Multiarmed-Bandit-Based Decentralized Computation Offloading in Fog-Enabled IoT

Misra, Sudip; Rachuri, Sri Pramodh; Deb, Pallav Kumar; Mukherjee, Anandarup

doi:10.1109/jiot.2020.3048365

Cited by 30 publications

(7 citation statements)

References 21 publications

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“…In this context, multi-armed bandit (MAB)-based solutions are developed to address these kinds of shortcomings [87]. In particular, the upper-confidence bound (UCB) mechanism is integrated for obtaining the guaranteed performance and low complexity [88], [89]. Accordingly, the work [88] introduced BLOT (bandit learning-based offloading of tasks) algorithm to offloading non-splitable tasks.…”

Section: Task Offloading and Redistributionmentioning

confidence: 99%

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Reinforcement learning based resource management for fog computing environment: Literature review, challenges, and open issues

et al. 2022

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Section: Task Offloading and Redistributionmentioning

confidence: 99%

“…Accordingly, the work [88] introduced BLOT (bandit learning-based offloading of tasks) algorithm to offloading non-splitable tasks. Meanwhile, D2CIT-a decentralized computation offloading is proposed in [89] to offloading the subtasks, which are constitutes of a high-complexity tasks.…”

Section: Task Offloading and Redistributionmentioning

confidence: 99%

Reinforcement learning based resource management for fog computing environment: Literature review, challenges, and open issues

et al. 2022

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“…AI and ML tools provide efficient techniques to analyze and predict the statues of system accurately. Reinforcement learning is a such kind of techniques [82], [83], which can help to build PLs efficiently through online learning mechanism (i.e., exploitation and exploration). Thus, using these in the context of computational offloading enable the system to make dynamic and efficient offloading decisions.…”

Section: F Application Of Ai and Ml-based Techniquesmentioning

confidence: 99%

A Survey on Matching Theory for Distributed Computation Offloading in IoT-Fog-Cloud Systems: Perspectives and Open Issues

Tran-Dang

Kim

2022

IEEE Access

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Fog computing has been widely integrated in the IoT-based systems, creating IoT-Fog-Cloud (IFC) systems to improve the system performances and satisfy the quality of services (QoS) and quality of experience (QoE) requirements for the end users (EUs). This improvement is enabled by computational offloading schemes, which perform the task computation nearby the task generation sources (i.e., IoT devices, EUs) on behalf of remote cloud servers. To realize the benefits of offloading techniques, however, there is a need to incorporate efficient resource allocation frameworks, which can deal effectively with intrinsic properties of computing environment in the IFC systems such as resource heterogeneity of computing devices, various requirements of computation tasks, high task request rates, and so on. While the centralize optimization and non-cooperative game theory based solutions are applicable in a certain number of application scenarios, they fail to be efficient in many of cases, where the global information and control might be unavailable or cost-intensive to achieve it in the large-scale systems. The need of distributed computational offloading algorithms with low computation complexity has motivated a surge of solutions using matching theory. In the present review, we first describe the fundamental concept of this emerging tool enabling the distributed implementation in the computing environment. Then the key solution concepts and algorithmic implementations proposed in the framework of literature are highlighted and discussed. Given the powerful tool of matching theory, its full capability is still unexplored and unexploited in the literature. We thereby discover and discuss existing challenges and corresponding solutions that the matching theory can be applied to resolve them. Furthermore, new problems and open issues for application scenarios of modern IFC systems are also investigated thoroughly. INDEX TERMSIoT-Fog-Cloud systems, Matching theory, Distributed algorithm, Computational offloading. I. INTRODUCTION Practically, the Internet of Things (IoT) has become an integral element for realizing smart practical systems such as smart cities [1], smart grids [2], smart factories [3], smart logistics, and supply chain [4], [5]. The fundamental aspect of IoT-based systems is to connect all devices through the Internet protocol to exchange high volume data and pro-cess them to create smart services and applications [6], [7]. Owing to limited computation resources, network, storage, and energy, IoT devices are inadequate for executing all computational tasks, especially tasks with huge volumes and complex data structures. Cloud computing is an essential solution to this problem because it provides powerful resources to fulfill tasks efficiently [8], [9]. However, cloud

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“…Resource allocation and task scheduling for edge-fog-cloud architectures have been a subject of thorough research in recent years. S. Misra et al in [5] propose an approach where highlevel tasks are divided into smaller independent subtasks and distributed among the Fog nodes using a greedy approach. In [6], [7] authors propose greedy and game theoretic approaches, respectively, for deciding whether to offload the tasks to nearby fog nodes or cloud services by considering factors such as delays, energy consumption, etc.…”

Section: Related Workmentioning

confidence: 99%

Priority-Queue based Dynamic Scaling for Efficient Resource Allocation in Fog Computing

Bhushan¹

2021

Preprint

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In this emerging world of connected devices, the need for more computing devices with a focus on delay-sensitive application is critical. In this paper, we propose a priority-queue based Fog computing architecture combined with dynamic scalability of fog devices, which not only reduces the delay experienced by delay-sensitive tasks by categorizing the delay-sensitive and delay-insensitive tasks, but also dynamically allocates the fog devices within the network depending upon the computation load for reducing the power consumption. The results show that the proposed algorithm is able to achieve a significant lower delay for both delay-sensitive and -insensitive tasks when compared with other related schemes with a 14.5% lower power consumption.

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Multiarmed-Bandit-Based Decentralized Computation Offloading in Fog-Enabled IoT

Cited by 30 publications

References 21 publications

Reinforcement learning based resource management for fog computing environment: Literature review, challenges, and open issues

Reinforcement learning based resource management for fog computing environment: Literature review, challenges, and open issues

A Survey on Matching Theory for Distributed Computation Offloading in IoT-Fog-Cloud Systems: Perspectives and Open Issues

Priority-Queue based Dynamic Scaling for Efficient Resource Allocation in Fog Computing

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