Prediction and Monitoring Agents using Weblogs for improved Disaster Recovery in Cloud

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Rushba Javed 1,* Sidra Anwar 1 Khadija Bibi 1 M. Usman Ashraf 1 Samia Siddique 1

1. GC Women University Sialkot/ Department of Computer Science & Information Technology, Pakistan

* Corresponding author.


Received: 22 Dec. 2018 / Revised: 10 Jan. 2019 / Accepted: 20 Jan. 2019 / Published: 8 Apr. 2019

Index Terms

Cloud Computing, Disaster, Prediction, Monitoring, Disaster Recovery, Prediction Agent, Monitoring Agent, Monitoring and Prediction using Weblog, Server virtualization, Monitor Downtime, Optimize Recovery time


Disaster recovery is a continuous dilemma in cloud platform. Though sudden scaling up and scaling down of user’s resource requests is available, the problem of servers down still persists getting users locked at vendor’s end. This requires such a monitoring agent which will reduce the chances of disaster occurrence and server downtime. To come up with an efficient approach, previous researchers’ techniques are analyzed and compared regarding prediction and monitoring of outages in cloud computing. A dual functionality Prediction and Monitoring Agent is proposed to intelligently monitor users’ resources requests and to predict coming surges in web traffic using Linear Regression algorithm. This solution will help to predict the user’s future requests’ behavior, to monitor current progress of resources’ usage, server virtualization and to improve overall disaster recovery process in Cloud Computing.

Cite This Paper

Rushba Javed, Sidra Anwar, Khadija Bibi, M. Usman Ashraf, Samia Siddique, "Prediction and Monitoring Agents using Weblogs for improved Disaster Recovery in Cloud", International Journal of Information Technology and Computer Science(IJITCS), Vol.11, No.4, pp.9-17, 2019. DOI:10.5815/ijitcs.2019.04.02


[1]T. G. Peter Mell (NIST), "The NIST Definition of Cloud Computing," September 2011. 

[2]M. G. Avram, "Advantages and Challenges of Adopting Cloud Computing from Enterprise," 2014. 

[3]S. e. a. Jafar, "Identifying Benefits and Risks Associated with Utilizing Cloud Computing," 2013. 

[4]F. T. W. X. Colin Ting Si Xue, "BENEFITS AND CHALLENGES OF THE ADOPTION OF," International Journal on Cloud Computing: Services and Architecture (IJCCSA), December 2016. 

[5]T. Harris, "CLOUD COMPUTING – An Overview," 2011. 

[6]M. PERLIN, "Downtime, Outages and Failures - Understanding Their True Costs," 17 September 2012. [Online]. Available: [Accessed 19 November 2018].

[7]S. S. M. E. M. C. H. M. K. C. Rodrigo de Souza Couto, "Network Design Requirements for Disaster Resilience in IaaS Clouds," IEEE Communications Magazine, vol. 52, no. 10, p. 52 – 58 , October 2014.

[8]K. Morrison, "90% of Companies Have Experienced Unexpected Downtime [Infographic]," ADWEEK, 17 October 2014. [Online]. Available: [Accessed 19 November 2018].

[9]M. PERLIN, "Downtime, Outages and Failures - Understanding Their True Costs," 17 September 2012. [Online]. Available: [Accessed 19 November 2018].

[10]R. Troutman, "The History of Disaster Recovery," 23 November 2015. [Online]. Available: [Accessed 15 October 2018].

[11]N. Cornish, "A brief history of disaster recovery," 9 Junary 2016. [Online]. Available: [Accessed 15 October 2018].

[12]C.-S. M. S. L. S. &. T. D. Perng, "Data-driven Monitoring Design of Service Level," 9th IFIP/IEEE International Symposium on Integrated Network, 2005.

[13]A. V. d. W. D. &. L. L. Knobbe, "Experiments with data mining in enterprise," Proceedings of the Sixth IFIP/IEEE International Symposium on, 1999.

[14]R. V. B. K. &. V. W. A. Renesse, "A robust and scalable technology for distributed system monitoring, management, and data mining," ACM Transactions on Computer Systems (TOCS), vol. 21, no. 2, p. 164–206, May 2003.

[15]Vrishali P. Sonavane,”Study And Implementation Of LCS Algorithm For Web Mining”, International Journal of Computer Science Issues, Vol. 9, Issue 2, No 3, March 2012.

[16]VedpriyaDongre, JagdishRaikwal ,”An improved user browsing behavior prediction using web log analysis”, International Journal of Advanced Research in Computer Engineering and technology (IJARCET), Vol. 4, Issue 5, , May 2015.

[17]R. Khanchana and M. Punithavalli, “Web Usage Mining for Predicting Users’ Browsing Behaviors by using FPCM Clustering”, IACSIT International Journal of Engineering and Technology, Vol. 3, No. 5, October 2011.

[18]M. Jalali, N. Mustapha et al,” WebPUM: A Web-based recommendation system to predict user future movements”, in international journal Expert Systems with Applications 37 (2010) 6201–6212.

[19]S. T. Abhishek Chauhan, "Prediction of User Browsing Behavior Using Web Log Data," Gautam Buddha University, Uttar Pradesh, India, vol. 2, 2016.

[20]ShailyG.Langhnoja , Mehul P. Barot , Darshak B. Mehta,” Web Usage Mining Using Association Rule Mining on Clustered Data for Pattern Discovery, International Journal of Data Mining Techniques and Applications, Vol 02, Issue 01, June 2013.

[21]N. R. Information, "History of Disaster Recovery: Four Key Facts," 3 March 2015. [Online]. Available: [Accessed 14 December 2018].

[22]P. Croetti, "IT business continuity and disaster recovery: Past to future," 26 Febrary 2018. [Online]. Available: [Accessed 14 December 2018].

[23]R. DeVos, "50% of disaster recovery strategies will fail – Here’s how you can save yours," 10 October 2017. [Online]. Available: [Accessed 14 December 2018].

[24]J. K. P. S. &. M. H. Sami Nousiainen, "Anomaly detection from server log data A case study," VTT RESEARCH NOTES 2480, 2009. 

[25]P. M. B. Megha P. Jarkad, "Improved Web Prediction Algorithm Using Web Log Data," International Journal of Innovative Research in Computer and Communication Engineering, vol. III, no. 5, May 2015.