A Software System for Optimal Virtualization of a Server Farm 1

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1 БЪЛГАРСКА АКАДЕМИЯ НА НАУКИТЕ BULGARIAN ACADEMY OF SCIENCES ПРОБЛЕМИ НА ТЕХНИЧЕСКАТА КИБЕРНЕТИКА И РОБОТИКАТА, 60 PROBLEMS OF ENGINEERING CYBERNETICS AND ROBOTICS, 60 София 2009 Sofia A Software System for Optimal Virtualizatio of a Server Farm 1 Daiel Vatov Istitute of Iformatio Techologies, 1113 Sofia daiel.vatov@gmail.com 1. Itroductio The term virtualizatio broadly describes the separatio of a resource or request for a service from the uderlyig physical delivery of that service. With virtual memory, for example, computer software gais access to more memory tha is physically istalled, via the backgroud swappig of data to disk storage. Similarly, virtualizatio techiques ca be applied to other IT ifrastructure layers icludig etworks, storage, laptop or server hardware, operatig systems ad applicatios. This bled of virtualizatio techologies or virtual ifrastructure provides a layer of abstractio betwee computig, storage ad etworkig hardware, ad the applicatios ruig o it. The deploymet of virtual ifrastructure is odisruptive, sice the user experieces are largely uchaged. However, virtual ifrastructure gives admiistrators the advatage of maagig pooled resources across the eterprise. A key beefit of virtualizatio is the ability to ru multiple operatig systems o a sigle physical system ad share the uderlyig hardware resources kow as partitioig. 1 This work was supported by the Europea Social Fud ad Bulgaria Miistry of Educatio ad Sciece uder the Operatio Programme Huma Resources Developmet, Grad BG051PO001/07/3.3-02/7. 25

2 Today, virtualizatio ca apply to a rage of system layers, icludig hardware-level virtualizatio, operatig system-level virtualizatio, ad high-level laguage virtual machies. Hardware-level virtualizatio was pioeered o IBM maiframes i the 1970s, ad the more recetly Uix/RISC system vedors bega with hardware-based partitioig capabilities before movig o to software-based partitioig. For Uix/RISC ad idustry-stadard 86 systems, the two approaches typically used with software-based partitioig are hosted ad hypervisor architectures. A hosted approach provides partitioig services o top of a stadard operatig system ad supports the broadest rage of hardware cofiguratios. I cotrast, a hypervisor architecture is the first layer of software istalled o a clea x86-based system (hece it is ofte referred to as a bare metal approach). Sice it has direct access to the hardware resources, a hypervisor is more efficiet tha hosted architectures, eablig greater scalability, robustess ad performace. Virtual ifrastructure iitiatives ofte sprig from data ceter server cosolidatio projects, which focus o reducig existig ifrastructure box cout, retirig older hardware or life-extedig legacy applicatios. Server cosolidatio beefits result from a reductio i the overall umber of systems ad related recurrig costs (power, coolig, rack space, etc.) While server cosolidatio addresses the reductio of existig ifrastructure, server cotaimet takes a more strategic view, leadig to a goal of ifrastructure uificatio. Server cotaimet uses a icremetal approach to workload virtualizatio, whereby ew projects are provisioed with virtual machies rather tha physical servers, thus deferrig hardware purchases. Partitioig aloe does ot deliver server cosolidatio or cotaimet, ad i tur cosolidatio does ot equate to full virtual ifrastructure maagemet. Beyod partitioig ad basic compoet-level resource maagemet, a core set of systems maagemet capabilities are required to effectively implemet realistic data ceter solutios. These maagemet capabilities should iclude comprehesive system resource moitorig (of metrics such as CPU activity, disk access, memory utilizatio ad etwork badwidth), automated provisioig, high availability ad workload migratio support. 2. Problem descriptio While the large scale virtuaqliztio is gaiig speed, i may orgaizatios it is for first time whe they decide to cosolidate their server farms from physical deploymet to virtual machies. The problems that they face are: Is it worth doig this i our case? How much will it cost to virtualize the departmet X? What about departmets Y ad Z? How much we will save i terms of maiteace cost ad effort? The overall efficiecy of the solutio ad the cofidece of the customer that was chose the right strategy is a precoditio for the successful spread of the virtualizatio i the data-ceters. The optimizatio problem that should be solved ca be defied as: 26

3 A set of physical machies with differet OS is give ad applicatios ruig o them, owed by differet departmets i the orgaizatio, with differet importace for the successful daily operatios ad with differet maiteace costs. How these physical machies ca be cosolidated ad virtualized i optimal way, takig ito accout the orgaizatio's goals ad specific costraits? The strategy for virtualizatio that the compay may choose ca vary depedig o its goals. Below we shall describe several strategies but the real life scearios ca be more umerous. We defie for each physical machie that should be virtualized a value v. We ca have a sceario where v is: v = support effort. usefuless The machies that are takig too much effort for support but are cosidered less valuable for achievig orgaizatio's goals receive higher v. I this sceario they will be virtualized first which will improve the overall value for the server farm M (v i ), where M is the umber of machies i the farm. If we defie v as v = importace probably it is wise to cosider ot to have too much virtual machies with high importace o oe hypervisor. Thus i case of failure of a hypervisor the impact will be, hopefully, ot fatal. O the cotrary if the orgaizatio decides to address this risk by istallig redudat hypervisors it will be better if the most importat virtual machies are grouped together o less hypervisors thus reducig the cost of the solutio. Aother cosolidatio strategy may require to separate the physical machies i groups depedig o some predefied criteria. Such a criteria ca be departametal owership, geographical locatio, machie's architecture, operatig system, etc. 3. Theoretical backgroud To get proper uderstadig of the cosolidatio algorithm, we shall itroduce a importat class of combiatorial problems, kow as the kapsack problems. These types of problems are used to solve may practical problems like plaig, schedulig, resource allocatio. More details about the Kapsack Problems ca be foud i [2]. The classical 0-1 Kapsack Problem ca be defied as follows. There are objects ad a kapsack. Each object has a associated value ad a resource requiremet. For example these ca be price ad weight ad the kapsack ca carry ot more tha R kilograms. The goal is to pick a set of objects i such a way so that the overall value of all the items i the kapsack to be maximized without exceedig the capacity of the kapsack. 27

4 such that Mathematically the problem is stated as follows: maximize V = x i r i R, x i {0, 1}, i = 1, 2,...,. I the problem defiitio x i is a variable for object i. The problem is called 0-1 kapsack problem because the variable x i ca either take a value of 0 implyig object i is ot picked, or a value of 1 implyig object i is picked. Ay set of picked objects that satisfy the kapsack costraits is called feasible solutio of the problem. The solutio of 0-1 kapsack problem is the feasible solutio which maximizes the sum of the values of picked objects. If we relax the itegrality costraits the resultig problem is a liear program. This is the LP relaxatio of the kapsack problem (KP) deoted LP (KP). such that maximize V = x i r i R 0 x i 1, i = 1, 2,...,. Sice the costraits of LP(KP) are more relaxed tha i 0-1 KP V LP(KP) V KP it ca be used to obtai upper boud for KP solutio (See [2]). The classical 0-1 KP ca be geeralized for multiple resource costraits. Such a problem is called multi costrait kapsack problem or Multi Dimesio Kapsack Problem (MDKP). I MDKP each object requires m resources ad has value v. Resource requiremets are give by the resource vector r i = (r i1, r i2,...,r im ). The amouts of the resources available i the kapsack are give by R = (R 1, R 2,..., R m ). The MDKP problem is stated as follows: maximize V = x i v i x i v i x i v i such that x i r ik R k, k = 1, 2,..., m; x i {0, 1}, i = 1, 2,...,. 28

5 Aother geeralizatio of the kapsack problem that will be used is Multiple Choice Multi-Dimesio Kapsack Problem (MMKP). The problem defiitio is the same as for MDKP but the objects are grouped i differet stacks (groups). I geeral every group has differet umber of objects. A feasible pick for the kapsack ca cotai oly oe object from a group. The MMKP problem is defied as follows. The i-th group has l i objects. Object j of group i has value v ij, ad requires resource r ij = (r ij1, r ij2,..., r ijm ). The amouts of available resources are give by R = (R 1, R 2,..., R m ), such that maximize V = l i j =1 x ij v ij l i j =1 x ij r ijk R k, k = 1, 2,..., m, l i x ij = 1, i = 1, 2,...,, j =1 x ij {0, 1}, i = 1, 2,..., ; j = 1, 2,..., l i. The special case of MMKP, where there is oly oe resource costrait (i.e. m = 1), is called a Multiple-Choice Kapsack Problem (MCKP). Variats of kapsack problems comprise a importat class of combiatorial optimizatio problems. The classical 0-1 kapsack problem is oe of the most studied problems i operatios research or combiatorial optimizatio. Please refer to [2] for good surveys of algorithms for the kapsack problems ad some of their variats. Sice the 0-1 kapsack problems are NP-hard [2], the worst-case computatio time of the optimal solutios grows expoetially with the size of the problem. For this reaso, there are two types of solutios proposed for the KP ad its variats: optimal solutios, ad ear-optimal solutios. The objective of the ear-optimal solutios is to provide solutios which are close to optimal values, but require computatio times which are much shorter tha those of the optimal solutios. Most of the optimal algorithms for the variats of the KP uses a geeral search method, called the brach ad boud method Brach ad boud method for MMKP The brach ad boud method provides a popular approach for may variats of the KPs. This sectio describes shortly a extesio of the brach ad boud method for MMKP which was proposed by Kha [1]. At ay solutio state of the MMKP, a group is either free deotig o item is picked from this group, or it is fixed deotig a item is picked from this group. The fixed/free status of the groups are idicated usig the group status vector g. If group i is free, g i is 0; otherwise, the group is fixed ad g i is 1. The solutio state of a ode is represeted by a solutio vector x = {x ij } where i = 1,..., ad j = 1,..., l i. For a 29

6 fixed group i, if x ij = 1, it implies that item j is picked, ad otherwise x ij = 0 implyig that item j is ot picked. The brach ad boud algorithm for the MMKP ivolves the iterative geeratio of a search-tree. The basic algorithm works as follows: Step 1. Start with a solutio state where all groups are free. Compute the upper boud, select the brachig group, ad iitialize the search-tree with this ode as the oly live ode 2. Step 2. Fid the e-ode 3 e which is the live ode with the largest value of upper boud. Step 3. If ode e does ot have ay free group (i.e. all the groups are fixed), the this ode represets the optimal solutio, ad the algorithm termiates. Step 4. If ode e has at least oe free group, the this ode is expaded by fixig the brachig group b. Fixig group b ivolves the followig steps for each item j of this group: a) form a ew ode t where the picked items are the picked items of ode e ad the item j of group b, b) compute the upper boud at ode t, c) select the brachig group if there exists ay free group i t, d) if ode t is feasible, put ode t as a live ode ito the search-tree. Step 5. Go back to Step 2. The mai distictio of this algorithm from the well kow 0-1 brach ad boud method lies i dealig with groups of items. I [1] a pseudo-code of the brach ad boud algorithm for the MMKP is give Heuristics HEU Sice MMKP is a NP-hard problem, the computatio time for ay optimal algorithm, such as Bblp, may grow expoetially with the size of the problem istace i the worst case. This may ot be acceptable for time-critical applicatios such as admissio cotrol ad dyamic resource allocatio i a multimedia system. These applicatios are forced to accept a ear-optimal solutio if the computatioal time for optimal solutio exceeds real-time bouds. Here are the mai cocepts of the heuristic: It starts with a solutio where from each group the item with the smallest value (v ij ) is picked, ad iteratively improve the solutio by gradually replacig items of smaller values with those of larger values as log as the solutio remais feasible. It uses Toyoda s cocept of aggregate resource where the required resource vector of a item is coverted to a scalar idex usig pealty factors take from the curret resource usage vector [1]. Here the mai idea is to pealize the use of resources depedig o the curret resource state. It applies a large pealty for a heavily used resource, ad a small pealty for a lightly used resource. 2 A ode which has bee geerated ad whose childre have ot yet bee geerated. 3 Called also brachig or expadig ode. 30

7 To fid the ext item to be picked, it chooses the oe which maximizes the savigs i aggregate resource. But if o such item is foud, it chooses the oe which maximizes the value gai per uit of aggregate resource. A detailed algorithm ad pseudo-code procedure for HEU ca be foud i [1]. 4. Software system descriptio I this descriptio we will skip the trivial fuctioalities of the system ad will just eumerate them. The focus will be put o the fuctioality cocerig the cosolidatio ad the applicatio of the MMKP algorithms i this process. The system i which will be itegrated the MMKP algorithms is VMWare's Capacity Plaer 4. At the core of VMware Capacity Plaer 5 is the Iformatio Warehouse, which cotais a growig set of idustry referece data. This iformatio ca be leveraged for comparative aalysis ad bechmarkig to help guide system cosolidatio ad capacity optimizatio decisios for the eterprise. VMware Capacity Plaer is used as a busiess aalysis, plaig ad decisio support tool to direct the key phases withi a variety of ifrastructure assessmet projects as described below: Assess the curret workload capacity of a IT ifrastructure through discovery ad ivetory of IT assets. Measure system workloads ad capacity utilizatio across various elemets of the IT ifrastructure - icludig by fuctio, locatio ad eviromet. Pla for capacity optimizatio through utilizatio aalysis, bechmarkig, tredig ad idetificatio of capacity optimizatio alteratives. Idetify resources ad establish pla for virtualizatio, hardware purchase or resource redeploymet. Decide o the optimal solutio by evaluatig various alteratives through sceario modelig ad what-if aalysis. Determie which alterative best meets the pre-defied criteria. Moitor resource utilizatio through aomaly detectio ad alerts based o bechmarked thresholds. Help geerate recommedatios to esure ogoig capacity optimizatio. The sub-system that eables access to the gathered data ad is used for maagig the cosolidatio ad assessmet process is web based J2EE applicatio. The assessmet is cofigured i a five-step wizard where all the ecessary iformatio is gathered. The parameters that are cofigured are: Geeral iformatio as ame, owership, access. Iput systems that should be cosidered for cosolidatio. Cosolidatio settigs o Is it allowed systems to cosolidate oto the same hardware whe the systems have differet departmets, eviromets, fuctios, locatios or operatig systems? o Should the cosolidated systems be virtualized? 4 The opiios ad ideas expressed here do ot represet the official opiio of VMWare Ic

8 o Is it allowed to cosolidate systems with differet processor architectures oto the same hardware? o Should the systems be redeployed oly o ew hardware or to use the old hardware whe possible. o Level of details summary or detailed iformatio for each virtual machie The type of the hypervisor hardware ad the specific parameters memory, cpu, etc. The boudaries for the cosolidatio cosolidate over locatio, eviromet, fuctio, etc. Miimal limits uder which machies will ot take part i the assessmet process. For the purpose of itegratig MMKP algorithms i the cosolidatio egie this wizard will be exteded to allow supplyig also: Value for each machie. The sematics of the values will deped o the cosolidatio strategy chose. Sice umber of servers may be quite big the value will be assiged through regular expressio besides eumeratig each server idividually. For example v = 50 if (software ivetory cotais postgres ad departmet equals Accoutig ). Threshold for the total value of the virtual machies ruig o the hypervisor servers. This threshold will be used for strategies that do ot cosolidate all the physical machies. Curretly the attributes that are used for classificatio are: locatio, fuctio, eviromet, departmet ad OS. The classificatio is doe to separate the iput servers producig i this way several cosolidatio task - oe per group. To allow groupig of the physical machies i the sese of MMKP problem the curret groups will be exteded with a free slot for a group with user chose sematics e.g. importace of the server. The curret fuctioality providig alterative sceario geeratio will be exteded to iclude the ew parameters. To illustrate usage lets cosider the case where a virtualizatio have to take place i a compay whose IT ifrastructure cosists of 200 servers owed by three departmets. The servers are situated i two geographical locatios. The software that rus o the servers vary from databases to itraet sites. The utilizatio of the servers also vary i terms of average ad peak values. Part of the assessmet settigs for this compay are: Do ot cosolidate servers i differet geographical locatios; Group the machies by their departametal owership. The cosolidatio egie will solve two MMKP problems oe per a geographical locatio. For each MMKP problem the physical servers will be separated i groups accordig to their departametal owership. Lets assume that the cosolidatio strategy selected by the compay assigs higher values to the machies with lower importace for the orgaizatio but with higher maiteace costs. I this way the compay will put the cosolidatio threshold to such a value that will esure the expeses doe for the ew equipmet ad maiteace costs spread over oe year period will be less tha the curret sell price of the old 32

9 hardware (or recyclig costs) ad its maiteace costs. Groupig the machies by departmets esures that from the savigs will beefit all departmets. 5. Coclusio This article preseted the straightforward applicatio of MMKP problem to model the cosolidatio process. While usig real-life scearios the model is ot as rich as it ca be. Future extesio to the model may iclude the iformatio for the performace load durig the differet hours of the day. This will provide differet optimal cosolidatio solutios for each time iterval. There exist software products allowig dyamically, without stoppig the virtual machie, to move it from oe hypervisor to aother. A algorithm tryig to fid the optimal or ear to the optimal solutio to the cosolidatio problem may beefit from this. O the other had the model is quite simple ad ca be easily itegrated with other legacy software systems. For example it ca beefit from a ERP system to determie i automatic way the values for v for each machie i the server farm. R e f e r e c e s 1. M d. S h a h a d a t u l l a h K h a, C. F l m d S h a h a d a t u l l a h K h a. Supervisors Dr. Ki F. Li, Dr. Eric, ad G. Maig. Quality Adaptatio i a Multisessio Multimedia System: Model, Algorithms ad Architecture. Techical Report, M a r t e l l o, S., P. T o t h. Kapsack Problems: Algorithms ad Computer Implemetatios. Techical Report, Софтверная система для оптимальной виртуализации серверной фермы Даниел Ватов Институт информационных технологий, 1113 София daiel.vatov@gmail.com (Р е з ю м е) В работе представлено применение мультидименсионной задачи рюкзака с множеством выборов. Когда используются сценарии реальной жизни, модель не так богата. Расширение модели может включить информацию для поведения нагрузки в разных часах сутoк. Это дает оптимальные решения в каждом интервале времени. Существуют софтверные продукты, которые позволяют динамично, не останавливая виртуальную машину, переходить с одного хипервайзера к другому. Алгоритм, который ищет оптимального или вблизи оптимального, решения задачи, может воспользоваться этим. С другой стороны, модель очень простая и может быть легко интегрирована с другими софтверными системами. 33