Availability Enhancement for Cloud Services by Migration based Rejuvenation: Analytical Modeling

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1 3rd Iteratioal Coferece o Computatioal Techiques ad Artificial Itelligece (ICCTAI'2014) Feb , 2014 Sigapore Availability Ehacemet for Cloud Services by gratio based Rejuveatio: Aalytical Modelig Pa Pa Hlaig, ad Thadar Thei Abstract Virtualizatio is a core techology i cloud computig ad virtual machie migratio is a powerful tool to facilitate system maiteace, load balacig, fault tolerace, ad power-savig. As cloud services have bee widely used ad most of the cloud services are ruig o top of the virtual machie (VM), software agig i VM is a challegig issue ad high availability assurace of VMs becomes a sigificat cocer. Whe a applicatio goes cotiuously, VM performace will degrade ad failure rate will icrease due to software agig. VM rejuveatio, VM migratio is a promisig techique for ehacig the availability of cloud services as it ca postpoe or prevet the software agig i VM. Whe applicatio ruig o VM eeds to be rejuveated the hosted VM ca be migrated to aother VM o other hosts usig VM migratio ad cotiue to provide cloud services. I this paper, the effectiveess of VM rejuveatio is ivestigated by Markovia modelig. Numerical examples are preseted to illustrate the applicability of the model. Keywords Availability, Cloud Services, Markov Model, Rejuveatio, Software Agig, VM gratio N I. INTRODUCTION OWADAYS, Cloud computig services are becomig the primary source of computig power for both eterprises ad persoal computig applicatios. A cloud computig platform ca provide a variety of resources, icludig ifrastructure, software, ad services, to users i a o-demad fashio [13]. To access these resources, a cloud user submits a request for resources. The cloud provider the provides the requested resources from a commo resource pool ad allows the user to use these resources for a required time period. Compared to traditioal approaches, cloud computig services elimiate the costs of purchasig ad maitaiig the ifrastructures for cloud users [7]. Virtualizatio techology plays a key role i cloud computig platform sice it makes it possible to sigificatly reduce the umber of physical servers i cloud data ceters by havig each server host multiple idepedet virtual machies Pa Pa Hlaig, versity of Computer Studies, Yago, Myamar id: ms.papahlaig@gmail.com, Thadar Thei, versity of Computer Studies, Yago, Myamar. ( thadarthei@gmail.com). (VMs) maaged by a Virtual Machie Moitor (VMM) ofte referred to as a Hypervisor. Virtualizatio brigs some beefits like better utilizatio of resources ad fault tolerace [4]. A importat feature for virtualized cloud systems is the ability to move virtual machies (VMs) from oe physical host to aother. This characteristic is called VM migratio [3]. May orgaizatios ad busiesses which rely o cloud computig platforms require early uiterrupted service. Therefore system availability is a importat cocer for cloud platforms. I this cotext, software rejuveatio is a auspicious techique to achieve high availability [5]. I cloud eviromets the VMs ru o hypervisor ad applicatios ru o hosted VMs. These compoets are liable to suffer failures or hags due to software agig. Several studies have reported that the uavailability of servers more ofte origiates from software faults rather tha hardware faults. Whe software applicatios executig cotiuously for a log period of time, their performace degraded i rate ad icreased occurrece rate of hag/crash failures [16]. I this situatio, VM rejuveatio mechaism ca be performed as a fault prevetio actio ad has bee widely used to avoid the occurrece of uplaed failures. For assurig high availability ad reliability of systems from structural perspective, model-based assessmet has bee applied to may egieerig domais. State-space models such as Cotiuous Time Markov Chai (CTMC), semi-markov process ad Stochastic Petri Net (SPN) have bee used widely for evaluatig the performace [10], reliability/ availability [1], ad performability [8] of computer systems. I this paper, we costruct availability model to assess the steady state availability of the virtualized cloud system cosiderig the effect of VM rejuveatio. The rest of the paper is orgaized as follows. I sectio II we discuss the related work. Sectio III describes the architecture of the virtualized cloud system aalyzed i this paper, whereas Sectio IV presets the availability modelig. Model aalysis through the umerical results discuss i Sectio V. Fially we coclude our paper i Sectio VI. II. RELATED WORK There have bee a lot of research works to assess the availability of the system with rejuveatio. Matheus et al. [12] proposes a comprehesive availability model of a cloud 45

2 3rd Iteratioal Coferece o Computatioal Techiques ad Artificial Itelligece (ICCTAI'2014) Feb , 2014 Sigapore computig eviromet with time-based rejuveatio supported by the live migratio mechaism. They evaluate the impact that differet rejuveatio policies based o live migratio produced o the steady-state availability. F.Salfer ad K.Wolter [6] ivestigated the effect of three time-triggered system rejuveatio policies o service availability usig a queuig model. They defied a metric for steady-state availability usig combiatio of simulatio ad aalytical reasoig. They aalyzed time-to-failure of systems with rejuveatio. The author, Risaka [9] described a fault-tolerat software system with two versio redudat structure ad radom rejuveatio schedule, ad evaluated quatitatively a depedability measure like the steady-state system availability. They developed CTMC model with redudacy ad rejuveatio, by takig ito accout of the failure correlatio o the failure property betwee two software systems. The authors [15] preseted a mixed software rejuveatio policy for a operatioal software system with multiple degradatio states, which cosidered both the history iformatio ad the curret ruig state. By this policy, the system was rejuveated whe it achieved to a degradatio threshold or it came to pre-determied rejuveatio iterval. Some studies icorporated software rejuveatio for VM ito availability model ad computed the dow time cost or steady state availability of the system [2], the research paper [14] provided stochastic process based models to evaluate availability of the system i case of without virtualizatio techology ad i case whe virtualizatio ad software rejuveatio were used. I this paper, we discussed aalytical models for evaluatig the effectiveess of software rejuveatio i virtualized cloud system which experiece software agig. The aim of the aalytical modelig approach was to access the steady state availability determiig the times to trigger rejuveatio icorporate with VM migratio. III. SYSTEM ARCHITECTURE This study cosiders a system with -physical machies. Physical machie (PM) are sometime called physical hosts ad each host cotai a VMM which rus the VMs with desired applicatios, oe maagemet server which is a compoet resposible for cotrollig the etire cloud eviromet by meas of cloud maagemet tool ad maagemet server eeds to be up ad ruig, because it cotrols the whole eviromet. There is a remote storage volume which is accessed by the VMs ad maaged by the maagemet server. The virtualized cloud system architecture is preseted i the Fig. 1. Sice software applicatios o VMs execute cotiuously for log period of time the processes correspodig to the software i executio age or slowly degrade their performace. Whe oe of the VM o the physical host degrades performace because of executio age, rejuveatio activities will be scheduled. If performace degradatio rate is slow, VM is i applicatio level rejuveatio state ad at that state the VM will be rejuveate with some activities such as clea the iteral state or service restart. If performace degradatio rate is high, its state reaches system level rejuveatio state, that we refer migratio sate ad at that state VM should be migrated to aother host to become a healthy oe. We assume that migratio decisio such as which host VM should be migrated is decided by maagemet server. Fig. 1 Virtualized cloud system architecture for cloud services IV. AVAILABILITY MODELING I this sectio, we preset our proposal to ehace cloud service availability by applyig VM rejuveatio mechaism. Log ruig cloud applicatio ca occur Software agig. First, we study the agig behavior of the cloud service applicatios which are ruig o VMs. The we costruct a state trasitio model to measure the availability of the system. The markov chai state trasitio diagram for the system is show i Fig. 2. I the model, there are five states: Up State (U i ), Degradatio State (D i ), Applicatio Level Rejuveatio State (R i ), System Level Rejuveatio State (grate state) (M i ) ad Failure State (F). Iitially the VM is fuctioig i the iitial Up State, U 1. As time progresses, VM performace will be degraded ad state may chage from the U 1 state to D 1 with rate λ d. If VM performace degradatio is low, its state reaches R 1 state with triggerig rate λ r. At that state, applicatio level rejuveatio activities are performed ad state chage form R 1 state to U 1 agai with rate µ r. Whe VM performace degradatio rate is very high, state will eter M 1 state with rate λ s ad VM will be migrated from oe physical host to aother with rate λ m. There is o physical host that ca accept the VM, all services ruig o the VM will eter failure state with rate λ. After the VM has bee recovered with rate µ, it will become Up State agai. As a model assumptio, migratio probabilities are calculated by maagemet server based o their capacity of physical hosts. We also assume that Sojour time i all the state of the system is expoetially distributed. 46

3 3rd Iteratioal Coferece o Computatioal Techiques ad Artificial Itelligece (ICCTAI'2014) Feb , 2014 Sigapore Fig. 2 State trasitio diagram for the system We defie the steady-state probabilities of the system as follows: Probabilities i the up state: P ; Probabilities i the degradatio state: P Di ; Probabilities i the applicatio level rejuveatio state: P Ri ; Probabilities i the system level rejuveatio state or migratio state: P ; Probabilities i the failure state: P F ; where i= the umber of operatioal VMs We compute the steady-state probability by writig dow the steady-state balace equatios as follows. For state P U1, P d U1 mp i2 For state P ( i=2,3,,), P d mp i2 For state P Di ( i=1,2,,) r Di D P mp r R1 P r F P P P (3) Ri (1) (2) For state P F P (7) P F The coservatio equatio of Fig. 2 is obtaied by summig the probabilities of all states i the system ad the sum of equatio is 1. i1 P P P P P 1 (8) i1 Di i1 Ri i1 Combiig the above metioed balace equatios with the coservatio equatio, ad solvig these simultaeous equatios, we acquire the closed-form solutio for the system. d P Di P (9) r d P Ri P s r (10) (11) 2 s d P P m s r P F P (12) F For state P Ri ( i=1,2,,) s r PRi r PDi (4) For state P ( i=1,2,,-1 ) i mpm ( i 1) P (5) s R( i1) 1 P d d s d i i i r s r m s r s d 2 s d (2 ) m s r m s r V. MODEL ANALYSIS 1 (13) For state P M ( i= ) P i mpm i P s Ri ( 1) 1 (6) A. Availability ad Dowtime Aalysis Availability is a probability of a system which provides the services i a give istat time. I our model, services are ot 47

4 3rd Iteratioal Coferece o Computatioal Techiques ad Artificial Itelligece (ICCTAI'2014) Feb , 2014 Sigapore available whe VM is i applicatio level rejuveatio state (Ri), system level rejuveatio state, migratio state, () ad fail state (F). Availabili ty 1 Uavailability (14) Availabili ty 1 PRi P PF i 1 i 1 (15) Automated Reliability ad Performace Evaluator ad it is a well kow package i the field of reliability ad performability aalysis of the system. Dowtime is the expected total dowtime of the applicatio with rejuveatio i a T time uits is Dowtime T * PRi P PF i 1 i 1 (16) B. Numerical Results I order to aalyze the availability of the system, we perform umerical aalysis usig the followig parameter values show i TABLE I. Fig.4 Availability vs. Differet VM Degradatio Rate ad Rejuveatio Trigger Rate Fig. 4 illustrates the availability chages for the proposed model with 3 physical hosts system. The ifluece of VM degradatio rates ad rejuveatio trigger rates o availability is show. The rejuveatio trasitio firig rates λr are assumed 1 time/3 days ad 1 time/4 days. It ca be observed that the rejuveatio trigger rate icreases for VM, the higher availability ca be achieved. TABLE I PARAMETER VALUES Parameter Descriptio Value (hr-1) λd λr λs 1/λm 1/ r λ VM degradatio rate VM rejuveatio trigger rate VM migratio trigger rate migratio time rejuveatio time failure rate repair rate 1 time / week 1 time / 3days 1 time / day 30 sec 10 sec 3 time / moth 1 time / hrs For example, we assume that there are 3 physical hosts i our system ad the state trasitio diagram of 3 physical hosts system is modeled i Fig. 3. Fig.5 Dowtime vs. Differet VM Degradatio Rate ad Rejuveatio Trigger Rate Fig. 5 plotted the dowtime as a fuctio of the VM degradatio rates ad rejuveatio trigger rates. For the system with higher VM degradatio rate, it ca be show that the rejuveatio trigger rate icrease for VM, the lower dowtime ca be achieved. Fig.3 State Trasitio Diagram of 3 Physical Hosts System Steady-state probabilities of 3 physical hosts system are as follows: Up state: PU1+PU2+PU3; Degradatio state: PD1+PD2+PD3; Applicatio level Rejuveate state: PR1+PR2+PR3; gratio state: PM1+PM2+PM3; Failure state: PF; The steady state availability ad dowtime aalysis of 3 physical hosts system are show i the followig Figures. The results derived from umerical equatios are validated with SHARPE tools. SHARPE [11] is Symbolic Hierarchical 48

5 3rd Iteratioal Coferece o Computatioal Techiques ad Artificial Itelligece (ICCTAI'2014) Feb , 2014 Sigapore or more physical host system to be migrated from oe physical host to aother. Fig.6 Availability vs. Differet VM Degradatio Rates ad Differet Rejuveatio Rates The availability chages for the model with VM degradatio rates ad rejuveatio rates are show i Fig 6. The rejuveatio time µ r are assumed 20secods ad 10secods. It ca be observed that the quicker rejuveatio rate for VM, the higher availability ca be achieved. Fig.7 Dowtime vs. Differet VM Degradatio Rates ad Differet Rejuveatio Rates The differeces i dowtime with differet VM degradatio time ad differet rejuveatio time are show i Fig.7. From the result, it is apparet that the quicker rejuveatio time for VM ca ehace the availability ad reduce the dowtime. Fig.8 Availability vs. Differet Number of Physical Hosts I Fig.8, we plot the steady-state availability o differet physical hosts. Whe there is oe physical host i the system, the operatioal VM o the host ca t be migrated to other physical host. The amout of availability icremet from 1 physical host to 2 or more physical hosts is sigificat because there are more opportuities for the operatioal VM i the two Fig.9 Availability vs. Differet Number of Physical Hosts ad gratio Rate We aalyze the availability o differet physical hosts as a fuctio of differet migratio rates. The chage i the availability of system with the differet umbers of physical hosts ad differet migratio rates is plotted i Fig 9. The more physical hosts i the system, the more chace the operatioal VM to be migrated. We also observe that the amout of availability icremet depeds o migratio rates. The faster migratio rate for the VM ca ehace the availability. VI. CONCLUSION I this paper, we have preseted a approach to study the availability aalysis o virtualized cloud system for cloud services with VM migratio is as a rejuveatio actio. It is foud that VM migratio is very helpful for system level rejuveatio process of VM. We have also show that how applicatio level rejuveatio ad system level rejuveatio ca ehace the availability of the cloud services ad ca reduce the dowtime. The feasibility ad correctess of our approach is evaluated with SHARPE tools ad umerical derivatios. Accordig to the evaluatio aalysis, the proposed migratio based rejuveatio model provides the availability ehacemet for cloud services. REFERENCES [1] A. Goyal, et al., Probabilistic modelig of computer system availability, Aals of Operatios Research. 8, , March, [2] A. Rezaei ad M. Sharifi, Rejuveatig High Available Virtualized Systems, The 2010 Iteratioal Coferece o Availability, Reliability ad Security, IEEE, [3] C. Clark, K. Fraser, S. Had, J. G. Hase, E. Jul, C. Limpach, I. Pratt, ad A. Warfield, Live migratio of virtual machies, i Proceedigs of the 2d Symposium o Networked Systems Desig & Implemetatio Volume 2. USENIX Associatio, 2005, pp [4] C. Gog, J. Liu, Q. Zhag, H. Che, ad Z. Gog, The characteristics of cloud computig, i Parallel Processig Workshops (ICPPW), th It. Cof. o. IEEE, 2010, pp [5] F. Machida, D. S. Kim, ad K. S. Trivedi, Modelig ad aalysis of software rejuveatio i a server virtualized system, i Software Agig ad Rejuveatio (WoSAR), 2010 IEEE 2d It. Workshop o. IEEE,2010, pp [6] F.Salfer ad K.Wolter, Aalysis of Service Availability for Timetriggered Rejuveatio Policies, Systems ad Software, May 10,

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