) Utlzaton; (c) Allocated cap; (d) Reource uage. Intal cap for each applcaton are 10%. All the allocaton cheme are converged to a teady tate. No teady tate error ext n each allocaton cheme. Alo n preence of udden change of avalable reource, they can make a rapd repone, a hown n Fg. 8. PF Schedulng Perod (a) (a) (b) Fg. 7. (to be contnued) (b) Fg. 8. (to be contnued)

8 > REPLACE THIS LINE WITH YOUR PAPER IDENTIFICATION NUMBER (DOUBLE-CLICK HERE TO EDIT) < 8 (c) (c) (d) Fg. 8. Performance of lght tak. (a) proportonal factor; (b) utlzaton; (c) allocated cap; (d) reource uage. C. Robutne and Adaptablty Adaptablty of the allocaton algorthm alo teted. Total avalable bandwdth jump to 8 and 10 at the 30 th and 60 th chedulng perod, repectvely. Performance metrc are ued the ame a thoe n Fg 7-8, but here we only how reult wth heavy tak. (a) (d) Fg. 9. Reponvene to bandwdth change wth heavy tak. (a) proportonal factor; (b) utlzaton; (c) allocated cap; (d) reource uage. A hown n Fg. 9, durng the frt 30 expermental perod, reult are the ame a thoe ncluded n Fg. 7. After 30 perod when avalable bandwdth jump, our control method can react to bandwdth change very fat. From the perpectve of control theory, th varance n bandwdth can be condered a a dturbance, and Fg. 9 how that the robutne, or ant-dturbance capablty of our approach, atfactory. D. Parametrc Convergence and Stablty A a routne n control ytem degn, tablty analy ndpenable becaue only table ytem can be appled. We fnd that ytem tablty entve to ome parameter, e.g. CapScale n (7). A larger CapScale that reult n a rapd convergence to a teady tate may alo lead to performance ocllaton and ntablty. For example, when CapScale et to 8, lght applcaton wll not converge to a table value. In th cae, the control ytem cannot work n a table tate, a hown n Fg. 10. The reaon that even the mallet control cale already exceed requred control power, o the ytem are adjuted ether above or below and never reach the table tatu. (b) Fg. 9. (to be contnued)

9 > REPLACE THIS LINE WITH YOUR PAPER IDENTIFICATION NUMBER (DOUBLE-CLICK HERE TO EDIT) < 9 (a) (b) UC Cap (b) (c) Fg. 10. Performance of lght tak wth a large CapScale. (a) proportonal factor; (b) utlzaton; (c) allocated cap. Heavy tak are not a entve to a large CapScale a lght one nce ther tolerance to the mallet CapScale hgher. In Fg. 11, CapScale alo et to 8. Whle ome ocllaton occur, the magntude very mall. In our applcaton we et CapScale from 3 to 5 baed on our experence. PF app1 app2 app3 app4 app Schedulng Perod (a) Fg. 11. (to be contnued) (c) Fg. 11. Performance of heavy tak wth a large CapScale. (a) proportonal factor; (b) utlzaton; (c) allocated cap. E. Performance Comparon Several other reource allocaton cheme are developed n comparon, a hown n Table II: teratve and even tand for the way to allocate bandwdth among applcaton, n an teratve way a decrbed n Secton IV or jut to dvde the total bandwdth evenly; dynamc tand for CPU allocaton manner decrbed n Secton III, whle fxed mean that allocated CPU reource for each applcaton are contant. Obvouly, our approach belong to Cae 1. TABLE II. ALGORITHM SETTINGS Bandwdth CPU Cae 1 Iteratve dynamc Cae 2 Iteratve Fxed Cae 3 Even dynamc Cae 4 Even Fxed Some reult are provded n Table III where performance metrc from top to bottom are fnal throughput (.e., the volume of data proceed durng the experment, and TP n hort), CPU uage and bandwdth (BD n hort) uage n percentage. Character of H and L are abbrevaton of heavy and lght, ndcatng the type of applcaton. Stll experment are carred out for 100 perod and each perod lat for 100 econd. Therefore, each cenaro nvolve an upper lmt of 50, 100 and 150Gb of data tranfer, repectvely, correpondng to three total bandwdth ettng,.e., 5, 10, and 15Mb. Our algorthm preval n all the cenaro n Table III. For example, ung fewer reource, our algorthm obtan a hgher throughput. In the extreme cae,.e., Cae 4 where the bandwdth evenly allocated and CPU reource are fxed, the performance not atfactory depte the fact that ther reource conumpton ubtantal. The performance of Cae

10 > REPLACE THIS LINE WITH YOUR PAPER IDENTIFICATION NUMBER (DOUBLE-CLICK HERE TO EDIT) < 10 2 and 3 better than Cae 4, but tll not a good a our. Th reult quanttatvely valdate our earler aumpton that unlateral adjutment of bandwdth or CPU reource not uffcent enough to reach the goal of hgh throughput and hgh utlzaton of reource multaneouly. Furthermore t jutfe our methodology to co-chedule bandwdth and CPU reource n an ntegrated and coordnated way. Index Fnal TP (Mb) CPU Uag e (%) BD Uag e (%) TABLE III. PERFORMANCE COMPARISON Cae Total Bandwdth (Mbp) H L H L H L VI. RELATED WORK Stream proceng [8] ha been one of the major focue of databae reearch n recent year, and ome technque, e.g. Aurora [9] and TelegraphCQ [10], have been developed to deal wth contnuou quere on data tream. Thee ytem often deal wth lght-weght data,.e., the volume of each tem n the ncomng tream mall and uually n-memory proceng needed to yeld an extremely low latency. Our work focue on chedulng data treamng job on vrtualzed reource. Dtrbuted computng technque evolve from cluter, grd to cloud computng [11]. Reource management and allocaton ha been a key ue n thee area [12]. In cloud computng wth vrtualzaton technology a the key enabler, vrtual machne [13] or vrtual cluter [14] are bac unt n management, chedulng and optmzaton [15]. Tool ncludng Eucalyptu [16], VMPlant [17] and Uher [18] can erve th management purpoe. Some cheduler are developed to upport data treamng applcaton, e.g. GATES [19] and Streamlne [20], but they manly concern on computng reource allocaton wthout takng bandwdth nto account. Several project uch a EnLIGHTened [21], G-lambda [22] and PHOSPHORUS [23] put emphae on networkng reource. They have the total control over a dedcated optcal network o that a determntc path can be obtaned wth advance reervaton or on demand, whle our work ue a TCP/IP baed hared network, uch that bandwdth more of an ue. Streamflow [40] propoe a framework that enhance a tradtonal centfc workflow nfratructure to enable the tream proceng capablty, and reource allocaton not dcued. In [41] a dtrbuted and ppelned dataflow executon ytem propoed. The executon optmzed by explotng parallelm, load balancng and co-locatng. In our approach all the executon n a vrtual envronment and we have a unque requrement to harmonze computaton and bandwdth allocaton. Control theory ha been uccefully appled to optmze performance or qualty of ervce (QoS) for varou computng ytem. An extenve ummary of related work can be found n the frt chapter of th book [7]. Approache uch a proportonal, ntegral, and dervatve (PID) control [24][25], pole placement [26], lnear quadratc regulator (LQR) [26] and adaptve control [27][28] are appled n varou ytem. Mot of them requre a prece model of the controlled object(). Ung thee approache t nevtable to etablh dfferental equaton model that defne the ytem repone to nput. Th rather challengng n ome cae ncludng data treamng applcaton, due to varable couplng and heavly nonlnear property of the ytem. Fortunately, fuzzy control offer an alternatve, provdng a formal technque to repreent, manpulate and mplement human expert heurtc knowledge for controllng an object va IF-THEN rule. Fuzzy control doe not rely on mathematcal modelng of an object and ntead t etablhe a drect nonlnear mappng between nput and output. Th can gnfcantly reduce the dffculty of a control ytem degn n our applcaton cenaro. The frt applcaton of fuzzy control wa ntroduced nto ndutry n 1974 [29]. Fuzzy control [30][31] alo a reearch topc n computng ytem manly on admon control to get a better qualty of ervce. Adaptve fuzzy control appled for utlzaton management of perodc tak [32], where the utlzaton defned a the rato of the etmated executon tme to the tak perod. Fuzzy nference carred out to decde the threhold over whch the QoS of tak hould be degraded or even ncomng tak be rejected. In th work, executon tme etmaton mut be provded, whch not feable for ome applcaton. A recent tudy relevant to our work [33] focued on provdng predctable executon o a to meet the deadlne of tak. Vrtualzaton technology appled to mplement the o-called performance contaner and compute throttlng framework, to realze the controlled tme-harng of hgh performance computng reource. Sytem dentfcaton carred out to etablh the model of controlled object and a proportonal and ntegral (PI) controller ued. Th work ha mlar motvaton wth our, whle we adopt the model-free fuzzy control approach gven the nature of the condered data

11 > REPLACE THIS LINE WITH YOUR PAPER IDENTIFICATION NUMBER (DOUBLE-CLICK HERE TO EDIT) < 11 treamng applcaton. VII. CONCLUSION In th work we provde a new approach for co-chedulng and co-allocaton of vrtualzed reource for data treamng and proceng. Fuzzy logc control of CPU reource and teratve bandwdth allocaton are mplemented for ntegrated and coordnated reource management. Expermental reult how the good performance n reource utlzaton, data throughput and robutne of the approach, n comparon wth everal other method wth le adaptablty to dynamc envronment. In further work, bede CPU and bandwdth, we plan to nvetgate vrtualzed network and elatc torage allocaton. More complex cenaro, uch a workflow [34][42], ervce compoton [35], and economcal provonng [47] wll be condered. In thee cenaro VM can be ued for each tage n a proce and optmzed to acheve a balanced reource allocaton and thu a better overall performance. 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Workhop on Qualty of Servce, June, 2004.

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Pu, "Economcal and Robut Provonng of N-Ter Cloud Workload: A Mult-level Control Approach," Proc. IEEE Internatonal Conference On Dtrbuted Computng Sytem (ICDCS), Mnneapol, Mnneota, USA, BIOGRAPHIES Junwe Cao (M 99 SM 05) receved h Ph.D. n computer cence from the Unverty of Warwck, Coventry, UK, n He receved h bachelor and mater degree n control theore and engneerng n 1998 and 1996, repectvely, both from Tnghua Unverty, Bejng, Chna. He currently a Profeor and Vce Drector, Reearch Inttute of Informaton Technology, Tnghua Unverty, Bejng, Chna. He alo wth Tnghua Natonal Laboratory for Informaton Scence and Technology, Bejng, Chna. He now a Vtng Scentt of MIT LIGO Laboratory. Before jonng Tnghua Unverty n 2006, he wa a Reearch Scentt at MIT LIGO Laboratory and NEC Laboratore Europe for about 5 year. He ha publhed over 130 paper and cted by nternatonal cholar for over 2200 tme. He the book edtor of Cybernfratructure Technologe and Applcaton, publhed by Nova Scence n H reearch focued on advanced computng technologe and applcaton. Prof. Cao a Senor Member of the IEEE Computer Socety and a Member of the ACM and CCF. He alo a member of LIGO Scentfc Collaboraton. Wen Zhang (M 07) work n Chongqng Mltary Delegate Bureau, General Armament Department of PLA. He receved h PhD n control engneerng and applcaton from Department of Automaton, Tnghua Unverty, Bejng, Chna, n H reearch wa focued on dynamc control of data treamng and proceng. We Tan receved the B.S. degree and the Ph.D. degree n Control Scence and Engneerng, from Department of Automaton, Tnghua Unverty, Chna n 2002 and 2008, repectvely. He currently a reearch taff member n IBM T. J. Waton Reearch Center, USA. From 2008 to 2010 he wa a reearcher at Computaton Inttute, Unverty of Chcago and Argonne Natonal Laboratory, USA. H reearch nteret nclude ervce-orented archtecture, bune and centfc workflow, Petr net, workload optmzaton, bune ntellgence and data centrc computng. He ha publhed over 30 journal and conference paper. He Aocate Edtor of the IEEE Tranacton on Automaton Scence and Engneerng. He erved n program commttee of many nternatonal conference and wa the PC co-char of the Frt IEEE/ACM Workhop on the applcaton of Socal Networkng concept to Cluter, Cloud, Grd and Servce Computng (SN4CCGrdS). Contact hm at wtan@u.bm.com.