To Investigate Data Center Performance and Quality of service in IaaS CloudComputing Systems.

manoj v, pranav vallabaneni


Cloud data center management is a key problem due to the numerous and heterogeneous strategies that can be applied, ranging from the VM placement to the federation with other clouds. Performance evaluation of Cloud Computing infrastructures is required to predict and quantify the cost benefit of a strategy portfolio and the corresponding Quality of Service (QoS) experienced by users. Such analyses are not feasible by simulation or on the field experimentation, due to the great number of parameters that have to be investigated. In this paper, we present an analytical model, based on Stochastic Reward Nets (SRNs), that is both scalable to model systems composed of thousands of resources and flexible to represent different policies and cloud specific strategies. Several performance

metrics are defined and evaluated to analyze the behavior of a Cloud data center: utilization, availability, waiting time, and responsiveness. A resiliency analysis is also provided to take into account load bursts. Finally, a general approach

is presented that, starting from the concept of system capacity, can help system managers to opportunely set the data center parameters under different working conditions.


R. Buyya et al., “Cloud computing and emerging it platforms: Vision, hype, and reality for delivering computing as the 5th utility,” Future Gener. Comput. Syst., vol. 25, pp. 599–616, June 2009.

X. Meng et al., “Efficient resource provisioning in compute clouds via vm multiplexing,” in Proceedings of the 7th international conference on Autonomic computing, ser. ICAC ’10. New York, NY, USA: ACM, 2010, pp. 11–20.

H. Liu et al., “Live virtual machine migration via asynchronous replication and state synchronization,” Parallel and Distributed

Systems, IEEE Transactions on, vol. 22, no. 12, pp. 1986 –1999, dec. 2011.

B. Rochwerger et al., “Reservoir - when one cloud is not enough,”Computer, vol. 44, no. 3, pp. 44 –51, march 2011.

R. Buyya, R. Ranjan, and R. Calheiros, “Modeling and simulation of scalable cloud computing environments and the cloudsim

toolkit: Challenges and opportunities,” in High Performance Computing Simulation, 2009. HPCS ’09. International Conference on, june

, pp. 1 –11.

A. Iosup, N. Yigitbasi, and D. Epema, “On the performance variability of production cloud services,” in Cluster, Cloud and Grid

Computing (CCGrid), 2011 11th IEEE/ACM International Symposium on, may 2011, pp. 104 –113.

V. Stantchev, “Performance evaluation of cloud computing offerings,” in Advanced Engineering Computing and Applications in

Sciences, 2009. ADVCOMP ’09. Third International Conference on,oct. 2009, pp. 187 –192. [8] S. Ostermann et al., “A Performance Analysis of EC2 Cloud Computing Services for Scientific Computing,” in Cloud Computing, ser. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer Berlin Heidelberg, 2010, vol. 34, ch. 9, pp. 115–131.

H. Khazaei, J. Misic, and V. Misic, “Performance analysis of cloud computing centers using m/g/m/m+r queuing systems,” Parallel

and Distributed Systems, IEEE Transactions on, vol. 23, no. 5, pp. 936 –943, may 2012.

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,” in Dependable Computing (PRDC), 2010 IEEE 16th Pacific Rim International Symposium on,

dec. 2010, pp. 125 –132.

G. Ciardo et al., “Automated generation and analysis of Markov reward models using stochastic reward nets.” IMA Volumes in

Mathematics and its Applications: Linear Algebra, Markov Chains, and Queueing Models, vol. 48, pp. 145–191, 1993.

D. Gupta, L. Cherkasova, R. Gardner, and A. Vahdat, “Enforcing performance isolation across virtual machines in xen,” in

Proceedings of the ACM/IFIP/USENIX International Conference on Middleware, New York, NY, USA: Springer-Verlag New York,

Inc., 2006, pp. 342–362.

M. Armbrust et al., “A view of cloud computing,” Commun. ACM, vol. 53, pp. 50–58, Apr. 2010.

J. N. Matthews et al., “Quantifying the performance isolation properties of virtualization systems,” in Proceedings of the 2007

workshop on Experimental computer science, ser. ExpCS ’07. NewYork, NY, USA: ACM, 2007.

M. Mishra and A. Sahoo, “On theory of vm placement: Anomalies in existing methodologies and their mitigation using a novel

vector based approach,” in Cloud Computing (CLOUD), 2011 IEEE International Conference on, july 2011, pp. 275 –282.

A. V. Do et al., “Profiling applications for virtual machine placement in clouds,” in Cloud Computing (CLOUD), 2011 IEEE International Conference on, july 2011, pp. 660 –667.

A. Verma et al., “Server workload analysis for power minimization using consolidation,” in Proceedings of the USENIX Annual technical conference, Berkeley, CA, USA, 2009, pp. 28–28.

G. Balbo et al., Modellin

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