Performance Modeling of Cryptographic Service System Virtualization Based on ISSM

doi:10.16182/j.issn1004731x.joss.201708008

Abstract

Abstract: The complicated architecture of cryptographic service system virtualization raised the difficulty of performance modeling. A performance modeling approach based on ISSMs was proposed. The approach divided the execution process into two stages, host preprocessing and arithmetic-module calculating, and built two sub-models based on queuing theory. On this basis, the effectiveness of this approach was verified. The results show that this method can analyze the impacts on system performance caused by task arrival rates, host and cryptographic card configurations quantitatively, and also be helpful for providing reasonable solutions to deploy virtualized cryptographic service system on cloud computing platforms.

Key words: cryptographic service system, virtualization, performance modeling, response time, cloud computing, blocking probability

CLC Number:

TP391.9

Guo Songhui, Li Qingbao, Sun Lei, Gong Xuerong, Yang Tianchi. Performance Modeling of Cryptographic Service System Virtualization Based on ISSM[J]. Journal of System Simulation, 2017, 29(8): 1692-1701.

References

[1] 林闯, 苏文博, 孟坤, 等. 云计算安全: 架构、机制与模型评价[J]. 计算机学报, 2013, 36(9): 1765-1784.
(Lin Chuang, Su Wenbo, Meng Kun, et al.Cloud Computing Security: Architecture, Mechanism and Modeling[J]. Chinese Journal of Computers, 2013, 36(9): 1765-1784.)
[2] 李林, 杨先文, 郑斌. 多任务密码服务系统设计与实现[J]. 信息工程大学学报, 2013, 14(1): 96-102.
(Li Lin, Yang Xianwen, Zheng Bin.Design and Implementation of Multi-Tasking Cryptography Service System[J]. Journal of Information Engineering University, 2013, 14(1): 96-102.)
[3] Vaquero L M, Rodero-Merno L, Caceres J, et al.A Break in the Clouds: Towards a Cloud Definition[J]. ACM SIGCOMM Computer Communication Review (S0146-4833), 2008, 39(1): 50-55.
[4] Sandhu R, Sood S.Scheduling of big data applications on distributed cloud based on QoS parameters[J]. Cluster Computing (S1386-7857), 2015, 18(2): 817-828.
[5] 程仲汉, 官水旺, 黄皓. 多核环境下一种支持动态预测性能损失的方法[J]. 系统仿真学报, 2014, 26(11): 2803-2809.
(Cheng Zhonghan, Guan Shuiwang, Huang Hao.Method to Support Degradation Prediction Online in Multicore Environment[J]. Journal of System Simulation (S1004-731X), 2014, 26(11): 2803-2809.)
[6] Ghosh R, Longo F, Frattini F, et al.Scalable Analytics for IaaS Cloud Availability[J]. IEEE Transactions on Cloud Computing (S2168-7161), 2014, 2(1): 57-70.
[7] 何选森. 随机过程与排队论 [M]. 长沙: 湖南大学出版社, 2010: 186-190.
(He Xuansen.Random Process and Queuing Theory [M]. Changsha, China: Hunan University Press, 2010: 186-190.)
[8] Yigitbasi N, Iosup A, Epema D, et al.C-Meter: A Framework for Performance Analysis of Computing Clouds [C]// Proceedings of the 2009 IEEE/ACM 9th International Symposium on Cluster Computing and the Grid (CCGRID '09), Shanghai, China. USA: IEEE Press, 2009: 472-477.
[9] Salah K, Boutaba R.Estimating service response time for elastic cloud applications [C]// Proceedings of the 2012 IEEE 1st International Conference on Cloud Networking (CLOUDNET), Paris, France. USA: IEEE Press, 2012: 12-16.
[10] Mytilinis I, Tsoumakos D, Kantere V, et al.I/O Performance Modeling for Big Data Applications over Cloud Infrastructures [C]// Proceedings of the 2015 IEEE International Conference on Cloud Engineering (IC2E). Tempe, USA. USA: IEEE Press, 2015: 201-206.
[11] Khazaei H, Misic J, Misic V B.A Fine-Grained Performance Model of Cloud Computing Centers[J]. IEEE Transactions on Parallel and Distributed Systems (S1045-9219), 2013, 24(11): 2138-2147.
[12] 明仲, 尹剑飞, 杨巍, 等. 一种Web系统性能测试框架及其混合建模过程[J]. 计算机研究与发展, 2010, 47(7): 1192-1200.
(Ming Zhong, Yin Jianfei, Yang Wei, et al.A Web Performance Testing Framework and Its Mixed Performance Modeling Process[J]. Journal of Computer Research and Development, 2010, 47(7): 1192-1200.)
[13] Kraft S, Casale G, Krishnamurthy D, et al.Performance models of storage contention in cloud environments[J]. Software and Systems Modeling (S1619-1366), 2013, 12(4): 681-704.
[14] Pu X, Liu L, Mei Y D, et al.Who Is Your Neighbor: Net I/O Performance Interference in Virtualized Clouds[J]. IEEE Transactions on Services Computing (S1939-1374), 2013, 6(3): 314-329.
[15] Bourguiba M, Haddadou K, El Korbi I, et al.Improving Network I/O Virtualization for Cloud Computing[J]. IEEE Transactions on Parallel and Distributed Systems (S1045-9219), 2014, 25(3): 673-681.
[16] Guan H B, Ma R H, Li J.Workload-Aware Credit Scheduler for Improving Network I/O Performance in Virtualization Environment[J]. IEEE Transactions on Cloud Computing (S2168-7161), 2014, 2(2): 130-142.
[17] Chydzinski A, Adamczyk B.Analysis of a Scheduler for Virtualization of Links with Performance Isolation[J]. Applied Mathematics & Information Sciences (S1935-0090), 2014, 8(6): 2653-2666.
[18] Li D D, Jin H, Liao X F, et al.Improving disk I/O performance in a virtualized system[J]. Journal of Computer and System Sciences (S0022-0000), 2013, 79(2): 187-200.
[19] Tian K, Dong Y Z, Mi X, et al.sEBP: Event Based Polling for Efficient I/O Virtualization [C]// Proceedings of the 2012 IEEE International Conference on Cluster Computing (CLUSTER), Beijing, China. USA: IEEE Press, 2012: 135-143.
[20] Neuts M F.Matrix-Geometric Solutions in Stochastic Models: An Algorithmic Approach [M]. Baltimore, USA: The Johns Hopkins University Press, 1981: 125-141.
[21] Barham P, Dragovic B, Fraser K, et al.Xen and the Art of Virtualization [C]// Proceedings of the 19th ACM Symposium on Operating Systems Principles (SOSP '03). New York, USA: ACM Press, 2003: 164-177.