Abstract
Fog computing systems have been widely integrated in IoT-based applications to improve quality of services (QoS), such as low response service delays. This improvement is enabled by task offloading schemes, which perform task computation near the task generation sources (i.e., IoT devices) on behalf of remote cloud servers. However, reducing delay remains challenging for offloading strategies owing to the resource limitations of fog devices. In addition, a high rate of task requests combined with heavy tasks (i.e., large task size) may cause a high imbalance of the workload distribution among the heterogeneous fog devices, which severely impacts the offloading performance in terms of delay. To address this issue, this chapter proposes a dynamic cooperative task offloading approach called DCTO, which is based on the resource states of fog devices, to dynamically derive the task offloading policy. Accordingly, a task can be executed by either a single fog or multiple fog devices through the parallel computation of subtasks to reduce the task execution delay. Through extensive simulation analysis, the proposed approaches showed potential advantages in reducing the average delay significantly in systems with a high rate of service requests and heterogeneous fog environment compared with the existing solutions. In addition, the proposed scheme can be implemented online owing to its low computational complexity compared with the algorithms proposed in related works.
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References
Zanella A, Bui N, Castellani A, Vangelista L, Zorzi M (2014) Internet of things for smart cities. IEEE Internet Things J 1(1):22–32
Saleem Y, Crespi N, Rehmani MH, Copeland R (2019) Internet of things-aided smart grid: technologies, architectures, applications, prototypes, and future research directions. IEEE Access 7:62962–63003
Chekired DA, Khoukhi L, Mouftah HT (2018) Industrial iot data scheduling based on hierarchical fog computing: a key for enabling smart factory. IEEE Trans Industr Inform 14(10):4590–4602
Tran-Dang H, Krommenacker N, Charpentier P, Kim D-S (2022) The internet of things for logistics: perspectives, application review, and challenges. IETE Tech Rev 39:1–29
Tran-Dang H, Krommenacker N, Charpentier P, Kim D (2020) Toward the internet of things for physical internet: perspectives and challenges. IEEE Internet Things J 7(6):4711–4736
** J, Gubbi J, Marusic S, Palaniswami M (2014) An information framework for creating a smart city through internet of things. IEEE Internet Things J 1(2):112–121
Tran-Dang H, Kim D (2018) An information framework for internet of things services in physical internet. IEEE Access 6:43967–43977
Rimal BP, Choi E, Lumb I (2009) A taxonomy and survey of cloud computing systems. In: 2009 fifth international joint conference on INC, IMS and IDC, pp 44–51
Botta A, de Donato W, Persico V, Pescape A (2016) Integration of cloud computing and internet of things: a survey. Futur Gener Comput Syst 56:684–700
Bonomi F, Milito R, Zhu J, Addepalli S (2012) Fog computing and its role in the internet of things. In: Proceedings of the first edition of the MCC workshop on Mobile cloud computing – MCC 2012. ACM Press
Sarkar S, Chatterjee S, Misra S (2018) Assessment of the suitability of fog computing in the context of internet of things. IEEE Trans Cloud Comput 6(1):46–59
Dastjerdi AV, Buyya R (2016) Fog computing: hel** the internet of things realize its potential. Computer 49(8):112–116
Aazam M, Zeadally S, Harras KA (2018) Offloading in fog computing for IoT: review, enabling technologies, and research opportunities. Futur Gener Comput Syst 87:278–289
Tran-Dang H, Kim D-S (2021) Frato: fog resource based adaptive task offloading for delay-minimizing iot service provisioning. IEEE Trans Parallel Distrib Syst 32(10):2491–2508
Mattson T, Sanders B, Massingill B (2004) Patterns for parallel programming, 1st edn. Addison-Wesley Professional
Jiang Y-S, Chen W-M (2014) Task scheduling in grid computing environments. In: Advances in intelligent systems and computing. Springer International Publishing, pp 23–32
Elgazar A, Harras K, Aazam M, Mtibaa A (2018) Towards intelligent edge storage management: determining and predicting mobile file popularity. In: 2018 6th IEEE international conference on mobile cloud computing, services, and engineering (MobileCloud), pp 23–28
Liu L, Chang Z, Guo X, Mao S, Ristaniemi T (2018) Multiobjective optimization for computation offloading in fog computing. IEEE Internet Things J 5(1):283–294
Yao J, Ansari N (2019) Fog resource provisioning in reliability-aware iot networks. IEEE Internet Things J 6(5):8262–8269
Yousefpour A, Patil A, Ishigaki G, Kim I, Wang X, Cankaya HC, Zhang Q, **e W, Jue JP (2019) Fogplan: a lightweight QoS-aware dynamic fog service provisioning framework. IEEE Internet Things J 6(3):5080–5096
Yang Y, Liu Z, Yang X, Wang K, Hong X, Ge X (2019) POMT: paired offloading of multiple tasks in heterogeneous fog networks. IEEE Internet Things J 6(5):8658–8669
Yousefpour A, Ishigaki G, Gour R, Jue JP (2018) On reducing iot service delay via fog offloading. IEEE Internet Things J 5(2):998–1010
Zhang G, Shen F, Liu Z, Yang Y, Wang K, Zhou M (2019) Femto: fair and energy-minimized task offloading for fog-enabled IoT networks. IEEE Internet Things J 6(3):4388–4400
Liu Z, Yang X, Yang Y, Wang K, Mao G (2019) DATS: dispersive stable task scheduling in heterogeneous fog networks. IEEE Internet Things J 6(2):3423–3436
Mukherjee M, Kumar S, Mavromoustakis CX, Mastorakis G, Matam R, Kumar V, Zhang Q (2020) Latency-driven parallel task data offloading in fog computing networks for industrial applications. IEEE Trans Industr Inform 16(9):6050–6058
Liu Z, Yang Y, Wang K, Shao Z, Zhang J (2020) Post: parallel offloading of splittable tasks in heterogeneous fog networks. IEEE Internet Things J 7(4):3170–3183
Lee G, Saad W, Bennis M (2019) An online optimization framework for distributed fog network formation with minimal latency. IEEE Trans Wirel Commun 18(4):2244–2258
Guo K, Sheng M, Quek TQS, Qiu Z (2020) Task offloading and scheduling in fog ran: a parallel communication and computation perspective. IEEE Wireless Commun Lett 9(2):215–218
Al-khafajiy M, Baker T, Al-Libawy H, Maamar Z, Aloqaily M, Jararweh Y (2019) Improving fog computing performance via fog-2-fog collaboration. Futur Gener Comput Syst 100:266–280
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Tran-Dang, H., Kim, DS. (2023). Dynamic Collaborative Task Offloading in Fog Computing Systems. In: Cooperative and Distributed Intelligent Computation in Fog Computing. Springer, Cham. https://doi.org/10.1007/978-3-031-33920-2_5
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DOI: https://doi.org/10.1007/978-3-031-33920-2_5
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