A MODEL FOR INVESTIGATING CLOUD COMPUTING (IAAS) IN DATA CENTER PERFORMANCE AND QOS

Transcription

1 A MODEL FOR INVESTIGATING CLOUD COMPUTING (IAAS) IN DATA CENTER PERFORMANCE AND QOS 1 G. REKHA, 2 G. LAKSHMI KANTH 1 PG Scholar, Dept of CSE gunakalarekha@gmail.com 2 Associate Professor, Dept of CSE svlakshmikanth@gmail.com ABSTRACT Cloud knowledge center management could be a key drawback owing to the many and heterogeneous ways that may be applied, ranging from the VM placement to the federation with different clouds. Performance analysis of cloud computing infrastructures is required to predict and quantify the cost-benefit of a technique portfolio and therefore the corresponding quality of service (QoS) veteran byusers. Such analyses don\'t seem to be possible by simulation or on-the-field experimentation, owing to the good variety of parameters that ought tobe investigated. during this paper, we have a tendency to gift associate analytical model, supported random reward nets (SRNs), that\'s each climbable to modelsystems composed of thousands of resources and versatile to represent completely different policies and cloud-specific ways. Severalperformance metrics area unit outlined and evaluated to investigate the behavior of a cloud knowledge center: utilization, convenience, waiting time, andresponsiveness. A resiliency analysis is additionally provided to require into consideration load bursts. Finally, a general approach is bestowed that,starting from the thought of system capability, will facilitate system managers to opportunely set the info center parameters underneath completely ifferentworking conditionsindex Terms Scriptless Attacks, XSS, CSS, SVG, HTML5, Attack Fonts. INTRODUCTION CLOUD computing could be a promising technology able to strongly modify the manner computing and storage resources are going to be accessed within the close to future [1]. Through the provision of on-demand access to virtual resources available on the net, cloud systems supply services at three completely different levels: infrastructure as a service (IaaS),platform as a service (PaaS), and code as a service(saas). above all, IaaS clouds give users withcomputational resources within the type of virtual machine(vm) instances deployed within the supplier knowledge center, whilepaas and SaaS clouds supply services in terms of specificsolution stacks and application code suites, severally. solely natural to surprise what styles of measures square measure necessary to guard such knowledge, particularly in reference to security and privacy issues. To integrate business needs and application-levelneeds, in terms of quality of service (QoS), cloud serviceprovisioning is regulated by service-level agreements(slas): contracts

2 between purchasers and suppliers that categoricalthe price for a service, the QoS levels needed throughout theservice provisioning, and therefore the penalties related to thesla violations. In such a context, performance analysisplays a key role permitting system managers to judge theeffects of various resource management methods on thedata center functioning and to predict the corresponding costs/benefits Going back one step, we have a tendency to note that XSS attacks ought to meet 3 preconditions guaranteeing their success: Cloud systems disagree from ancient distributed systems. First of all, they\'re characterised by a awfully sizable amount ofresources that may span totally different body domains.moreover, the high level of resource abstraction permits North American nation toimplement specific resource management techniques suchas VM multiplexing [2] or VM live migration [3] that, even iftransparent to final users, need to be thought-about within the styleof performance models to urately perceive the systembehavior. Finally, totally different clouds, happiness to an equivalent orto totally different organizations, will dynamically be part of one another toachieve a typical goal, sometimes drawn by heoptimization of resources utilization. This mechanism,referred to as cloudfederation [4], permits North American nation to supply andrelease resources on demand, therefore providing lastic capabilities to the full infrastructure. For these reasons, typical performance analysis approaches like simulation or on-the-field measurementscannot be simply adopted. Simulation [5], [6] doesn\'t permitus to conduct comprehensive analyses of the systemperformance thanks to the nice range of parameters thathave to be investigated. On-the-field experiments [7], [8] aremainly targeted on the offered QoS; they\'re supported obtained knowledge to the inner resource management ways enforced by the system supplier. On the contrary,analytical techniques [9], [10] represent a decent candidate,thanks to the restricted resolution price of their associatedmodels. However, to accurately represent a cloud system, an associate degreealytical model needs to be: Scalable. To manage terribly massive systems composedof tons of or thousands of resources.. Flexible. permitting North American nation to simply implement totally differentstrategies and policies and to represent totally differentworking conditions.in this paper, we tend to gift a random model, based onstochastic reward nets (SRNs) [11], thamentioned options permitting to capture the key ideas ofan IaaS cloud system. The planned model is climbableenough to represent systems composed of thousands ofresources and it makes attainable to represent each physicaland virtual resources exploiting cloud-specific ideassuch as the infrastructure physical property. With relation to theexisting literature, the innovative facet of this workis that a generic and comprehensive read of a cloud systemis conferred. Low-level details, like VM multiplexing,are simply integrated with cloud-based actions likefederation, permitting North American nation to research totally different mixedstrategies. Associate in Nursing complete set of performance metrics isdefined concerning each the system supplier (e.g., utilization) and therefore the final users (e.g., responsiveness). Moreover,ifferent operating conditions area unit investigated and aresiliency analysis is provided to require into consideration the effects of load bursts. Finally, to supply a good comparison among totally different resource management ways, alsotaking into consideration the system physical property, a performanceevaluation approach is delineate. Such Associate in Nursing approach, basedon the idea of system capability, presents a holistic readof a cloud system and it permits system managers to check the better resolution with relation to a longtime goal and toopportunely set the system

3 parameters.t exhibits the on top ofthe paper is organized as follows: In Section two, we formulate the matter and that we describe the planned analytical model. In Sections three and four, we have a tendency to illustrate the steady STATE AND TRANSIENT SOLUTION OF the model and therefore the CORRESPONDING PRFORMANC metrics which will be outlined. InSection 5, we have a tendency to discuss some NUMERICAL RESULTS WHICH WILL BEobtained through the model answer additionally presenting Associate in rsingapproach to assess THE PERFORMANCE OF A CLOUD system,while in Section half-dozen, we have a tendency to summarize our results providing insights for future work. Finally, within the supplementary file,which CAN BE FOUND ON the pc Society Digital Library at TPDS , we offer a important summary of the state of the art and a numerical comparison with one in every of thereferred works, additionally because the required background and a few in-depth discussions. THE ANALYTICAL MODEL WE CONTEMPLATE ASSOCIATE IN NURSING IAAS CLOUD SYSTEM COMPOSED OFNPHYSICALRESOURCES (SEE FIG. 1). JOB REQUESTS (IN TERMS OF VM INSTANTIATION REQUESTS) AREA UNIT ENQUEUED WITHIN THE SYSTEM QUEUE.SUCH A QUEUE INCORPORATES A FINITE SIZEQ; ONCE ITS LIMIT IS REACHED,FURTHER REQUESTS AREA UNIT REJECTED. THE SYSTEM QUEUE IS MANAGED ACCORDING TO A INVENTORY ACCOUNTING PROGRAMING POLICY. ONCE A RESOURCE IS available, a job is accepted and the corresponding VM is instantiated. We assume that the instantiation time is negligible and that the service time (i.e., the time needed to execute a job) is exponentially distributed. According to the VM multiplexing technique [2], [12], the cloud system can provide a numbermof logical resources greater thann. In this case, multiple VMs can be allocated in the same physical machine (PM), for example, a core in a multicore architecture. Multiple VMs sharing the same PM can incur in a reduction of the performance mainly due to I/O interference between VMs [13]. We define the degradation factord( 0) as the percentage increase in the expected service time experienced by a VM when multiplexed with another VM. The performance degradation of multiplexed VMs depends on the multiplexing technique and on the VM placement strategy [14], [15], [16]. We assume that, to reduce the degradation and to obtain a fair distribution of VMs, the system is able to optimally balance the load among the PMs with respect to the resources required by VMs (e.g., trying to multiplex CPU-bound VMs only with O-bound VMs), thus reaching a homogeneous degradation factor. Then, indicating with the expected service time of a VM in isolation, we can derive the expected time needed to execute two multiplexed VMs. In general, we can express the expected execution time ofi multiplexed VMs as System managers can obtain an estimation of parameter by means of theoretical studies or statistical observations. Cloud federation [4] allows the system to use, in particular situations, the resources offered by other public cloud systems through a sharing and paying model. In this way, elastic capabilities can be exploited to respond to particular load conditions. Job requests can be redirected to other clouds by transferring the corresponding VM disk images through the network. With respect to the federation technique, we make the following assumptions. A job is redirected only if it arrives when the system queue is full. Federate clouds are characterized by an availability.federate clouds are also characterized by a quality level that determines the QoS reached by a request, in terms of expected service time (i.e., a VM that needs a time to accomplish it works will

4 experience an execution time. A redirected job is inserted in theupload queue waiting for the VM transfer completion. There is a maximum number of concurrent redirected jobs (elasticity level) i.e., the upload queue has a finite size equal. The network bandwidth allows us to transmit up to kvms in parallel. The time needed to transfer a VM disk image is exponentially distributed with mean. Finally, with respect to the arrival process, we willinvestigate three different scenarios. In the first one(constant arrival process), we have a tendency to assume the arrival method be a homogeneous Poisson method with rate. However, largescale distributed systems with thousands of users, such as cloud systems, might exhibit self-similarity/long-range dependence with reference to the arrival method [17]. For these reasons, to require into consideration the dependences of the job arrival rate on each the times of per week and therefore the hours of a day, within the second situation (Periodic arrival process), we also prefer to model the work arrival method as a Markov Modulated Poisson method (MMPP). particularly, we will ask an wherever represent the expected arrival rate in high- and low-load conditions, while1 represent the expected duration of the 2 load conditions. The last situation (Bursty arrival process) takes into consideration the presence of a burst small indefinite quantity mounted and short period and it\'ll be accustomed investigate the system resiliency. To capture the most options of a typical IaaS cloud, we make use of SRNs [11]. SRNs area unit associate extension of generalized random Petri Nets (GSPNs) [18] that enable North American nation to associate reward rates with the marking (i.e., the distribution of tokens within the numerous places) [19]. within the reminder of the paper, we\'ll use the notation to ask the number of token in placep. Moreover, within the operate definitions, we have a tendency to adopt a C- like syntax victimisation the ternary operator rather than theif elseconstruct. We give a formal summary of the SRN notation within the Appendix A of the supplementary file offered on-line. The planned SRN cloud performance model is delineated in TransitionTarr models the arrival method.if the constant arrival method is taken into consideration, we can characterize transition Tarr with associate exponentially distributed firing time with mean. On the contrary, to model the periodic arrival method, the speed of transition Tarr will be computed as a operate of the quantity of tokens in situ Pmmpp, therefore getting the 2 completely different load conditions: low load (with rate capable ) once mmpp¼0and high load (with rate capable)when mmpp¼1.the alternation between low- and high-load conditions is modeled by exponentially distributed transitionsth2l and Tl2h with rate h2l and l2h, severally. The technique adopted to model the Bursty arrival method are discussed later in Section four. The system queue is shapely through placepqueue.a token during this place represents employment waiting within the queue. When the amount of tokens inpqueue is larger than the Queue sizeq, transition tdropis enabled (see the

5 corresponding guard perform ), so modeling the rejection of a request. Cloud resources area unit shapely by tokens in placepres. Such tokens represent themlogical resources offered by the system. once a resource is offered and there area unit unfinished requests, a token is removed (through transitiontlocal) from the system queue and is more to position Prun.IfVM multiplexing isn\'t allowed (M¼N), service time is modeled through transitiontserv with firing rate adequate, whereas the VM parallel execution (one for every PM) is modeled by setting transitiontserv with the infinite server semantic [18] to extend its firing rate in proportion to the number of tokens within the facultative placeprun. a lot of formally, indicating with Rserv run the marking-dependent rate of transition Tserv, we have Rserv run 2.1 Modeling Cloud Federation Federation with alternative clouds is sculptural permitting tokens in placepqueue to be rapt, through transition tupload, in the upload queue drawn by placepsend. In accordance with the assumptions created before, transitiontupload is enabled given that the amount of tokens in situ Pqueue is greater thanqand the amount of tokens in placepsend is less than D. Moreover, to require into consideration the united cloud accessibility, coincidental enabled transitionstuploadan tdropare managed by setting their weights with the valuesaf and, severally. during this means, there\'ll be a probability equal toaf to send Associate in Nursing incoming request to federated clouds and a chance equal to drop it. The VM transferring time is sculptural by transitiontsend.to take into consideration the parallel transferring with a restricted network information measure, the speed of transitiontsend is intended as follows: till the information measure isn\'t saturated (i.e., the number of VMs within the transfer queue is a smaller amount thank), the transition rate is proportional to the amount of waiting VMs. once the amount of VMs within the transfer queue is greater give thanks, the transition rate isn\'t any additional increased and it remains proportional tok. Indicating with Rsend Send the marking-dependent rate of transitiontsend. When VM multiplexing is allowed, the amount of running VMs may be larger thann, M and every PM can be loaded with over one VM. presumptuous associate degree optimal programming formula ready to balance the load among the NPMs, the maxireached by every PM (i.e., the utmost range of VMs running on one PM) is given by of the instantiated VMs may be divided into 2 set that correspond to the set of VMs running with a multiplexing level equal tol and therefore the set of VMs running with a multiplexing level up to, severally. The cardinality of such sets may be obtained as To model such a configuration, we want to calculate the equivalent rate of transitiontserv taking under consideration the parallel execution of the VMs and therefore the performance degradation issue d. we have a tendency to begin calculative the expected execution Then, to additionally take into account the VM parallel execution, I accordance with the infinite server linguistics, the markingdependent rate of transitiontservice when VMs are running onnpms is given by multiplying the amount of running VMs by the typical execution rate given by theinverse of (7). 2.3 Understanding the Model Complexity To respect the measurability demand of the planned model, we want to research its complexness. especially, we are interested within the analysis of the state-space cardinality that is the parameter that primarily influences the performance of the numerical resolution techniques. The state spacesof the model is given by the set of all its tangible markings [18]. Considering the SRN of and the corresponding guard

6 functions, we will create some considerations on the token distributions. Tokens in places Pqueue, Pres, and Prun ar ruled by the subsequent rule. giving rise to variety of attainable mixtures of the token distribution in such places Volume 2, Issue 3 APR 2015 In a market-oriented area, such as the cloud computing, an accurate evaluation of these parameters is required to quantify the offered QoS and opportunely manage SLAs. Future works will include the analysis of autonomic techniques able to change on-the-fly the system CONCLUSION configuration to react to a change on the working conditions. In this paper, we have presented a stochastic model to We will also extend the model to represent PaaS and SaaS evaluate the performance of an IaaS cloud system. cloud systems and to integrate the mechanisms needed to Several performance metrics have been defined, such as availability, utilization, and responsiveness, capture VM migration and data center consolidation aspects allowing us to investigate the impact of different that cover a crucial role in energy saving policies. strategies on both provider and user point of views. REFERENCES [1] R. Buyya et al., Cloud Computing and Emerging IT Platforms: Vision, Hype, and Reality for Delivering Computing as the Fifth Utility, Future Generation Computer System,vol. 25, pp , June [2] X. Meng et al., Efficient Resource Provisioning in Compute Clouds via VM Multiplexing, Proc. Seventh Int l Conf. Autonomic Computing (ICAC 10),pp , [3] H. Liu et al., Live Virtual Machine Migration via Asynchronous Replication and State Synchronization, IEEE Trans. Parallel and Distributed Systems,vol. 22, no. 12, pp , Dec [4] B. Rochwerger et al., Reservoir When One Cloud Is Not Enough, Computer,vol. 44, no. 3, pp , Mar [5] R. Buyya, R. Ranjan, and R. Calheiros, Modeling and Simulation of Scalable Cloud Computing Environments and the Cloudsim Toolkit: Challenges and Opportunities, Proc. Int l Conf. High Performance Computing Simulation (HPCS 09),pp. 1-11, June [6] A. Iosup, N. Yigitbasi, and D. Epema, On the Performance Variability of Production Cloud Services, Proc. IEEE/ACM 11 th Int l Symp. Cluster, Cloud and Grid Computing (CCGrid), pp , May [7] V. Stantchev, Performance Evaluation of Cloud Computing Offerings, Proc. Third Int l Conf. Advanced Eng. Computing and Applications in Sciences (ADVCOMP 09),pp , Oct S. Ostermann et al., A Performance Analysis of EC2 Cloud Computing Services for Scientific Computing, Proc. Int l Conf. Cloud Computing,LNCS vol. 34, pp , Springer, Heidelberg,2010.

7 [8] H. Khazaei, J. Misic, and V. Misic, Performance Analysis of Cloud Computing Centers Using M/G/m/m+r Queuing Systems, IEEE Trans. Parallel and Distributed Systems, vol. 23, no. 5, pp , May [9] R. Ghosh, K. Trivedi, V. Naik, and D.S. Kim, End-to-End Performability Analysis for Infrastructure-as-a-Service Cloud: An Interacting Stochastic Models Approach, Proc. IEEE 16 th Pacific Rim Int l Symp. Dependable Computing (PRDC),pp , Dec [10] G. Ciardo et al., Automated Generation and Analysis of Markov Reward Models Using Stochastic Reward Nets, Linear Algebra, Markov Chains, and Queuing Models,vol. 48, pp , Springer, [11] D. Gupta, L. Cherkasova, R. Gardner, and A. Vahdat, Enforcing Performance Isolation across Virtual Machines in Xen, Proc. ACM/IFIP/USENIX Int l Conf. Middleware,pp , [12] M. Armbrust et al., A View of Cloud Computing, Comm. ACM, vol. 53, pp , Apr [13] J.N. Matthews et al., Quantifying the Performance Isolation Properties of Virtualization Systems, Proc. Workshop Experimental Computer Science (ExpCS 07),2007. [14] M. Mishra and A. Sahoo, On Theory of VM Placement: Anomalies in Existing Methodologies and Their Mitigation Using a Novel Vector Based Approach, Proc. IEEE Fourth Int l Conf. Cloud Computing (CLOUD 11),pp , July [15] A.V. Do et al., Profiling Applications for Virtual Machine Placement in Clouds, Proc. IEEE Int l Conf. Cloud Computing (CLOUD 11),pp , July [16] A. Verma et al., Server Workload Analysis for Power Minimization Using Consolidation, Proc. USENIX Ann. Technical Conf., pp , [17] G. Balbo et al.,modelling with Generalized Stochastic Petri Nets.John Wiley & Sons, [18] R. Sahner, K.S. Trivedi, and A. Puliafito,Performance and Reliability Analysis of Computer Systems: An Example Based Approach Using the SHARPE Software Package,Kluwer Academic Publishers, [19] J.K. Muppala, K.S. Trivedi, and S.P. Woolet, On Modeling Performance of Real-Time Systems in the Presence of Failures, Readings in Real-Time Systems,pp , IEEE CS Press, [20] A. Puliafito, S. Riccobene, and M. Scarpa, Evaluation of Performability Parameters in Client-Server Environments, The Computer J.,vol. 39, no. 8, pp , [21] R. Ghosh, F. Longo, V. Naik, and K. Trivedi, Quantifying Resiliency of IaaS Cloud, Proc. IEEE 29th Symp. Reliable Distributed Systems,pp , [22] SPNP Manual, SPNPv6-manual.pdf. 2013