Total views : 248

An Efficient Method for estimation of cost in cloud computing using neural network


  • Chandigarh University, Gharuan - 140413, Punjab, India


Objectives: To study the cost estimation using FFNN (Feed Forward Neural Network) and BPNN (Back Propagation Neural Network). Methods/Statistical Analysis: In proposed work, resource allocation has been done in which cost function has been estimated. The whole simulation is being done using MATLAB 2010a environment. Findings: From the simulation results, it is analyzed that using FFNN, 95% of accuracy is achieved. Application/Improvements: With the advent of this technology, the cost of computation, application hosting, proper storage of content and delivery is abridged considerably.


Back Propagation Neural Network, Cloud Computing, Cost Estimation, Feed Forward Neural Network, Resource Allocation.

Full Text:

 |  (PDF views: 237)


  • Raju IR, Varma PS, Sundari MV, Moses GJ. Deadline aware two stage scheduling algorithm in cloud computing. Indian Journal of Science and Technology. 2016; 9(4):1–10.
  • Madni SHH, Latiff MS, Coulibaly Y, Abdulhamid SM. An appraisal of meta-heuristic resource allocation techniques for IaaS cloud. Indian Journal of Science and Technology. 2016; 9(4):1–14.
  • Theja PR, Babu SKK. An evolutionary computing based energy efficient VM consolidation scheme for optimal resource utilization and QoS assurance. 2015; 8(26):1–11.
  • Shyamala K, Rani TS. An analysis on efficient resource allocation mechanisms in cloud computing. Indian Journal of Science and Technology. 2015; 8(9):1–8.
  • Madni SHH, Latiff MSA, Coulibaly Y, Abdulhamid SM. An appraisal of meta-heuristic resource allocation techniques for IaaS Cloud. IJST. 2016; 9(4):1–14.
  • Kirubakaramoorthi R, Arivazhagan D, Helen D. Analysis of cloud computing technology. IJST. Indian Journal of Science and Technology. 2015; 8(21):1–3.
  • Man CT, Kayashima M. Virtual machine placement algorithm for virtulized desktop infrastructure, Proceedings of IEEE CCIS; 2011. p. 334–7.
  • Lin C, Liu P, Wu J. Energy-aware virtual machine dynamic provision and scheduling for cloud computing. IEEE 4th International Conference on Cloud Computing; 2011. p. 736–7.
  • Jadeja Y, Modi K. Cloud computing-concepts, architecture and challenges. 2012 International Conference on Computing, Electronics and Electrical Technologies (ICCEET); 2012. p. 877–80.
  • Buyya R, Yeo CS, Venugopal S, Broberg J, Brandic I. Cloud computing and emerging IT platforms: Vision, hype and reality for delivering computing as the 5th utility. Futur Gener Comput Syst. 2009; 25(6):599–616.
  • Yamini B, Selvi DV. Cloud virtualization: A potential way to reduce global warming. Recent Advances in Space Technology Services and Climate Change (RSTSCC); 2010 Nov 13-15. p. 55–7.
  • Witkowski M, Brenner P, Jansen R, Go DB, Ward E. Enabling sustainable clouds via environmentally opportunistic computing. 2010 IEEE 2nd International Conference on Cloud Computing Technology and Science (CloudCom); 2010 Nov 30-Dec 3. p. 587–92.
  • Cavdar D, Alagoz F. A survey of research on greening data centers. 2012 IEEE Global Communications Conference (GLOBECOM); 2012 Dec3-7. p. 3237–42.
  • Ding Y, Qin X, Liu L, Wang T. Energy efficient scheduling of virtual machines in cloud with deadline constraint. Science Direct. 2015; 50:62–74.
  • Zhu X, Yang LT, Wang HCJ, Yin S, Liu XC. Real-time tasks oriented energy-aware scheduling in virtualized clouds. IEEE Transactions on Cloud Computing. 2014; 2(2).
  • Fang JTTJC, Chou JH. Optimized task scheduling and resource allocation on cloud computing environment uses Improved Differential Evolution Algorithm (IDEA). Computers and Operations Research Elseveir. 2014; 40(12):3045–55.
  • Xiao Z, Song W, Chen Q. Dynamic resource allocation using virtual machines for cloud computing environment. IEEE Transactions Parallel and Distributed Systems. 2013; 24(6):1107–17.
  • Wu MC, Shiung R, Chan YH. A green energy-efficient scheduling algorithm using the DVFS technique for cloud datacenters. Future Generation Computer Systems. 2014; 37:141–7.
  • Patel R, Patel, Patel S. Quality of service based efficient resource allocation in cloud computing. International Journal for Technological Research in Engineering. 2015; 2(9).
  • Manasa HB, Basu A. Energy aware resource allocation in cloud datacenter. IJEAT. 2013; 2(5).
  • Fiandrino C, Kliazovich D, Zomaya BP. Performance and energy efficiency metrics for communication systems of cloud computing data centers. IEEE Transactions on Cloud Computing. 2015; 99:1–1.
  • Dabbagh M, Hamdaoui B, Guizani M, Rayes A. Towards energy-efficient cloud computing: Prediction, consolidation, and over commitment. IEEE Network. 2015; 29(2):56–61.
  • Li Q, Hao Q, Xiao L, Li Z. Adaptive management of virtualized resources in cloud computing using feedback control. 1st International Conference on Information Science and Engineering; 2009Apr. p. 99–102.
  • Walsh WE, Tesauro G, Kephart JO, Das R. Utility functions in autonomic systems. ICAC ’04: Proceedings of the First International Conference on Autonomic Computing; IEEE Computer Society. 2004. p. 70–7.
  • Li J, Qiu M, Niu JW, Chen Y, Ming Z. Adaptive resource allocation for preempt able jobs in cloud systems. 10th International Conference on Intelligent System Design and Application; 2011 Jan. p. 31–6.
  • Shi JY, Taifi M, Khreishah A. Resource planning for parallel processing in the cloud. IEEE 13th International Conference on High Performance and Computing; 2011 Nov. p. 828–33.
  • Goudarzi H, Pedram M. Multi-dimensional SLA-based resource allocation for multi-tier cloud computing systems. IEEE International Conference on Cloud Computing; 2011 Sep. p. 324–31.
  • Jernal A, Rajkumar. Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Future Generation Computer Systems. 2012 May; 28(5):755–68.
  • Rager M, Gahm C, Denz F. Energy-oriented scheduling based on evolutionary algorithms. Computer and Operations Research. 2015; 54:218–31.
  • Patel YS, Mehrotra N, Soner S. Green cloud computing: A review on Green IT areas for cloud computing environment. 2015 International Conference on Futuristic Trends on Computational Analysis and Knowledge Management (ABLAZE); 2015 Feb25-27. p. 327–32.
  • Guazzone M, Anglano C, Canonico M. Energy-efficient resource management for cloud computing infrastructures. 2011 IEEE Third International Conference on Cloud Computing Technology and Science (CloudCom); 2011. p. 424–31.
  • Beloglazov A, Buyya R. Energy efficient allocation of virtual machines in cloud data centers. 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing (CCGrid); 2010 May17-20. p. 577–8.
  • Abrahamsson P, Helmer S, Phaphoom N, Nicolodi L, Preda N, Miori L, Angriman M, Rikkila J, Wang X, Hamily K, Bugoloni S. Affordable and energy-efficient cloud computing clusters: the bolzano raspberry pi cloud cluster experiment. 2013 IEEE 5th International Conference on Cloud Computing Technology and Science (CloudCom); 2013. p. 170–5.
  • Bo A, Victor L, David I, Michael Z. Automated Negotiation with Decommitment for Dynamic Resource Allocation in Cloud Computing. Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems; 2010. p. 981–8.
  • Buyya R, Shin C. Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility. Future Generation Computer Systems. 2009; 25(6):599–16.
  • Wahul RM, Kurawale S, Joshi A, Langhe P, Aher S. Load balancing of resources using virtual machines in a cloud computing environment. IJERMT. 2015; 5:63–33.
  • Sharkh AM, Jammal M, Shami A, Ouda A. Resource allocation in a network-based cloud computing environment: design challenges. IEEE Communications Magazine. 2013; 51(11):46–52.
  • Rai A, Bagwan R, Guha S. Generalized resource allocation for the cloud. ACM Proceedings of the 3rd ACM Symposium on Cloud Computing; 2012. p. 15.


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.