Skip to main content
SpringerLink
Log in

HPFE: a new secure framework for serving multi-users with multi-tasks in public cloud without violating SLA

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Efficient resource distribution for multiple users is one of the most challenging problems of the cloud service provider (CSP). This problem is more difficult when each user submits multi-tasks to the cloud environment. Hence, CSP needs an efficient framework to optimize multiple objectives, e.g., cost, time, and load balancing as well as to manage tasks of different users in a secure way. This paper presents a new efficient scheduling framework, called highest priority first execute (HPFE), for serving multiple users with multiple tasks in a secure and optimized way. Computational results show that the proposed HPFE reduces the makespan and increases the load balancing degree comparing to the most recent algorithms; Min–Min, Max–Min, minimum completion time, first come first serve, genetic algorithm and simulated annealing.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22

Similar content being viewed by others

References

  1. Mell P, Grance T (2009) The NIST definition of cloud computing. National Institute of Standards and Technology, Information Tech. Lab., October 2009. http://www.nist.gov/itl/cloud

  2. Kumar PM, Lokesh S, Varatharajan R, Babu GC, Parthasarathy P (2018) Cloud and IoT based disease prediction and diagnosis system for healthcare using fuzzy neural classifier. Future Gen Comput Syst 86:527–534

    Article  Google Scholar 

  3. Kumar PM, Devi U, Manogaran G, Sundarasekar R, Chilamkurti N, Varatharajan R (2018) Ant colony optimization algorithm with internet of vehicles for intelligent traffic control system. Comput Netw 144:154–162

    Article  Google Scholar 

  4. Kumar PM, Gandhi UD (2018) A novel three-tier internet of things architecture with machine learning algorithm for early detection of heart diseases. Comput Electr Eng 65:222–235

    Article  Google Scholar 

  5. Kumar PM, Gandhi UD (2017) Enhanced DTLS with CoAP-based authentication scheme for the internet of things in healthcare application. J Supercomput. https://doi.org/10.1007/s11227-017-2169-5

    Article  Google Scholar 

  6. Priyan MK, Devi GU (2018) Energy efficient node selection algorithm based on node performance index and random waypoint mobility model in internet of vehicles. Clust Comput 21(1):213–221

    Article  Google Scholar 

  7. Manogaran G, Varatharajan R, Lopez D, Kumar PM, Sundarasekar R, Thota C (2018) A new architecture of internet of things and big data ecosystem for secured smart healthcare monitoring and alerting system. Future Gen Comput Syst 82:375–387

    Article  Google Scholar 

  8. Sfondrini N, Motta G, You L (2015) Service level agreement (SLA) in public cloud environments: a survey on the current enterprises adoption. In: The 5th international conference on information science technology (ICIST), pp 181–185

  9. Nawaz F, Janjua NK, Hussain OK, Hussain FK, Chang E, Saberi M (2018) Event-driven approach for predictive and proactive management of SLA violations in the cloud of things. Future Gen Comput Syst 84:78–97

    Article  Google Scholar 

  10. Sharkh MA, Jammal M, Shami A, Ouda A (2013) Resource allocation in a network-based cloud computing environment: design challenges. IEEE Commun Mag 51(11):46–52

    Article  Google Scholar 

  11. Khan MA (2016) A survey of security issues for cloud computing. J Netw Comput Appl 71:11–29

    Article  Google Scholar 

  12. Singh A, Gupta P, Lonare R, Sharma RK, Ghodichor NA (2017) Data security in cloud computing. Int J Emerg Trends Eng Manag Res 3(2):1–5

    Google Scholar 

  13. Mathew T, Sekaran KC, Jose J (2014) Study and analysis of various task scheduling algorithms in the cloud computing environment. In: Proceedings of the international conference on advances in computing, communications and informatics (ICACCI), pp 658–664

  14. Kalra M, Singh S (2015) A review of metaheuristic scheduling techniques in cloud computing. Egypt Inf J 16(3):275–295

    Google Scholar 

  15. Keshanchi B, Souri A, Navimipour NJ (2017) An improved genetic algorithm for task scheduling in the cloud environments using the priority queues: formal verification, simulation, and statistical testing. J Syst Soft 124:1–21

    Article  Google Scholar 

  16. Kaur K, Chharbra A, Gurvinder S (2010) Heuristics based genetic algorithm for scheduling static tasks in homogeneous parallel system. J Comput Sci 4(2):183–198

    Google Scholar 

  17. Kaur S, Verma A (2012) An efficient approach to genetic algorithm for task scheduling in cloud computing environment. Int J Inf Technol Comput Sci 4(10):74–79

    Google Scholar 

  18. Carretero J, Xhafa F, Abraham A (2007) Genetic algorithm based schedulers for grid computing systems. Int J Innov Comput Inf Control 3(6):1–19

    Google Scholar 

  19. Su N, Shi A, Chen C, Chen E, Wang Y (2016) Research on virtual machine placement in the cloud based on improved simulated annealing algorithm. In: World automation congress (WAC), pp 1–7

  20. Addya SK, Turuk AK, Sahoo B, Sarkar M, Biswash SK (2017) Simulated annealing based VM placement strategy to maximize the profit for cloud service providers. Eng Sci Technol Int J 20(4):1249–1259

    Google Scholar 

  21. Abdullahi M, Ngadi MA (2016) Correction: hybrid symbiotic organisms search optimization algorithm for scheduling of tasks on cloud computing environment. PLoS One 11(8):e0162054

    Article  Google Scholar 

  22. Patel G, Mehta R, Bhoi U (2015) Enhanced load Balanced Min–Min algorithm for static meta task scheduling in cloud computing. Proc Comput Sci 57:545–553

    Article  Google Scholar 

  23. Derakhshan M, Bateni Z (2018) Optimization of tasks in cloud computing based on MAX–MIN, MIN–MIN and priority. In: The 4th international conference on web research (ICWR), pp 45–50

  24. Moggridge P, Helian N, Sun Y, Lilley M, Veneziano V, Eaves M (2017) Revising Max–Min for scheduling in a cloud computing context. In: IEEE 26th international conference on enabling technologies: infrastructure for collaborative enterprises (WETICE), pp 125–130

  25. Parsa S, Entezari-Maleki R (2009) RASA: a new task scheduling algorithm in grid environment. World Appl Sci J 7:152–160

    Google Scholar 

  26. Tseng Li-Ya, Chin Y-H, Wang S-C (2009) A minimized makespan scheduler with multiple factors for grid computing systems. Expert Syst Appl 36(8):11118–11130

    Article  Google Scholar 

  27. Dubey K, Kumar M, Sharma SC (2018) Modified HEFT algorithm for task scheduling in cloud environment. Proc Comput Sci 125:725–732

    Article  Google Scholar 

  28. Hu H, Li Z, Hu H, Chen J, Ge J, Li C, Chang V (2018) Multi-objective scheduling for scientific workflow in multicloud environment. J Netw Comput Appl 114:108–122

    Article  Google Scholar 

  29. Kaur M, Kadam S (2018) A novel multi-objective bacteria foraging optimization algorithm (MOBFOA) for multi-objective scheduling. Appl Soft Comput 66:183–195

    Article  Google Scholar 

  30. Nasr AA, El-Bahnasawy NA, Attiya G, El-Sayed A (2018) A new online scheduling approach for enhancing QOS in cloud. Future Comput Inf J 3(2):424–435

    Article  Google Scholar 

  31. https://www.fing.edu.uy/inco/grupos/cecal/hpc/HCSP/HCSP_inst.htm. Accessed May 2018

  32. Nasr AA, El-Bahnasawy NA, Attiya G, El-Sayed A (2018) Using the TSP solution strategy for cloudlet scheduling in cloud computing. J Netw Syst Manag. https://doi.org/10.1007/s10922-018-9469-9

    Article  Google Scholar 

  33. Nasr AA, Chronopoulos AT, El-Bahnasawy NA, Attiya G, El-Sayed A (2018) A novel water pressure change optimization technique for solving scheduling problem in cloud computing. Clust Comput. https://doi.org/10.1007/s10586-018-2867-7

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computer Science and Engineering Department, Faculty of Electronic Engineering, Menoufia University, Menouf, Egypt

    Aida A. Nasr, Nirmeen A. El-Bahnasawy, Gamal Attiya & Ayman El-Sayed

  2. Cloud Computing and Wireless Sensor Lab, Indian Institute of Technology Roorkee, Saharanpur Campus, Roorkee, India

    Kalka Dubey & S. C. Sharma

Authors
  1. Aida A. Nasr
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kalka Dubey
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nirmeen A. El-Bahnasawy
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. S. C. Sharma
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Gamal Attiya
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Ayman El-Sayed
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Aida A. Nasr.

Ethics declarations

Conflict of interest

The authors have no conflicts of interest to declare.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nasr, A.A., Dubey, K., El-Bahnasawy, N.A. et al. HPFE: a new secure framework for serving multi-users with multi-tasks in public cloud without violating SLA. Neural Comput & Applic 32, 6821–6841 (2020). https://doi.org/10.1007/s00521-019-04091-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-019-04091-2

Keywords

Search

Navigation