Capacty Plannng for Vrtualzed Servers Martn Bchler, Thoas Setzer, Benjan Spetkap Departent of Inforatcs, TU München 85748 Garchng/Munch, Gerany (bchler setzer benjan.spetkap)@n.tu.de Abstract Today's data centres offer any dfferent IT servces ostly hosted on dedcated physcal servers. Vrtualzaton provdes a techncal eans for server consoldaton leadng to ncreased server utlzaton. The ter refers to the abstracton of coputng resources across any aspects of coputng and has been used to descrbe dfferent technques. Vrtualzaton engnes allow hostng ultple vrtual servers (ncludng operatng syste plus applcatons) on a sngle physcal server, and soetes to grate and dynacally allocate these vrtual servers to physcal servers on deand. Ths allows for uch fleblty n capacty anageent. Ths artcle presents a nuber of capacty plannng probles n the presence of vrtualzed IT nfrastructures and decson odels to allocate these vrtual servers optally. Based on a data set of CPU traces fro a data centre provder, we present the results of eperents usng dfferent proble forulatons. Introducton Nowadays data centers host ost of ther servces (e.g., ERP odules, databases or Web servers) on dedcated physcal servers. The cople resource requreents of enterprse servces and the desre to provson for peak deand are reasons for usng hgh-capacty physcal servers. As a consequence, server utlzaton s typcally very low, whch ncurs hgh nvestents and hgh operatonal cost. The Gartner Group estates that the utlzaton of servers n a typcal copany s around 20 percent []. Vrtualzaton can be used as techncal eans for server consoldaton. Benefts are hgher server utlzaton levels, reduced te for deployent, easer syste anageent, and overall lower hardware and operatng costs. Whle tradtonal capacty plannng lterature heavly reles on queung networks and dscrete event sulaton, new types of capacty plannng probles arse n the presence of vrtualzaton. Essentally, an IT servce anager needs to decde whch servces should be hosted on whch servers. In case of volatle deand, these decsons need to be ade dynacally by a controller, n order to ake sure enough capacty s avalable for each servce at each pont n te. In ths paper we present a set of capacty plannng probles for vrtualzed IT nfrastructures. We consder the proble of an IT servce provder hostng the IT servces of ultple custoers. Based on the users deands the IT servce provder needs to deterne the parttonng of servers usng pre-defned objectve functons (e.g., nzng the aount of servers used). We have developed a set of decson odels and a software pleentaton to deterne optal allocatons of applcaton servces or vrtual servers resp. to physcal servers. The paper s structured as follows: In secton 2 we contnue wth a bref overvew of vrtualzaton technques. In secton 3 we ntroduce three capacty plannng probles and adequate optzaton odels. In secton 4 and 5 we evaluate two of the proposed odels usng ndustry data. In secton 6 related work s dscussed. In secton 7 conclusons are drawn and future work s dscussed.
2 Vrtualzaton Technology Multple approaches allow for server vrtualzaton: SMP (syetrc ultprocessor) servers can be subdvded nto fractons, each of whch s actng as a sngle server and able to run an operatng syste. Ths s often descrbed as physcal or logcal hardware parttonng. Eaple products are HP npar, or IBM DLPAR. Software vrtualzaton ncludes approaches on or below the operatng syste level, or on the applcaton level. So called hypervsor software such as Lnu VServer, Mcrosoft Vrtual Server, Parallels, Queu, VMware, and XEN create vrtual servers whose physcal resource use s dynacally adjustable, enablng ultple solated and secure vrtualzed servers on a sngle physcal server. Accordng to IDC server spendng around vrtualzaton s a rapdly growng arket that wll aount to nearly US$ 5 bllon by 2009 [2]. 3 Proble Forulatons 3. Statc Server Allocaton Proble A basc server consoldaton proble s the Statc Server Allocaton Proble (SSAP). Here, the IT servce anager needs to consoldate servers and assgn servces (.e., vrtual servers) to (physcal) servers so as to nze the nuber of servers used or nze overall server costs, respectvely. Suppose that we are gven n servces j J that are to be served by servers. For splcty, we assue that no one servce requres ore capacty than can be provded by a sngle server. Custoers order u j unts of capacty (e.g., SAPS) and each server has a certan capacty s. For safety reasons, s ght be set below the physcal capacty lt. y s a bnary decson varable ndcatng whch servers are used, c descrbes a potental cost of a server, and j descrbes whch servce s allocated to whch server. The proble can be forulated as an nstance of the well-known bn packng proble (see equatons ()). n s. t. = y, c y = j n j = j u j = j s y {0,} J Bn packng s known to be NP-hard. However, polynoal and lnear te approaton algorths est. For eaple, the frst ft decreasng algorth orders the tes fro largest to sallest, then places the sequentally n the frst bn n whch they ft, and can be pleented n O(n log n). Johnson et al. showed that ths strategy s never suboptal by ore than 22%, and furtherore that no effcent bn-packng algorth can be guaranteed to do better than 22% [3]. In addton to the basc forulaton n (), often addtonal sde constrants have to be consdered. For eaple, syste adnstrators want to ake sure that a partcular servce j s only allocated to one out of a restrcted set K of k servers, e.g., runnng a certan operatng syste (2) or that two servces j and l are not allocated to the sae server for soe reason (3). k = = j (2) j + l (3) Fed allocatons of servces to servers can be done n a pre-processng step where the servces are assgned anually and the capacty of the respectve servers s decreased accordngly. Vrtualzaton engnes such as VMware allow to set lower and upper bounds for the capacty ()
allocated to a vrtual server (.e., servce). The nu capacty s guaranteed, and the vrtual server s workload can fluctuate up to a au capacty. Ths nterval between nu and au capacty s shared wth other servces. Therefore, the optzaton proble should assgn those servces on a physcal server that have ther deand,.e. workload peaks at dfferent tes of the day or week. Typcally, servce deand of ths sort has seasonal patterns on a daly or weekly bass. For eaple, the payroll accountng s done at the end of every week and requres specfc servces, whle deand for an OLAP applcaton s constant durng a week, but has an everyday peak n the ornng hours, when anagers access ther daly reports. Suppose, te s dvded nto a set of dscrete te steps T ndeed by t={,, τ }. The workloads vary over te, so that we get a separate atr u jt descrbng how uch capacty servce j requres n te step t. Ths atr can for eaple be generated by takng the au deand value of each te step n a workload trace. Based on ths atr we forulate the Statc Server Allocaton Proble wth varable workload (SSAPv) n (4). n s. t. = y, = c y n j= j u j jt = j {0,} 3.2 Dynac Server Allocaton Proble s y J τ Data centre vrtualzaton also allows servces to be grated over te to dfferent servers. For eaple, the dynac workload balancng use case n [4] descrbes how servers can be dynacally nstalled and de-nstalled dependng on changng servce workload. The Dynac Server Allocaton Proble (DSAP) descrbes the decson of how any servers are requred overall n a plannng perod (say a week) and how servces are allocated to servers n the ndvdual te steps (e.g., 3 hour te slots of the day). n s. t. = jt = c y n j= u jt jt = jt, y {0,} s y In addton, however, the adnstrator ght want to nze the nuber of reallocatons over te. The bnary decson varable z jt deternes double the aount of reallocatons (each re-allocaton s counted twce). Ths way, a syste adnstrator can lt the nuber of re-allocatons fro one perod to another to p (6). z {2,..., τ} z jt jt jt jt jt z jt jt n τ z = j= t= 2 jt {0,} 2 p {2,..., τ} (4) (5) (6)
The nuber of servers an IT servce provder can save usng data centre vrtualzaton has been one of the an sales arguents for software vendors n ths feld. Qute obvously, ths depends very uch on the level of nterference of the dfferent workload traces (.e., te seres). The above DSAP helps to quantfy how uch can actually be saved copared to dedcated server hostng based on hstorc workload data. 4 Descrpton of Data Fro a professonal data centre we obtaned workload traces contanng the CPU utlzaton (easured n so called perforance unts, PUs) of 30 dedcated servers hostng dfferent types of applcaton servces (e.g., web servers, applcaton servers and databases). The data descrbes fve nute average values durng a onth. Most of these workload traces ehbt cyclc patterns on a daly bass. Thus, we consder daly workload fluctuatons. As the nuber of te steps consdered drectly pacts the nuber of varables n SSAPv and DSAP, we aggregated the 5 nute average values and calculated an estator for the hourly CPU workload requreents to lower the nuber of varables. For each servce we deterned the au workload of the 5 nute ntervals n an hour based on a saple of ultple days and deterned ths as our estator for {u jt }. Ths s a rather conservatve estator, whch s based on the type of servce and the rsk atttude of the data centre provder. If provders are wllng to overbook resources to a certan degree they can lower u jt based on addtonal statstcal consderatons. All decson odels n Secton 3 are bnary progras. In partcular, SSAPv and DSAP ght becoe ntractable for practcal proble szes wth a large nuber of servces and te steps consdered. There are several strateges to solve the above entoned decson probles n practcally acceptable tes.. Decrease the nuber of te steps consdered per day. 2. Pre-select servces wth destructve nterference by dentfyng negatvely correlated workload traces usng for eaple covarance atrces or Prncple Coponent Analyss. 3. Use heurstcs or eta-heurstcs such as Genetc Algorths to solve the proble. 5 Eperents and Results Based on the data fro our ndustry partner we conducted eperents wth SSAP and SSAPv wth respect to runte and nuber of servers assgned. SSAPv takes nto account the hourly workload varatons durng a day u jt, whle SSAP only consders the au workload u j of a day n order to satsfy the workload deands durng a day. We consdered four dstnct scenaros of dfferent szes A (8 servces), B (5 servces), C (26 servces), and D (30 servces). Each scenaro represents an arbtrarly chosen subset of servces fro our data set. We assued that all servers have dentcal capacty and cost (e.g., a rack of hoogeneous blade servers). Usng the Frst Ft approach for the SSAP for each scenaro an upper bound for the nuber of servers was obtaned to host each servce. Based on ths upper bound for the nuber of servers, the nuber of decson varables and the nuber of LP-constrants are derved for the SSAP and the SSAPv. Table llustrates characterstcs of the respectve bnary progras. SSAP and SSAPv were solved optally for each of the four scenaros. SSAP solves the proble usng fewer servers than the Frst Ft approach n three of four cases. SSAPv further reduces the nuber of requred servers copared to SSAP n three of four cases (see Fg. ). The larger the scenaros, the ore can be ganed by optzaton forulatons. Further proveent ay be acheved by pre-selectng servces that ehbt strong copleentartes.
Scenaro (# Servces) No. of decson varables No. of constrants SSAP No. of constrants SSAPv A (08) 27 80 B (5) 64 9 C (26) 26 34 28 D (30) 279 39 246 Table : Proble szes of dfferent scenaros Nuber of Requred Servers No. of Servers 0 9 8 7 6 SSAP (Frst Ft) 5 SSAP (optal) 4 SSAPv 3 2 0 8 5 26 30 Nuber of Servces Fg. : Server requreents dependng on server allocaton ethod All calculatons were perfored on a laptop wth an AMD Athlon XP 2600+ processor,.9 GHz, wth 52 MB RAM usng a COTS branch-and-bound IP solver (Frontlne ). The branchand-bound algorth ncluded strong branchng (deternaton of the net varable to branch upon based on pseudo costs calculated fro the dual sple) and probng (settng certan bnary nteger varables to 0 or and dervng values for other bnary nteger varables, or tghtenng bounds on the constrants). The eperental results n Fg. llustrate the potental cost savngs of takng workload patterns nto account. Of course, the ore cople forulaton SSAPv ncurs hgher coputatonal costs. Optal solutons for the SSAP were calculated wthn less than 30 seconds for all four scenaros. Plus, there est polynoal te schees for ths proble (see Secton 3.).. SSAPv took uch longer. Dependng on the paraeter settngs, the deternaton of the optal soluton took 5 nutes to several hours. More etensve senstvty analyses need to be done here. 6 Related Work A nuber of approaches found n lterature address resource allocaton probles as descrbed n ths artcle. Urgaonkar et al. [5] analysed the overbookng of shared resource pools by eplotng knowledge about deand and workload profles (statstcal ultpleng). The authors analysed best-ft, worst-ft and rando placeent heurstcs to bundle copleentary servces on coon servers. Seltzsa et al. descrbe a syste called AutoGlobe for servce-orented database applcatons [6]. Aong others the syste ncludes coponents for load ontorng, and algorths for statc plannng and dynac capacty anageent. The statc allocaton heurstc deternes an ntal schedule balancng the workload across the servers, whle a fuzzy controller s used to handle overload stuatons based on fuzzy rules provded by syste adnstrators. Rola et al. [7] also propose an ntal approach to assgn applcatons to servers usng an nteger progra. The authors statstcally characterze the deand profles of busness applcatons based on hstorcal traces and projectons, provdng overall bounds on applcaton resource requreents.
Rola et al. take the traces of applcaton deand as nput and assgn the to servers by buldng deand profles based on hstorcal observatons and present two approaches to assgn the to a sall set of servers usng a Genetc Algorth [7]. In the R-Opus fraework, Cherkasova et al. present a Workload Placeent Servce based on statstcal ultpleng [8]. The total aount of servers s decreased by usng genetc algorths that penalze low utlzatons of resource pools as well as resource pools wth nsuffcent capacty. Our contrbuton s the proposal and coparson of dfferent allocaton approaches for vrtualzed servers. Senstvty analyses are perfored regardng coputatonal costs, the qualty of dfferent soluton algorths and dfferent proble szes. 7 Conclusons and Future Work Vrtualzaton enables uch fleblty for capacty anageent n a data centre. In ths paper, we have proposed three decson odels for capacty plannng probles that can be found n vrtualzed IT nfrastructures. We have pleented the SSAP and the SSAPv and provded frst eperental evaluatons based on workload traces fro an ndustry partner. In our future research we plan to evaluate a larger set of workload traces fro our ndustry partner and cluster the based on the type of applcaton. We plan to work on the developent of heurstcs and the applcaton of eta-heurstcs such as Genetc Algorths to solve SSAPv and DSAP for uch larger proble szes. In addton, we want to do senstvty analyses wth respect to the sze of the te steps used ( hour vs. 3 hour te steps) and develop further approaches to aggregate the workload traces based on the decson akers rsk atttude and nforaton n servce level agreeents. Pre-selecton of prosng canddate servces based on covarance of workload traces s another approach, wth whch we epect to prove the results further. References [] K. Parent, "Server Consoldaton Iproves IT's Capacty Utlzaton" Vol. 2006: Court Square Data Group, 2005. [2] IDC-Press, "Increasng the Load: Vrtualzaton Moves Beyond Proof of Concept n the Volue Server Market.", 2005. [3] D. Johnson, A. Deers, J. Ullan, M. Garey, and R. Graha, "Worst-Case Perforance Bounds for Sple One-Densonal Packng Algorths" SICOMP, Vol. 3, 974. [4] M. Mssbach and J. Stelzel, Adaptve Hardware-Infrastrukturen für SAP: SAP Press, 2005. [5] B. Urgaonkar, P. Shenoy, and T. Rescoe, "Resource Overbookng and Applcaton Proflng n Shared Hostng Platfors" presented at Usen OSDI, 2002. [6] S. Seltzsa, D. Gach, K. Kropass, and A. Keper, "AutoGlobe: An Autoatc Adnstraton Concept for Servce-Orented Database Applcatons" presented at 22nd Internatonal Conference on Data Engneerng (ICDE 2006), Atlanta, 2006. [7] J. Rola, A. Andrzejak, and M. Arltt, "Autoatng Enterprse Applcaton Placeent n Resource Utltes" presented at Workshop on Dstrbuted Systes: Operatons and Manageent, Hedelberg, 2003. [8] L. Cherkasova and J. Rola, "R-Opus: A Coposte Fraework for Applcaton Perforablty and QoS n Shared Resource Pools" Hewlett-Packard Labs, Palo Alto, 2006.