Blockchain‐Enabled Task Offloading and Resource Allocation in Fog Computing Networks

Huang, Xiaoge; Deng, Xuesong; Liang, Chengchao; Wu, Fan

doi:10.1155/2021/7518534

Cited by 7 publications

(3 citation statements)

References 21 publications

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“…Moreover, optimal resource allocation to efficiently harness the COIN paradigm, considering its dynamic resources, is a unique problem. Thus, we present a novel approach that considers both metaverse TS on the user side and computational TO on the Convex optimization ✓ ✓ ✓ × ✓ × 2020 [25] Game theory × ✓ ✓ × × × 2020 [30] Hardware, software ✓ ✓ ✓ × × ✓ 2020 [33] RL and game theory × ✓ ✓ × × ✓ 2020 [34] Convex optimization × ✓ ✓ × × × 2021 [40] Game theory…”

Section: Related Workmentioning

confidence: 99%

Dynamic Partial Computation Offloading for the Metaverse in In-Network Computing

Aliyu,

Oh,

et al. 2024

IEEE Access

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The computing in the network (COIN) paradigm is a promising solution that leverages unused network resources to perform tasks to meet computation-demanding applications, such as the metaverse. In this vein, we consider the partial computation offloading problem in the metaverse for multiple subtasks in a COIN environment to minimize energy consumption and delay while dynamically adjusting the offloading policy based on the changing computational resource status. The problem is NP-hard, and we transform it into two subproblems: the task-splitting problem (TSP) on the user side and the task-offloading problem (TOP) on the COIN side. We model the TSP as an ordinal potential game and propose a decentralized algorithm to obtain its Nash equilibrium (NE). Then, we model the TOP as a Markov decision process and propose the double deep Q-network (DDQN) to solve for the optimal offloading policy. Unlike the conventional DDQN algorithm, where intelligent agents sample offloading decisions randomly within a certain probability, the COIN agent explores the NE of the TSP and the deep neural network. Finally, the simulation results reveal that the proposed model approach allows the COIN agent to update its policies and make more informed decisions, leading to improved performance over time compared to the traditional baseline.

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Section: Related Workmentioning

confidence: 99%

Dynamic Partial Computation Offloading for the Metaverse in In-Network Computing

Aliyu,

Oh,

et al. 2024

IEEE Access

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“…To make efficient use of cloud computing and MEC resources, Wu et al [26] adopt a dynamic algorithm to decide the optimal computing place, while the optimization is applied to trade-off limited computing consumption against high latency. Huang et al [27] propose a task offloading and resource allocation framework based on fog computing networks (FCNs) to ensure fairness of transactions. However, the two methods pay more attention to optimizing the energy consumption of computing servers and ignore the impact of blockchain on system latency.…”

Section: Blockchain-empowered Iiot With Mecmentioning

confidence: 99%

Optimization Scheme of Trusted Task Offloading in IIoT Scenario Based爋n燚QN

Wang¹,

Lu²,

Sun³

et al. 2023

Computers, Materials &Amp; Continua

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With the development of the Industrial Internet of Things (IIoT), end devices (EDs) are equipped with more functions to capture information. Therefore, a large amount of data is generated at the edge of the network and needs to be processed. However, no matter whether these computing tasks are offloaded to traditional central clusters or mobile edge computing (MEC) devices, the data is short of security and may be changed during transmission. In view of this challenge, this paper proposes a trusted task offloading optimization scheme that can offer low latency and high bandwidth services for IIoT with data security. Blockchain technology is adopted to ensure data consistency. Meanwhile, to reduce the impact of low throughput of blockchain on task offloading performance, we design the processes of consensus and offloading as a Markov decision process (MDP) by defining states, actions, and rewards. Deep reinforcement learning (DRL) algorithm is introduced to dynamically select offloading actions. To accelerate the optimization, we design a novel reward function for the DRL algorithm according to the scale and computational complexity of the task. Experiments demonstrate that compared with methods without optimization, our mechanism performs better when it comes to the number of task offloading and throughput of blockchain.

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“…Therefore, in literature blockchain is utilized several times in integration with other computation methods such as game theory, graph‐based approaches, and optimization methods 42 . Huang et al 37 proposed a game theory‐based task offloading and resource allocation scheme using an amalgamation of blockchain and fog computing. An optimization problem is designed to minimize the overall energy consumption and delay.…”

Section: Related Workmentioning

confidence: 99%

Blockchain enabled trusted task offloading scheme for fog computing: A deep reinforcement learning approach

Jain

Kumar

2022

Trans Emerging Tel Tech

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With the recent advancements in the Internet of Things, cloud computing has emerged as an important industrial technology that assists in various data analysis operations. However, the remote locality of cloud servers and scalability issues of cloud computing make it unsuitable for real‐time computing‐intensive applications. Fog computing strives to support cloud computing in meeting scalability demands by providing location‐sensitive services closer to end devices. With decentralized heterogeneous resource capabilities, fog architecture can handle several computation‐intensive and delay‐sensitive user requests. Although deploying service providers in an untrustworthy environment makes it challenging to assess the trustworthy acquired services. Conspicuously, in this article, we present a trusted task offloading and resource allocation using blockchain technology. To start with, we analyze direct and indirect trust with a subjective logical aggregation approach using a distributed trust assessment approach. Additionally, we examined the various quality of service parameters and constructed a smart contract that utilizes the state‐of‐the‐art deep reinforcement learning algorithm, namely Deep Deterministic Policy Gradient, to maximize fog revenue while serving as many user requests as possible. The entire process from task generation to results calculation is assisted by blockchain and offloading task transactions are stored in the secure, immutable, and tamper‐resistant ledger. To assess the effectiveness of our proposed scheme, we compared the simulation results with other baseline schemes over different performance metrics in terms of reward, service latency, energy consumption, task drop ratio, and transaction success rate. The results suggest that enabling trust computation improves transaction success by 21%.

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Blockchain‐Enabled Task Offloading and Resource Allocation in Fog Computing Networks

Cited by 7 publications

References 21 publications

Dynamic Partial Computation Offloading for the Metaverse in In-Network Computing

Dynamic Partial Computation Offloading for the Metaverse in In-Network Computing

Optimization Scheme of Trusted Task Offloading in IIoT Scenario Based爋n燚QN

Blockchain enabled trusted task offloading scheme for fog computing: A deep reinforcement learning approach

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