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Adaptive application offloading for QoS maximization in cloud-fog environment with delay-constraint

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Abstract

The IoT devices with advanced computational powers supported by Artificial Intelligence (AI) in diversified application domains require infrastructures that can deliver resources and computational services based on application needs. Fog computing has emerged as a platform for applications that can provide flexible and shared computational capabilities to such delay-sensitive services along with maintaining network availability, minimum latency, and Quality of Service (QoS) maximization. The proposed model offers the adaptive offloading of the applications while achieving the maximum QoS in a delay-constrained service environment. Moreover, to minimize the resource starvation condition, a probabilistic application task scheduler algorithm is presented, which considers the task’s initial priority and deadline. The tasks within waiting queues are assigned new priorities and used to select the most relevant task, reducing the response time and failure rate. Besides, a cost parameter is included in the proposed model that chooses the cost-efficient option for offloading the applications to the available servers. This work aims to maximize the overall QoS and optimize the service cost, which is demonstrated through simulation results. Our proposed model adaptive application offloading for QoS maximization with delay constraint (AAOQM-DC) yields a performance improvement by getting 0.11-2.49 sec. downfall in overall response time, 0.12- 1.38 sec. decrement in network delay, and enhanced QoS by achieving 0.07-5.12% lower failure rate in comparison to state-of-the-art approaches.

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Notes

  1. \(K_i\) resources are required by \(i^{th}\) request.

References

  1. Noura M, Atiquzzaman M, Gaedke M (2019) Interoperability in Internet of Things: Taxonomies and Open Challenges. Mobile Netw Appl 24:796–809

  2. Gedeon J, Brandherm F, Egert R, Grube T, Muhlhauser M (2019) What the Fog? Edge Computing Revisited: Promises, Applications and Future Challenges, IEEE Access 7:152847–152878

    Google Scholar 

  3. Aazam M, Zeadally S, Harras KA (2020) Health Fog for Smart Healthcare. IEEE Consumer Elec Magazine 9(2):96–102

    Article  Google Scholar 

  4. Chaudhry SR, Palade A, Kazmi A, Clarke S (2020) Improved QoS at the Edge Using Serverless Computing to Deploy Virtual Network Functions. IEEE Internet Things J 7(100):10673–10683

    Article  Google Scholar 

  5. Chauhan N, Agrawal R, Garg K (2022) Opportunities and challenges for smart healthcare system in fog computing. Computational Intelligence in Healthcare Applications 13–31

  6. Naeem RZ, Bashir S, Amjad MF, Abbas H, Afzal H (2019) Fog computing in internet of things: Practical applications and future directions. Peer-to-Peer Netw Appl 12(5):1236–1262

    Article  Google Scholar 

  7. Najafizadeh A, Salajegheh A, Rahmani AM (2021) Privacy-preserving for the internet of things in multi-objective task scheduling in cloud-fog computing using goal programming approach Peer-to-Peer Netw Appl 14:3865–3890

  8. Wu H, Sun Y, Wolter K (2020) Energy-Efficient Decision Making for Mobile Cloud Offloading. IEEE Trans Cloud Comput 8(2):570–584

    Article  Google Scholar 

  9. Lyu X, Tian H, Jiang L, Vinel A, mahaarjan S, Gjessing S, Zhang Y (2018) Selective Offloading in Mobile Edge Computing for the Green Internet of Things. IEEE Netw 32(1):54–60

  10. Wang Q, Guo S, Yang Y (2019) Energy-Efficient Computation Offloading and Resource Allocation for Delay-sensitive Mobile Edge Computing. J Sustainable Computing: Informatics and Systems 21:154–164

    Google Scholar 

  11. Ommeren J-KV, Baer N, Mishra N, Roy B (2020) Batch service systems with heterogeneous servers. Queueing Syst 95:251–269

  12. Garg K, Chauhan N, Agrawal R (2022) Optimized Resource Allocation for Fog Network using Neuro-fuzzy Offloading Approach. Arab J Sci Eng 47:10333–10346

  13. Samanta A, Tang J (2020) Dyme: Dynamic Microservice Scheduling in Edge Computing Enabled IoT. IEEE Internet Things J 7(7):6164–6174

    Article  Google Scholar 

  14. Adhikari M, Mukherjee M, Srirama SN (2020) DPTO: A Deadline and Priority-Aware Task Offloading in Fog Computing Framework Leveraging Multilevel Feedback Queueing. IEEE Internet Things J 7(7):5773–5782

    Article  Google Scholar 

  15. Wei J, Zeng XF (2019) Optimal computing resource allocation algorithm in cloud computing based on hybrid differential parallel scheduling. Cluster Comput 22:7577–7583

  16. Tawalbeh L, Jararweh Y, Ababneh F, Dosari F (2015) Large scale cloudlets deployment for efficient mobile cloud computing. J Netw 10(1):70–76

  17. Mukherjee A, De D, Roy DG (2019) Power and latency aware cloudlet selection strategy for multi-cloudlet environment. IEEE Trans Cloud Comput 7(1):141–154

    Article  Google Scholar 

  18. Chunlin L, Jianhang T, Tang H, Luo Y (2019) Collaborative cache allocation and task scheduling for data-intensive applications in edge computing environment. Future Gen Comput Sys 95:249–264

    Article  Google Scholar 

  19. Yang L, Zhang H, Li M, Guo J, Ji H (2018) Mobile Edge Computing Empowered Energy Efficient Task Offloading in 5G. IEEE Trans Veh Technol 67(7):6398–6409

    Article  Google Scholar 

  20. Guo M, Guan Q, Ke W (2018) Optimal scheduling of VMs in Queueing Cloud Computing Systems with a Heterogeneous Workload. IEEE Access 6:15178–15191

  21. Samanta A, Chang Z, Han Z (2018) Latency-Oblivious Distributed Task Scheduling for Mobile Edge Computing, IEEE Global Comm. Conf. (GLOBECOM), Abu Dhabi, United Arab Emirates, 1-7

  22. Li L, Guan Q, Jin L, Guo M (2019) Resource Allocation and Task Offloading for Heterogeneous Real-Time Tasks With Uncertain Duration Time in a Fog Queueing System. IEEE Access 7:9912–9925

    Article  Google Scholar 

  23. Mahmud R, Ramamohannaro K, Buyya R (2018) Latency-Aware Application Module Management for Fog Computing Environments. ACM Trans Int Tech 19(1). Article 9, 1-21

  24. Memari P, Mohammadi SS, Jolai F, T-Moghaddam R (2022) A latency-aware task scheduling algorithm for allocating virtual machines in a cost-effective and time-sensitive fog-cloud architecture. J Supercomput 78:93–122

  25. Wang Q, Guo S, Yang Y (2019) Energy-Efficient Computation Offloading and Resource Allocation for Delay-sensitive Mobile Edge Computing. J Sustainable Computing: Informatics and Systems 21:154–164

    Google Scholar 

  26. Mao Y, Zhang J, Letaief KB (2016) Dynamic Computation Offloading for Mobile-Edge Computing With Energy Harvesting Devices. IEEE J Sel Areas Commun 34(120):3590–3605

  27. Zhao P, Tian H, Qin C, Nie G (2017) Energy-Saving Offloading by Jointly Allocating Radio and Computational Resources for Mobile Edge Computing. IEEE Access 5:11255–11268

    Article  Google Scholar 

  28. Samanta A, Chang Z (2019) Adaptive Service Offloading for Revenue Maximization in Mobile Edge Computing With Delay-Constraint. IEEE Internet Things J 6(2):3864–3872

    Article  Google Scholar 

  29. Chauhan N, Banka H, Agrawal R (2021) Delay-aware application offloading in fog environment using multi-class Brownian model. Wireless Netw 27:4479–4495

  30. Sonmez C, Ozgovde A, Ersoy C (2019) Fuzzy Workload Orchestration for Edge Computing. IEEE Trans Netw Serv Mgmt 16:769–782

  31. Yousefpour A, Fung C, Nguyen T, Kadiyala K, Jalali F, Niakanlahiji A, Kong J, Jue JP (2019) All one needs to know about fog computing and related edge computing paradigms: A complete survey. J Syst Arch 98:289–330

    Article  Google Scholar 

  32. Chiang M, Ha S, I C-L, Risso F, Zhang T (2017) Clarifying Fog Computing and Networking: 10 Questions and Answers. IEEE Commun Mag 55(4):18–20

  33. Gai K, Qin X, Zhu L (2021) An Energy-Aware High Performance Task Allocation Strategy in Heterogeneous Fog Computing Environments. IEEE Trans Comput 70(4):626–639

    Article  MATH  Google Scholar 

  34. Chauhan N, Banka H, Agrawal R (2021) Adaptive bandwidth adjustment for resource constrained services in fog queueing system. Cluster Comput 24:3837–3850

  35. Moody GB, Mark RG (1996) A Database to Support Development and Evaluation of Intelligent Intensive Care Monitoring. Comput Cardiol 23:657–660

  36. Silva M, Freitas D, Neto E, Lins C, Teichrieb V, Teixeira JM (2014) Glassist: Using Augmented Reality on Google Glass as an Aid to Classroom Management, XVI Sym. on Virtual and Augmented Reality, 37-44, Piata Salvador

  37. Bhogal AS, Mani AR (2017) Pattern Analysis of Oxygen Saturation Variability in Healthy Individuals: Entropy of Pulse Oximetry Signals Carries Information about Mean Oxygen Saturation. Front Physiol 8(555):1–9

  38. Sonmez C, Ozgovde A, Ersoy C (2018) EdgeCloudSim: An environment for performance evaluation of Edge Computing systems. Trans Emerging Tele Techn 29(11):1–17

    Google Scholar 

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Naveen Chauhan: Investigation, Proposed Framework designing, Conceptualization, Writing-original draft, Result simulation, Result compilation, Validation. Rajeev Agrawal: Supervision, Conceptualization, Result compilation, Quality check. Haider Banka: Supervision, Conceptualization, Result compilation, Quality check.

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Correspondence to Naveen Chauhan.

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Chauhan, N., Agrawal, R. & Banka, H. Adaptive application offloading for QoS maximization in cloud-fog environment with delay-constraint. Peer-to-Peer Netw. Appl. 16, 1010–1026 (2023). https://doi.org/10.1007/s12083-023-01452-6

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