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Soft Computing

, Volume 23, Issue 24, pp 13591–13602 | Cite as

An efficient method for fault tolerance in cloud environment using encryption and classification

  • Vipul GuptaEmail author
  • Bikram Pal Kaur
  • Surender Jangra
Methodologies and Application
  • 44 Downloads

Abstract

Cloud computing may be defined as management and provision of resources, software, application and information as services over the cloud which are dynamically scalable. Fault tolerance includes all the techniques necessary for robustness and dependability. The main advantages of using fault tolerance in cloud computing include failure recovery, lower costs and improved standards in performance. Even though the benefits are immeasurable, the element of risk on user applications due to failure remains a major drawback. So our suggested technique utilizes the effective fault tolerance method with the encryption algorithm. To improve the security of the recommended technique, triple-DES encryption algorithm is employed before the data transmission. For the transmission of encrypted data, the implemented method selects the minimum fault tolerance node. So the recommended technique utilizes the effective classification technique. Here, improved support vector machine (ISVM) classifier is used to classify the nodes based on its feature value and the content similarity each node. The proposed ISVM helps in predicting the faults if available, earlier before it occurs. The various parameters considered in our proposed system are accuracy, service reliability and availability. In the proposed method, the accuracy value of the fault tolerance is 79% which is better than in the existing method. The proposed method will be implemented in JAVA with CloudSim.

Keywords

Cloud computing Fault tolerance Triple DES Support vector machine Accuracy Service Availability 

Notes

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical statement

I confirm that the manuscript has not been submitted to more than one journal for simultaneous consideration. The manuscript has not been published previously (partly or in full) unless the new work concerns an expansion of previous work.

References

  1. Abdulhamid SM, Abd Latiff MS, Madni SHH, Abdullahi M (2018) Fault tolerance aware scheduling technique for cloud computing environment using dynamic clustering algorithm. Neural Comput Appl 29(1):279–293CrossRefGoogle Scholar
  2. Abujarad F, Lin Y, Bonakdarpour B, Kulkarni SS (2015) The complexity of automated addition of fault-tolerance without explicit legitimate states. Distrib Comput 28(3):201–219MathSciNetCrossRefGoogle Scholar
  3. Anarado I, Andreopoulos Y (2016) Core failure mitigation in integer sum-of-product computations on cloud computing systems. IEEE Trans Multimed 18(4):789–801CrossRefGoogle Scholar
  4. Bhuiyan MZA, Wang G, Cao J, Wu J (2015) Deploying wireless sensor networks with fault-tolerance for structural health monitoring. IEEE Trans Comput 64(2):382–395MathSciNetCrossRefGoogle Scholar
  5. Bui DM, Huynh-The T, Lee S (2018) Early fault detection in IaaS cloud computing based on fuzzy logic and prediction technique. J Supercomput 74(11):5730–5745CrossRefGoogle Scholar
  6. Chen W, da Silva RF, Deelman E, Fahringer T (2012) Dynamic and fault-tolerant clustering for scientific workflows. IEEE Trans Cloud Comput 4(1):49–62CrossRefGoogle Scholar
  7. Choi S, Chung K, Yu H (2015) Fault tolerance and QoS scheduling using CAN in mobile social cloud computing. Clust Comput 17(3):911–926CrossRefGoogle Scholar
  8. Dean DJ, Nguyen H, Wang P, Gu X, Sailer A, Kochut A (2016) PerfCompass: online performance anomaly fault localization and inference in infrastructure-as-a-service clouds. IEEE Trans Parallel Distrib Syst 27(6):1742–1755CrossRefGoogle Scholar
  9. Deng W, Yao R, Zhao H, Yang X, Li G (2017a) A novel intelligent diagnosis method using optimal LS-SVM with improved PSO algorithm. Soft Comput 1–18Google Scholar
  10. Deng W, Zhao H, Zou L, Li G, Yang X, Wu D (2017b) A novel collaborative optimization algorithm in solving complex optimization problems. Soft Comput 21(15):4387–4398CrossRefGoogle Scholar
  11. Deng W, Zhao H, Yang X, Xiong J, Sun M, Li B (2017c) Study on an improved adaptive PSO algorithm for solving multi-objective gate assignment. Appl Soft Comput 59:288–302CrossRefGoogle Scholar
  12. Deng W, Zhang S, Zhao H, Yang X (2018) A novel fault diagnosis method based on integrating empirical wavelet transform and fuzzy entropy for motor bearing. IEEE Access 6:35042–35056CrossRefGoogle Scholar
  13. Gupta P, Banga S (2013) Topic-review of cloud computing in fault tolerant environment with efficient energy consumption. Int J Sci Res Manag (IJSRM) 1(4):251–254Google Scholar
  14. He J, Dong M, Ota K, Fan M, Wang G (2016) NetSecCC: a scalable and fault-tolerant architecture for cloud computing security. Peer-to-Peer Netw Appl 9(1):67–81CrossRefGoogle Scholar
  15. Karaca O, Sokullu R (2012) A cross-layer fault tolerance management module for wireless sensor networks. J Zhejiang Univ Sci 13(9):660–673CrossRefGoogle Scholar
  16. Latif K, Rahmani A-M, Nigussie E, Seceleanu T, Radetzki M, Tenhunen H (2015) Partial virtual channel sharing: a generic methodology to enhance resource management and fault tolerance in networks-on-chip. J Electron Test 29(3):431–452CrossRefGoogle Scholar
  17. Liu D (2015) A fault-tolerant architecture for ROIA in cloud. J Ambient Intell Humaniz Comput 6(5):587–595CrossRefGoogle Scholar
  18. Liu Y, Yi X, Chen R, Zhai Z, Gu J (2018) Feature extraction based on information gain and sequential pattern for English question classification. IET SoftwGoogle Scholar
  19. Menychtas A, Konstanteli KG (2012) Fault detection and recovery mechanisms and techniques for service oriented infrastructures. In: Achieving real-time in distributed computing: from grids to clouds, pp 259–274Google Scholar
  20. Muhra A, Vu QH, Asal R, Al Muhairi H, Yeun CY (2014) Lightweight secure storage model with fault-tolerance in cloud environment. Electron Commer Res 14(3):271–291CrossRefGoogle Scholar
  21. Sun D, Chang G, Miao C, Wang X (2013) Analyzing, modeling and evaluating dynamic adaptive fault tolerance strategies in cloud computing environments. J Supercomput 66(1):193–228CrossRefGoogle Scholar
  22. Wang J, Bao W, Zhu X, Yang LT, Xiang Y (2015) FESTAL: fault-tolerant elastic scheduling algorithm for real-time tasks in virtualized clouds. IEEE Trans Comput 64(9):2545–2558MathSciNetCrossRefGoogle Scholar
  23. Yang B, Tan F, Dai Y-S, Guo S (2009) Performance evaluation of cloud service considering fault recovery. Cloud Comput 571–576Google Scholar
  24. Yang C-T, Liu J-C, Hsu C-H, Chou W-L (2015) On improvement of cloud virtual machine availability with virtualization fault tolerance mechanism. J Supercomput 69(3):1103–1122CrossRefGoogle Scholar
  25. Zhang W, Xu L, Duan P, Gong W, Lu Q, Yang S (2015) A video cloud platform combing online and offline cloud computing technologies. Pers Ubiquit Comput 19(7):1099–1110CrossRefGoogle Scholar
  26. Zhao H, Sun M, Deng W, Yang X (2016) A new feature extraction method based on EEMD and multi-scale fuzzy entropy for motor bearing. Entropy 19(1):14CrossRefGoogle Scholar
  27. Zhao H, Yao R, Xu L, Yuan Y, Li G, Deng W (2018) Study on a novel fault damage degree identification method using high-order differential mathematical morphology gradient spectrum entropy. Entropy 20(9):682CrossRefGoogle Scholar
  28. Zheng Z, Zhou TC, Lyu MR, King I (2012) Component ranking for fault-tolerant cloud applications. IEEE Trans Serv Comput 5(4):540–550CrossRefGoogle Scholar
  29. Zhu X, Wang J, Guo H, Zhu D, Yang LT, Liu L (2016) Fault-tolerant scheduling for real-time scientific workflows with elastic resource provisioning in virtualized clouds. IEEE Trans Parallel Distrib Syst 27(12):3501–3517CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Vipul Gupta
    • 1
    Email author
  • Bikram Pal Kaur
    • 2
  • Surender Jangra
    • 3
  1. 1.Punjab Technical UniversityKapurthalaIndia
  2. 2.Department of Computer Science and EngineeringChandigarh Engineering CollegeMohaliIndia
  3. 3.Department of Computer ApplicationsGTB CollegeBhawanigarh (Sangrur)India

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