An Novel Approach to Evaluate the Reliability of Cloud Rendering System Using Probabilistic Model Checker PRISM: A Quantitative Computing Perspective
Pages 78 - 85
Abstract
This paper proposes an approach to evaluate the reliability of cloud rendering system. After the requirement analysis, the rendering system was divided into three modules: preparing files, requesting resources, and rendering task execution. Each module may have an exception that will reduce reliability, and has the ability to recover it. To expose these details, the discrete-time Markov chain (DTMC) is improved to formalize the cloud rendering system. The model contains an abnormal state set representing exceptions and errors such as file corruption and failure to rendering subtasks. Then, a series of formal properties are defined to describe reliability in detail. The proposed method gives full consideration to the processes of rendering tasks. Finally, the properties are verified by performing PRISM in a quantitative way. The experiment shows that our method is effective to evaluate the reliability of the cloud rendering system.
References
[1]
Y. Chen, et al. 2016. Multi-Objective Service Composition with QoS Dependencies. IEEE Transactions on Cloud Computing PP.99, 1--1.
[2]
M. Rady. 2013. Formal Definition of Service Availability in Cloud Computing Using OWL. Computer Aided Systems Theory - EUROCAST 2013, Springer Berlin Heidelberg, 189--194.
[3]
S. Shi, C. H. Hsu. 2015. A survey of interactive remote rendering systems. Acm Computing Surveys, 47(4), 57.
[4]
P. Quax, J. Liesenborgs, A. Barzan, M. Croonen, W. Lamotte, B. Vankeirsbilck, et al. 2016. Remote rendering solutions using web technologies. Multimedia Tools & Applications, 75(8), 1--28.
[5]
J. Kim, et al. 2014. MDP based dynamic base station management for power conservation in self-organizing networks. Wireless Communications and NETWORKING Conference IEEE, 2384--2389.
[6]
A. Hinton, et al. 2006. PRISM: A Tool for Automatic Verification of Probabilistic Systems. International Conference on Tools and Algorithms for the Construction and Analysis of Systems Springer Berlin Heidelberg, 441--444.
[7]
M. Kwiatkowska, G. Norman, and D. Parker. 2011. PRISM 4.0: Verification of Probabilistic Real-time Systems. Computer Aided Verification - International Conference, CAV 2011, Snowbird, Ut, Usa, 14--20. Proceedings DBLP, 585--591.
[8]
L. Cloth, J. P. Katoen, M. Khattri, and R. Pulungan. 2017. Model checking markov reward models with impulse rewards, 722--731.
[9]
B. Alexander, K. Marios, and H. Thoma. 2011. Service Quality in Software-as-a-Service: Developing the SaaS-Qual Measure and Examining Its Role in Usage Continuance. Journal of Management Information Systems, 85--126.
[10]
P. Quax, J. Liesenborgs, A. Barzan, M. Croonen, W. Lamotte, B. Vankeirsbilck, et al. 2016. Remote rendering solutions using web technologies. Multimedia Tools & Applications, 75(8), 1--28.
[11]
H. Fecher, M. Huth, N. Piterman, and D. Wagner. 2010. Pctl model checking of markov chains: truth and falsity as winning strategies in games. Performance Evaluation, 67(9), 858--872.
[12]
M. Guo, D. V. Dimarogonas. 2015. Multi-agent plan reconfiguration under local ltl specifications. International Journal of Robotics Research, 34(2), 218--235.
[13]
L. Zhang, et al. 2011. Automata-based CSL model checking. International Conference on Automata, Languages and Programming Springer-Verlag, 271--282.
[14]
W. Liu, B. Gong, and Y. Hu. 2011. A large-scale rendering system based on hadoop. International Conference on Pervasive Computing and Applications IEEE, 2011, 470--475.
[15]
W. Zhou, et al. 2012. A New Software Architecture for Ultra-large-scale Rendering Cloud. International Symposium on Distributed Computing and Applications To Business, Engineering & Science IEEE, 2012, 196--199.
[16]
A. Hamlili. 2006. Reliability Evaluation and Prediction of Improvable Information and Communication etworks. Information and Communication Technologies. Ictta '06. IEEE, 3587--3592.
[17]
Y. Choe, E. Byon, and N. Chen. 2015. Importance Sampling for Reliability Evaluation With Stochastic Simulation Models. Technometrics, 57(3), 351--361.
[18]
N. Padmavathy, S. K. Chaturvedi. 2015. Reliability Evaluation of Mobile Ad Hoc Network: With and without Mobility Considerations. Procedia Computer Science, 46, 1126--1139.
[19]
Y. M. Ko, E. Byon. 2015. Reliability evaluation of large-scale systems with identical units. IEEE Transactions on Reliability, 64(1), 420--434.
[20]
K. Mukhina, A. Bezgodov. 2015. The Method for Real-time Cloud Rendering. Procedia Computer Science, 66, 697--704.
[21]
L. Zhu, A. Yue, and C. Zhou. 2015. A fast rendering method for 3d city building point cloud models. Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao/Journal of Computer-Aided Design and Computer Graphics, 27(8), 1443--1451.
[22]
K. K. Mohan, A. K. Verma, A. Srividya, et al. 2010. Integration of Black-Box and White-Box Modeling Approaches for Software Reliability Estimation. International Journal of Reliability Quality & Safety Engineering, 17(03), 261--273.
[23]
J. R. Annette, W. A. Banu, and P. S. Chandran. 2015. Rendering-as-a-Service: Taxonomy and Comparison. Procedia Computer Science, 50, 276--281.
[24]
S. Singh, I. Chana. 2015. QoS-Aware Autonomic Resource Management in Cloud Computing: A Systematic Review. Acm Computing Surveys, 48(3), 1--46.
[25]
B. G. Batista, C. H. G. Ferreira, D. C. M. Segura, et al. 2016. A QoS-driven approach for cloud computing addressing attributes of performance and security. Future Generation Computer Systems, 68, 260--274.
[26]
S. Akshay, T. Antonopoulos, J. Ouaknine, et al. 2015. Reachability problems for Markov chains. Information Processing Letters, 115(2), 155--158.
[27]
M. Iannelli, A. Pugliese. 2014. Continuous-time Markov chains. An Introduction to Mathematical Population Dynamics. Springer International Publishing, 135--164.
[28]
J. P. Katoen. 2013. Model checking meets probability: a gentle introduction. Engineering Dependable Software Systems.
[29]
C. Daws. 2005. Symbolic and Parametric Model Checking of Discrete-Time Markov Chains. Theoretical Aspects of Computing - ICTAC 2004. Springer Berlin Heidelberg, 280--294.
[30]
E. M. Hahn, H. Hermanns, and L. Zhang. 2011. Probabilistic reachability for parametric markov models. International Journal on Software Tools for Technology Transfer, 13(1), 3--19.
Index Terms
- An Novel Approach to Evaluate the Reliability of Cloud Rendering System Using Probabilistic Model Checker PRISM: A Quantitative Computing Perspective
Recommendations
Quantitative Analysis With the Probabilistic Model Checker PRISM
Probabilistic model checking is a formal verification technique for establishing the correctness, performance and reliability of systems which exhibit stochastic behaviour. As in conventional verification, a precise mathematical model of a real-life ...
Quantifying the Cloud Computing Reliability Using a Randomizer
CICN '14: Proceedings of the 2014 International Conference on Computational Intelligence and Communication NetworksCloud computing is providing different types of services anytime as per its users demand from anywhere through the Internet and hence gaining more and more popularity day by day. With the help of cloud computing, efficient utilization of computing ...
Comments
Information & Contributors
Information
Published In
November 2017
555 pages
ISBN:9781450353687
DOI:10.1145/3144457
- General Chairs:
- Tao Gu,
- Ramamohanarao Kotagiri,
- Publications Chair:
- Huai Liu
Copyright © 2017 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
In-Cooperation
- EAI: The European Alliance for Innovation
- SIGMM: ACM Special Interest Group on Multimedia
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 07 November 2017
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
MobiQuitous 2017
Acceptance Rates
Overall Acceptance Rate 26 of 87 submissions, 30%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 76Total Downloads
- Downloads (Last 12 months)5
- Downloads (Last 6 weeks)0
Reflects downloads up to 01 Mar 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in