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Task scheduling for improved response time of latency sensitive applications in fog integrated cloud environment

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Abstract

Fog integrated Cloud Computing is a distributed computing paradigm where near-user end devices known as fog nodes cooperate with cloud resources hosted at distant datacentres for providing computational and storage services to end user applications. One of the most challenging issues in fog integrated cloud based system is task scheduling. Most of the existing scheduling approaches involve centralized decision making which fail to exploit the advantages that may be achieved by a decentralized approach, that directly maps with the distributed architecture of fog based systems. This work proposes a decentralized heuristic algorithm for scheduling real-time IoT applications bounded by tolerable latency as the Quality of Service (QoS) constraint. The proposed technique aims to take into consideration the resource constraints of the fog resources to yield a schedule that not only meets the QoS requirements defined in terms of tolerable latency but also improves the response time of applications hosted on a fog-cloud infrastructure. Performance evaluation on different IoT applications indicate that the presented algorithm delivers better performance by reducing response time by 11% on an average in comparison to the other state-of-the-art policies.

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References

  1. Architecture Working Group OpenFog Consortium (2017) Openfog reference architecture for fog computing. OPFRA001 20817, 162

  2. Ashrafi TH, Hossain MA, Arefin SE, Das KD, Chakrabarty A (2018) IoT Infrastructure: Fog Computing Surpasses Cloud Computing. In: Iot infrastructure: fog computing surpasses cloud computing, In intelligent communication and computational technologies (pp. 43–55). Springer, Singapore

    Google Scholar 

  3. Basu S, Karuppiah M, Selvakumar K, Li KC, Islam SH, Hassan MM, Bhuiyan MZA (2018) An intelligent/cognitive model of task scheduling for IoT applications in cloud computing environment. Futur Gener Comput Syst 88:254–261

    Article  Google Scholar 

  4. Biswas R, Giaffreda R (2014) IoT and cloud convergence: opportunities and challenges. In 2014 IEEE world forum on internet of things (WF-IoT) (pp. 375-376). IEEE

  5. Bitam S, Zeadally S, Mellouk A (2018) Fog computing job scheduling optimization based on bees swarm. Enterprise Inform Syst 12(4):373–397

    Article  Google Scholar 

  6. Bonomi F, et al (2012) "Fog computing and its role in the internet of things." Proceedings of the first edition of the MCC workshop on Mobile cloud computing

  7. Brogi A, Forti S (2017) QoS-aware deployment of IoT applications through the fog. IEEE Internet Things J 4(5):1185–1192

    Article  Google Scholar 

  8. Cai H, Xu B, Jiang L, Vasilakos AV (2016) IoT-based big data storage systems in cloud computing: perspectives and challenges. IEEE Internet Things J 4(1):75–87

    Article  Google Scholar 

  9. Cisco delivers vision of fog computing to accelerate value from billions of connected devices (2014) Press release. Cisco. [Online]. Available: http://newsroom.cisco.com/release/1334100/Cisco-Delivers-Vision-of-Fog-Computing-to-Accelerate-Value-from-Billionsof-Connected-Devices-utm-medium-rss.

  10. Craciunescu R, Mihovska A, Mihaylov M, Kyriazakos S, Prasad R, Halunga S (2015) Implementation of fog computing for reliable E-health applications. In 2015 49th Asilomar conference on signals, systems and computers (pp. 459-463). IEEE

  11. Dastjerdi AV, Buyya R (2016) Fog computing: helping the internet of things realize its potential. Computer 49(8):112–116

    Article  Google Scholar 

  12. Deng R, Lu R, Lai C, Luan TH, Liang H (2016) Optimal workload allocation in fog-cloud computing toward balanced delay and power consumption. IEEE Internet Things J 3(6):1171–1181

    Google Scholar 

  13. Doukas C, Maglogiannis I (2012) Bringing IoT and cloud computing towards pervasive healthcare. In 2012 sixth international conference on innovative Mobile and internet Services in Ubiquitous Computing (pp. 922-926). IEEE

  14. El Kafhali S, Salah K (2017) Efficient and dynamic scaling of fog nodes for IoT devices. J Supercomput 73(12):5261–5284

    Article  Google Scholar 

  15. Goswami P, Mukherjee A, Maiti M, Tyagi SKS, Yang L (2021) A neural network based optimal resource allocation method for secure IIoT network. IEEE Internet of Things Journal

  16. Gu L, Zeng D, Guo S, Barnawi A, Xiang Y (2015) Cost efficient resource management in fog computing supported medical cyber-physical system. IEEE Trans Emerg Top Comput 5(1):108–119

    Article  Google Scholar 

  17. Guerrero C, Lera I, Juiz C (2019) A lightweight decentralized service placement policy for performance optimization in fog computing. J Ambient Intell Humaniz Comput 10(6):2435–2452

    Article  Google Scholar 

  18. Guevara JC, da Fonseca NL (2021) Task scheduling in cloud-fog computing systems. Peer-to-Peer Network Appl 14(2):962–977

    Article  Google Scholar 

  19. Gupta H, Vahid Dastjerdi A, Ghosh SK, Buyya R (2017) iFogSim: a toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Prac Exp 47(9):1275–1296

    Google Scholar 

  20. Johnson DS, Garey M (1979) Computers and intractability: a guide to the theory of NP-completeness. Freeman&Co, San Francisco

    MATH  Google Scholar 

  21. Liu L, Chang Z, Guo X, Mao S, Ristaniemi T (2017) Multiobjective optimization for computation offloading in fog computing. IEEE Int Things J 5(1):283–294

    Article  Google Scholar 

  22. Lord SR, Sherrington C, Menz HB, Close JC (2007) Falls in older people: risk factors and strategies for prevention. Cambridge University Press, Cambridge, GB

    Book  Google Scholar 

  23. Mahmud R, Kotagiri R, Buyya R (2018) Fog computing: a taxonomy, survey and future directions. In: Internet of everything (pp. 103–130). Springer, Singapore

  24. Mahmud R, Ramamohanarao K, Buyya R (2018) Latency-aware application module management for fog computing environments. ACM Transac Int Technol (TOIT) 19(1):1–21

    Google Scholar 

  25. Maiti M, Krakovich V, Shams SR, Vukovic DB (2020) Resource-based model for small innovative enterprises. Manag Decis 58:1525–1541

    Article  Google Scholar 

  26. Nguyen T, Doan K, Nguyen G, Nguyen BM (2020) Modeling multi-constrained fog-cloud environment for task scheduling problem. In 2020 IEEE 19th international symposium on network computing and applications (NCA) (pp. 1-10). IEEE

  27. Ni L, Zhang J, Jiang C, Yan C, Yu K (2017) Resource allocation strategy in fog computing based on priced timed petri nets. IEEE Internet Things J 4(5):1216–1228

    Article  Google Scholar 

  28. Pham XQ, Man ND, Tri NDT, Thai NQ, Huh EN (2017) A cost-and performance-effective approach for task scheduling based on collaboration between cloud and fog computing. Int J Distri Sensor Netw 13(11):1550147717742073

    Article  Google Scholar 

  29. Ramasubbareddy S, Sasikala R (2019) RTTSMCE: a response time aware task scheduling in multi-cloudlet environment. International journal of computers and applications, 1-6

  30. Schad J, Dittrich J, Quiané-Ruiz JA (2010) Runtime measurements in the cloud: observing, analyzing, and reducing variance. Proc VLDB Endowment 3(1–2):460–471

    Article  Google Scholar 

  31. Skarlat O, Nardelli M, Schulte S, Borkowski M, Leitner P (2017) Optimized IoT service placement in the fog. SOCA 11(4):427–443

    Article  Google Scholar 

  32. Slabicki M, Grochla K (2016) Performance evaluation of CoAP, SNMP and NETCONF protocols in fog computing architecture. In NOMS 2016-2016 IEEE/IFIP network operations and management symposium (pp. 1315-1319). IEEE

  33. Stavrinides GL, Karatza HD (2019) A hybrid approach to scheduling real-time IoT workflows in fog and cloud environments. Multimed Tools Appl 78(17):24639–24655

    Article  Google Scholar 

  34. Suznjevic M, Saldana J (2015) Delay limits for real-time services

    Google Scholar 

  35. Taneja M, Jalodia N, Davy A (2019) Distributed decomposed data analytics in fog enabled IoT deployments. IEEE Access 7:40969–40981

    Article  Google Scholar 

  36. Vermesan O, Friess P (2014) Internet of things applications-from research and innovation to market deployment. Taylor & Francis, p 364

  37. Wang L, Ranjan R (2015) Processing distributed internet of things data in clouds. IEEE Cloud Comput 2(1):76–80

    Article  Google Scholar 

  38. Wild D, Nayak U, Isaacs B (1981) How dangerous are falls in old people at home? Br Med J (Clin Res Ed) 282(6260):–266

  39. Yang Y, Zhao S, Zhang W, Chen Y, Luo X, Wang J (2018) DEBTS: delay energy balanced task scheduling in homogeneous fog networks. IEEE Internet Things J 5(3):2094–2106

    Article  Google Scholar 

  40. Yi S, et al (2015) "Fog computing: Platform and applications." 2015 Third IEEE workshop on hot topics in web systems and technologies (HotWeb). IEEE

  41. Zeng D, Gu L, Yao H (2020) Towards energy efficient service composition in green energy powered cyber–physical fog systems. Futur Gener Comput Syst 105:757–765

    Article  Google Scholar 

  42. Zhang T, Wang J, Liu P, Hou J (2006) Fall detection by embedding an accelerometer in cellphone and using KFD algorithm. Int J Comput Sci Network Sec 6(10):277–284

    Google Scholar 

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Authors and Affiliations

  1. The NorthCap University, Gurugram, India

    Rishika Mehta

  2. Victoria University of Wellington, Wellington, New Zealand

    Jyoti Sahni

  3. Delhi Skill and Entrepreneurship University, New Delhi, India

    Kavita Khanna

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  1. Rishika Mehta
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  2. Jyoti Sahni
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  3. Kavita Khanna
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Correspondence to Rishika Mehta.

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Mehta, R., Sahni, J. & Khanna, K. Task scheduling for improved response time of latency sensitive applications in fog integrated cloud environment. Multimed Tools Appl 82, 32305–32328 (2023). https://doi.org/10.1007/s11042-023-14565-0

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