Availability Analysis of Application Servers Using Software Rejuvenation and Virtualization

Transcription

1 Thein T, Park J S. Availability analysis of application servers sing software rejvenation and virtalization. JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY 24(2): Mar Availability Analysis of Application Servers Using Software Rejvenation and Virtalization Thandar Thein and Jong So Park, Member, IEEE Compter Engineering Department, Korea Aerospace University, 200-1, Hwajeon-Dong, Dkyang-G, Goyang City Gyeonggi-Do , Korea {thandar, jspark}@ka.ac.kr Received March ; revised Janary 7, Abstract Demands on software reliability and availability have increased tremendosly de to the natre of present day applications. We focs on the aspect of software for the high availability of application servers since the navailability of servers more often originates from software falts rather than hardware falts. The software rejvenation techniqe has been widely sed to avoid the occrrence of nplanned failres, mainly de to the phenomena of software aging or cased by transient failres. In this paper, first we present a new way of sing the virtal machine based software rejvenation named VMSR to offer high availability for application server systems. Second we model a single physical server which is sed to host mltiple virtal machines (VMs) with the VMSR framework sing stochastic modeling and evalate it throgh both nmerical analysis and SHARPE (Symbolic Hierarchical Atomated Reliability and Performance Evalator) tool simlation. This VMSR model is very general and can captre application server characteristics, failre behavior, and performability measres. Or reslts demonstrate that VMSR approach is a practical way to ensre ninterrpted availability and to optimize performance for aging applications. Keywords availability, modeling, software aging, software rejvenation, virtal machine 1 Introdction Demands on software reliability and availability have increased tremendosly in accordance with the present day applications. The stdy of software reliability and availability of application servers is needed becase the conseqences of software failre often case enormos economic and reptational losses. It has now been well-established that these software failres lead to more otages than hardware failres [1,2]. Frthermore, software exhibits an increasing failre rate over time, typically becase of a variety of reasons, inclding the complexity of the software systems, the natre of distribtion and the large size of client grops. Recently, researchers have reported that software applications execting continosly for a long period of time show a degraded performance and/or an increased occrrence rate of hang/crash failres. This wide-spread phenomenon is called software aging [3,4]. Stdies have been done on how to conteract the aging problem and how to implement a highly available system for application servers. The software rejvenation techniqe has been widely sed to avoid the occrrence of nplanned failres, mainly cased by the phenomena of software aging or transient failres [5]. A nmber of research papers on this topic can be fond in [4]. Withot any rejvenation, both the OS software and the application software rnning on top of it degrade in performance with time de to the exhastion of system resorces sch as free physical memory, and eventally crash, which is very ndesirable to high availability (HA) systems and fatal to mission-critical applications. The negative side effect of rejvenation is the temporary otage of service. To completely eliminate the service otage dring the rejvenation process, we adopt the sage of virtalization technology. Virtalization is a technology that combines or divides compting resorces to present one or many operating environments sing methodologies like hardware and software partitioning or aggregation, partial or complete machine simlation, emlation, time-sharing, and others. The virtalization technology has existed since the early days of compter science and attracts a heavy attention from the research commnity even today. Reglar Paper This research was spported by the Korea Research Fondation Grant fnded by the Korean Government (MOEHRD) nder Grant No. KRF D00006.

2 340 J. Compt. Sci. & Technol., Mar. 2009, Vol.24, No.2 Virtalization technologies find important applications over a wide range of areas sch as server consolidation, secre compting platforms, spporting mltiple operating systems, kernel debgging and development, system migration, etc. [6] Recent advances in the compting platform performance broght virtalization technology back to the main stage. Frthermore, innovations in architectre spport for virtalization made many exciting sage models possible. 1.1 Contribtions and Paper Layot The main goal of VMSR (virtal machine based software rejvenation) is to avoid the occrrence of software aging by applying the clean planned restart rejvenation mechanism and the replicated exection of application services in different VMs that will be rnning on top of the same physical machine. In this paper, we present a novel way of sing the virtal machine based software rejvenation named VMSR to offer high availability for application server systems. We adopt the virtalization technology to completely eliminate the otage cased by software rejvenation. We model and evalate the VMSR model sing both nmerical analysis and SHARPE simlation. The VMSR approach can provide high availability in single physical server configration and withot reqiring any additional hardware. So VMSR is a cost effective software based approach to coping with the software aging problem. Moreover, or approach can be applied to very practical sitations like crrent mlti-core processors (e.g., Xeon), which considerably increase the applicability of or approach. This paper is strctred as follows. In Section 2 we smmarize some fndamental aspects of software aging, software rejvenation and virtalization, and also discss the related work. Or proposed VMSR framework and simlation model are presented in Section 3. The reslts of analytic analysis follow in Section 4. Finally, we conclde or paper in Section 5. 2 Backgrond and Related Work 2.1 Software Aging Software aging refers to progressive performance degradation in the availability of OS resorces, data corrption and nmerical error accmlation that may lead to system crashes or ndesired hang ps [7]. Aging has not only been observed in software sed on a mass scale bt also in specialized software sed in high-availability and safety critical applications. This ndesired behavior is especially visible in long-rnning software sch as web server application and enterprise always-on applications. A proactive falt management method to deal with the software aging phenomenon is software rejvenation. 2.2 Software Rejvenation Software rejvenation is a proactive falt management techniqe aiming at cleaning p the internal state of the system to prevent the occrrence of more severe crash failres in the ftre. It involves occasionally terminating an application or a system, cleaning its internal state and restarting it. Crrent methods of software rejvenation are system restart, application restart (partial rejvenation) and node/application failover (in a clster system). An extreme bt well-known example of rejvenation is a system reboot. There are nmeros examples in real-life systems where software rejvenation is being sed. Software rejvenation is a cost effective techniqe for dealing with software falts that inclde protection not only against hard failres, bt also against performance degradation. Two major approaches have been proposed: periodical rejvenation based on time or work performed, and adaptive or proactive rejvenation [8 10]. The proactive approach is more efficient and lower in cost, reslting in higher availability. Any rejvenation typically involves an overhead, bt it prevents more severe failres from occrring. 2.3 Virtalization Virtalization is a rapidly growing new technology that is transforming the world of information technology. Virtalization allows mltiple operating system instances to rn concrrently on a single compter; it is a mean of separating hardware from a single operating system. A virtal machine is a tightly isolated software container that can rn its own operating systems and applications as if it were a physical compter. A virtal machine behaves exactly like a physical compter and contains its own virtal (i.e., software-based) CPU, RAM hard disk and network interface card (NIC). A virtal machine is composed entirely of software and contains no hardware components whatsoever. As a reslt, virtal machines offer a nmber of distinct advantages over physical hardware. VMs can provide desirable featres like software flexibility, better protection and hardware independence; they are applied in varios research areas

3 Thandar Thein et al.: Availability Analysis of Application Servers by VMSR 341 and have a great potential. 2.4 Related Work Virtalization has seen a recent, renewed and massive interest in compting. Many fields, sch as atonomic compting, service consolidation, secrity and edcation pblished reslts that praise the benefits of virtalization. Recent pblications discss the prospect of high performance compting (HPC) copled with virtalization [11 13]. Virtalization has proved to be a sccessfl tool for the management of complex ITenvironments and it is emerging as a techniqe to increase system reliability and availability [14,15]. Mergen et al. [13] present an atomatic and transparent mechanism for proactive falt tolerance for arbitrary MPI applications and their approach relies on operating system virtalization techniqes exemplified by Xen. To redce the downtime and to prevent the performance degradation de to cache misses after the reboot, a techniqe for fast rejvenation of VMM (Virtal Machine Monitor) called warm-vm reboot is proposed in [16]. The athors of [17] present a techniqe that offers high availability for Grid services based on concepts like virtalization, clstering and software rejvenation. In [15] the athors present an atomated self-healing techniqe sing virtalization and software rejvenation techniqe followed by an experimental stdy. Their reslt shows that it achieves a zero downtime withot losing any work-in-progress and does not incr a significant performance penalty and it can be applied to offthe-shelf application servers. In this paper, we mainly focs on the healing of software aging of application server in a single physical machine and hardware failre is not considered in this paper. IP fail-over capabilities and a myriad of load balancing policies, and also sed for the monitoring prpose. The main application server will be rnning on one VM and the remaining VMs will work as standby servers, where we instantiate a replica of the application server. Fig.1 shows a simple example of a system we consider in this paper. Fig.1. Virtalized single server architectre. In LB-VM, we will install some software modles that will be responsible for the detection of software aging or some potential anomaly. The aging behavior of any software can be captred by one or more indicators of aging. Sch an indicator is any measrable metrics of the server likely to be inflenced by the software aging. 3 VMSR Framework and System Modeling 3.1 VMSR Framework In this sbsection, we discss how or system makes se of the virtalization technology to manage the rejvenation process. The idea proposed in this paper is to hold mltiple replicas of the aging application in a single physical server configration, and trigger the rejvenation of each one in response to the detection of software aging. We will perform migration in the sitation when the original application in the active VM is abot to be rejvenated. On top of the virtalization middleware layer, we create (n + 1) VMs in a single physical application server, containing one Load Balancer VM (LB-VM) and n operational VMs. LB-VM will be sed for providing Fig.2. Virtal machines based software rejvenation process. In other VMs, we will install an SRA (Software Rejvenation Agent) that is responsible for the rejvenation operation. The SRA [8] was designed to monitor consmable resorces, estimate the time to exhastion of those resorces, and generate alerts to the management infrastrctre when the time to exhastion is less than a ser-defined notification horizon. Or proposed

4 342 J. Compt. Sci. & Technol., Mar. 2009, Vol.24, No.2 VMSR process is shown in Fig.2. When software aging or some potential anomaly happens in active VM, VM-LB will trigger a rejvenation operation. If the active VM is abot to be rejvenated, first, standby VM will be started and then all the new reqests and sessions are migrated from the active VM to the standby VM. When ongoing reqests are finished in the aging infected VM, and then the infected VM will be rejvenated. After rejvenation, the rejvenated VM becomes as good as the new. This approach ses VMs as containers for the replica in order to avoid the need for additional hardware and it can provide contined services dring rejvenation. 3.2 VMSR Model Analytical models are mathematical models which are an abstraction from the real world system and relate only to the behavior and characteristics of interest. We se a Markov chain based approach to bilding the model. Or VMSR model aims at bilding the analytic model of system degradation, and solving this model for determining the effectiveness of or VMSR approach as well as for deriving the optimal rejvenation process. In this sbsection, we are interested in determining how or VMSR framework can enhance the availability of the system and can redce an application s downtime and the cost de to downtime. The Markov chain state transition diagram with the VMSR framework is shown in Fig.3, where there are five states: the healthy state (H i ), the nstable state (U i ), the switchover state (S i ), the rejvenation state (R i ) and the failre state (H 0 ). The assmptions sed in the modeling are as follows: The failre rate (λ), the repair rate (µ), the nstable rate (λ ), and the rejvenation rate (λ r ) of the VM are identical at all states. Sojorn time in a state is exponentially distribted. According to the VMSR framework, first the active VM and all standby VMs are in the healthy state (H i ). When software applications execte continosly for long period of time the processes corresponding to the software in exection age or slowly degrade with respect to effective se of their system resorces. At that time, application software may change from the healthy state to the nstable state at a rate of (i λ ). In the nstable states, VM performance is degraded and software-aging effects render the system nreliable. When the active VM is abot to be rejvenated, the application software may change from the nstable state to the switchover state at a rate of (i λ). When all ongoing reqests are finished in the active VM, the active VM state may change from the nstable state to the rejvenation state with rate (i λ r ). After rejvenating the VM, the nstableness of the system is removed atomatically. In the failre state (H 0 ), all VMs stop rnning and no available VM remains. The tasks of falt detection and/or switchover from the active VM to the standby VM are represented in the switchover state (S i ). If the active VM has encontered the software aging, the standby VM will take the role of the active VM and contines to operate based on the crrent state. So, dring the rejvenation process, continos service is possible. We define the steady-state probabilities of the system as follows: probabilities in the healthy state: n P Hi; probabilities in the nstable state: n P Ui; probabilities in the rejvenation state: n P Ri; probabilities in the switchover state: n i=2 P Si; probability in the failre state: P H0 (n = the nmber of operational VMs). Or state transition diagram in Fig.3 can be described as a Markov process class. We can perform a steady state analysis of the diagram. The steady-state balance eqations of Fig.3 are as follows: For state H i (i = 2,..., n 1) (n 3) (µ + iλ )P Hi = λ s P si+1 + µ r P Ri + µp H(i 1). (1) Fig.3. State transition diagram for VMSR framework.

5 Thandar Thein et al.: Availability Analysis of Application Servers by VMSR 343 For state H n nλ P Hn = µp H(n 1) + µ r P Rn. (2) For state H 1 (n 2) (µ + λ )P H1 = λ s P s2 + µ r P R1 + µp H0. (3) For state H 0 µp H0 = λp U1. (4) For state U i (i = 1, 2, 3,..., n) (λ + λ r )P Ui = λ P Hi. (5) For state R i (i = 1, 2, 3,..., n) For state S i (i = 2, 3,..., n) µ r P Ri = iλ r P Ui. (6) λ s P si = iλp Ui. (7) The conservation eqation of Fig.3 is obtained by smming the probabilities of all states in the system and the sm of the eqation is 1. n P Hi + i=0 n P Ui + n P Ri + n P Si = 1. (8) i=2 Combining the above-mentioned balance eqations with the conservation eqation, and solving these simltaneos eqations, we acqire the closed-form soltions for the VMSR model. Simplex System (n = 1) In the simplex system, LB-VM and only one active VM exist. There are for states: the healthy state (H 1 ), the nstable state (U 1 ), the rejvenation state (R 1 ) and the failre state (H 0 ). So it is not necessary to represent the switch over state. [ P H1 = 1 + λ ( 1 + λ r + λ ) ] 1. (9) λ + λ r µ r µ Mltiplex System (n 2) The mltiplex system consists of the LB-VM, the active VM and the (n 1) standby VMs. [ { n P Hn = n! + λ λ + λ r + λ r µ r λ λ + λ r λ 1 λ ) n i i!( µ λ + λ r n 1 λ λ ) n i i!( µ λ + λ r n 1 λ λ ) n i (i 1)!( µ λ + λ r + λ λ s λ λ + λ r n 1 λ λ ) n i (i 1)!( µ λ + λ r i=2 ( λ λ ) }] n 1 +. (10) µ λ + λ r P Hi = n! ( λ λ ) n i PHn i! µ λ + λ r (i = 0, 1, 2,..., n), (11) ( λ ) P Ui = P Hi (i = 1, 2,..., n), (12) λ + λ r P Ri P Si = iλ r λ P Hi µ r λ + λ r (i = 1, 2,..., n), (13) = iλ λ P Hi λ s λ + λ r (i = 2, 3,..., n). (14) 4 Model Analysis 4.1 Availability Availability models captre the failre and repair behavior of systems and their components. States of the nderlying Markov chain will be classified as p states or down states. In or model shown in Fig.3, service is not available in the rejvenation processes in the rejvenation state (R 1 ) and the failre state (H 0 ). The system availability in the steady-state is defined as follows: Availability(A) = 1 Unavailability, Availability(A) = 1 (P H0 + P R1 ). (15) 4.2 Downtime and Downtime Cost To compte the downtime cost, we first look at the state transition diagram of VMSR framework as shown in Fig.3. When an application server is down, no service is provided and no revene is received. Therefore, there is a bsiness cost de to service being navailable dring downtime. Planned shtdown cost is far less than that of nexpected shtdown (C f C r ). Here, C f is the nit cost of the nexpected shtdown of a server, and C r is the nit cost of the rejvenation process. The expected total downtime and downtime cost of the system can be defined as a fnction of operation time (T ). Downtime(T ) = (P H0 + P R1 ) T, (16) DowntimeCost(T ) = (P H0 C f + P R1 C r ) T. 4.3 Nmerical Reslts (17) In this sbsection, we illstrate the applicability of or model and soltion methodology throgh nmerical

6 344 J. Compt. Sci. & Technol., Mar. 2009, Vol.24, No.2 examples. The exact model parameter vales for the VMSR model are not known. However, a good estimate vale for a range of model parameter is assmed. For this prpose, the parameters [18] are chosen as follows. The system sing or VMSR framework operates for one year continosly. The mean time between two consective failres is 1 year and the repair time is 12 hors. A healthy VM becomes nstable every 15 days. The rejvenation time and switchover time are 10 min and 3 min, respectively. The nmber of the operational VMs is varied from simplex (n = 1) to mltiplex (n = 4). We perform software rejvenation with the interval from (rate=3) to infinity (rate=0: no rejvenation). The nexpected downtime cost per nit is 100 times greater than that of the planned rejvenation cost. System Notation 1VM: only one active VM; 2VMs: one active VM and one standby VM; 3VMs: one active VM and 2 standby VMs; 4VMs: one active VM and 3 standby VMs. The change in the availability of a system with the different nmbers of VMs and different rejvenation rates is plotted in Fig.4. We observe that an nstable state is removed freqently with high rejvenation rates, the availability of application server with simplex configration (1VM) increases. VMs. From this reslt, it is apparent that 2VMs configration with VMSR approach is a cost-effective way and lower management cost to bild high availability systems. Fig.5. Availability vs. different rejvenation time and the different nmbers of VMs. Fig.6. Downtime cost vs. different rejvenation rates and the different nmbers of VMs. For example, operational 2VMs system with VMSR framework (n = 2) is modeled in Fig.7 and the steady state probabilities of the system are given. Fig.4. Availability vs. different rejvenation rates and the different nmbers of VMs. The inflence of rejvenation time along with rejvenation rates on availability is shown in Fig.5. From the figre, it can be observed that an nstable state is removed freqently with fast rejvenation time, and the availability of application server with simplex configration increases. According to Figs. 4 and 5, the amont of availability increment from 1VM to 2VMs is significant bt from 2VMs to 3VMs very little increment is shown. As the degree of operational VM redndancy is larger than or eqal to 3, the improvement of availability is not significant. Fig.6 shows the plot for downtime cost verss different rejvenation rates and the different nmbers of Fig.7. State transition diagram of 2VMs. Steady-state probabilities of 2VMs system are as follows: healthy state: P H2 + P H1 ; nstable state: P U2 + P U1 ; rejvenation state: P R2 + P R1 ; switchover state: P S2 ; failre state: H 0. Assessment of performance, reliability and availability is a key step in the design, analysis and tning of compter system. SHARPE [19,20] is a well known

7 Thandar Thein et al.: Availability Analysis of Application Servers by VMSR 345 modeling tool in the field of reliability, dependability and performability. SHARPE combines flexibility of Markov models and efficiency of combinatorial models, and is sed for software and hardware reliability/performance/performability modeling. We also compare the reslts obtained from the nmerical derivation and the reslts obtained from the nmerical soltion by SHARPE. In Fig.8, we plot the steady-state availability as the fnction of rejvenation rates and failre rates. We se λ = 1 time/year and λ = 2 times/year, and we perform software rejvenation with rate (λ r = 0: no rejvenation) to ((λ r = 3). lower than that of an nplanned shtdown. Fig.11 shows the possibility of the practical se of or approach. The downtime and downtime cost of the system with or approach decreases as the rejvenation rate increases. Fig.10. Downtime vs. different rejvenation rates. Fig.8. Availability vs. different rejvenation rates and the different failre rates (D=derivation, S=SHARPE). From the graph the amont of availability increment from no rejvenation to performing rejvenation is significant. The reslt shows that, if failre rate is large, rejvenation shold be performed freqently so that the application stays in healthy state. We also observe that the failre rate of the system acts as an important factor in the availability of the application servers. The effect of repair capability for the system to availability with different rejvenation rates is shown in Fig.9. We assme the repair rates are once per day and twice per day. The figre shows that the faster the repair, the larger the availability that is expected. Fig.9. Availability vs. different rejvenation rates and different repair rates (D=derivation, S=SHARPE). In Fig.10 we have plotted the downtime as a fnction of the rejvenation rates. The downtime cost of planned shtdown is mch Fig.11. Downtime cost vs. different rejvenation rates. According to Figs.8, 9 and 10, we have fond that or derivation reslts and SHARPE tool reslts are the same. 5 Conclsions In this paper, we have presented effective software rejvenation by sing the virtalization technology that has proved to be highly effective in conteracting the software aging problem. We have developed the availability model to assess the impact of the VMSR approach. The VMSR model is developed sing stochastic modeling. We have nmerically derived the steady-state system availability, downtime and downtime costs in terms of the parameters in the model. We validated or nmerical reslts with the evalation reslts throgh SHARPE. It is fond that or reslts and SHARPE reslts are the same. We have fond that virtalization can be helpfl for the software rejvenation process and fail-over in the occrrence of application failres and software aging. We have also fond that copling software rejvenation and virtalization can be sed as a preventive falt-tolerant techniqe and it improves the availability of application servers. Or proposed VMSR

8 346 J. Compt. Sci. & Technol., Mar. 2009, Vol.24, No.2 approach can be applied to a single-server or clster configrations by providing higher levels of availability and lower management cost. Ftre work will inclde experiments with an implementation nder real world conditions to verify the practical efficacy of or approach. Frthermore, we plan to extend the approach to non-deterministic aging profiles and transient failres. References [1] Gray J, Siewiorek D P. High-availability compter systems. IEEE Compter, 1991, 24(9): [2] Sllivan M, Chillarege R. Software defects and their impact on system availability A stdy of field failres in operating systems. In Proc. the 21st Int. Symp., Falt-Tolerant Compting, Montreal, Canada, Jne 25 27, 1991, pp.2 9. [3] Grottke M, Li L, Vaidyanathan K, Trivedi K S. Analysis of software aging in a Web server. IEEE Transactions on Reliability, 2006, 55(3): [4] Software rejvenation. Department of Electrical and Compter Engineering, Dke University, [5] Hang Y, Kintala C, Kolettis N, Flton N D. Software rejvenation: Analysis, modle and application. In Proc. the 25th Int. Symp., Falt-Tolerant Compting, Pasadena, CA, Jne 27 30, 1995, pp [6] Nanda S, Chieh T. A srvey on virtalization technologies. Tech. Rep. TR-179, Stony Brook University, Feb [7] Trivedi K S, Vaidyanathan K, Goseva-Popstojanova K. Modeling and analysis of software aging and rejvenation. In Proc. the 33rd Annal Simlation Symp., Greece, Apr. 2000, pp [8] Castelli V, Harper R E, Heidelberger P, Hnter S W, Trivedi K S, Vaidyanathan K, Zeggert W P. Proactive management of software aging. IBM Jornal of Research and Development, 2001, 45(2): [9] Garg S, Moorsel A V, Vaidynathan K, Trivedi K S. A methodology for detection and estimation of software aging. In Proc. the 9th Int. Symp. Software Reliability Engineering, Paderborn, Germany, November 4 7, 1998, p.283. [10] Vaidyanathan K, Trivedi K S. A measrement-based model for estimation of software aging in operational software systems. In Proc. the 10th IEEE Int. Symp., Software Reliability Engineering, Boca Raton, FL, USA, Nov. 1 4, 1999, pp [11] Barham B, Dragovic B, Fraser K, Hand S, Harris T, Ho A, Negebaer R, Pratt I, Warfield A. Xen and the art of virtalization. In Proc. the 19th Int. ACM Symp. Operating Systems Principles, New York, USA, ACM Press, 2003, pp [12] Begnm K M, Disney M. Scalable deployment and configration of high-performance virtal clsters. International Jornal of Applied Mathematics and Compter Sciences, 2005, ISSN , 2(4): [13] Mergen M F, Uhlig V, Krieger O, Xenidis J. Virtalization for high-performance compting. SIGOPS Operating Systems Review, 2006, 40(1): [14] Nagarajan A B, Meller F, Engelmann C, Scott L S. Proactive falt tolerance for HPC with Xen virtalization. In Proc. the 21st Annal Int. Conference on Sper Compting, Seattle, Washington, USA, Jne 17 21, 2007, pp [15] Silva L M, Alonso J, Silva P, Torres J, Andrzejak A. Using virtalization to improve software rejvenation. In Proc. the 6th IEEE Int. Symp. Network Compting and Applications, Jly 12 14, 2007, pp [16] Korai K, Chiba S. A fast rejvenation techniqe for server consolidation with virtal machines. In Proc. the 37th Annal IEEE/IFIP Int. Conference on Dependable Systems and Networks (DSN 07), Jne 25 28, 2007, pp [17] Alonso J, Silva L, Andrzejak A, Silva P, Torres J. Highavailable grid services throgh the se of virtalized clstering. In Proc. the 8th IEEE/ACM Int. Conference on Grid Compting, Astin, Texas, Sept , 2007, pp [18] Garg S, Pliafito A, Telek M, Trivedi K S. Analysis of preventive maintenance in transactions based software systems. IEEE Transactions on Compters, 1998, 47(1): [19] Hirel C, Sahner R, Zang X, Trivedi K S. Reliability and performability modeling sing SHARPE Compter Performance Evalation/TOOLS Lectre Notes in Compter Science 1786, Springer-Verlag, 2000, pp [20] Trivedi K S. SHARPE 2002: Symbolic hierarchical atomated reliability and performance evalator. In Proc. Int. Conference on Dependable Systems and Networks, 2002, p.544. Thandar Thein received her M.Sc. (compter science) and Ph.D. degrees in 1996 and 2004, respectively from University of Compter Stdies, Yangon (UCSY), Myanmar. She did post doctorate research in Korea Aerospace University. She is crrently a faclty member of UCSY. Her research interests inclde biqitos sensor network secrity, secrity engineering, and network secrity and srvivability. Jong So Park received the M.S. degree in electrical and compter engineering from North Carolina State University in And he received his Ph.D. degree in compter engineering from the Pennsylvania State University in From , he worked as an assistant professor at the Pennsylvania State University in Compter Engineering Department and he was president of the KSEA Central PA, Chapter. He is crrently a fll professor in Compter Engineering Department, Korea Aerospace University. His main research interests are information secrity, embedded system and hardware design. He is also interested in resorce exploration by sing airships, satellites, and microrobots. He is a member of IEEE and IEICE, and he is an exective board member of the Korea Institte of Information Secrity and Cryptology, and Korea Information Assrance Society.