On Cost-Driven Computation Offloading in the Edge: A New Model Approach

Du, Mingzhe; Wang, Yan; Xu, Cheng‐Zhong

doi:10.1109/ccgrid.2019.00063

Cited by 2 publications

(3 citation statements)

References 21 publications

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“…Importantly, the shared ledger interconnects the MEC servers in a secure manner, and each MEC server has an equal role in managing the MEC network without the need of external authorities according to the blockchain concept. Thus, the use of shared transaction ledger via blockchain eliminates single-point failure risks faced by traditional MEC systems [4], [6].…”

Section: A Blockchain As a Service For Mecmentioning

confidence: 99%

“…1) Basics of DRL: To address the formulated MDP, RLbased approaches can be used to obtain the sequence of actions to maximize the long-term reward in (6). In RL, a learning agent performs trial and error processes via jumping state by state in the defined MDP by taking some actions to obtain certain rewards.…”

Section: B Proposed Drl-based Algorithmmentioning

confidence: 99%

“…This solution potentially reduces computation pressure on devices and provides ultralow latency mobile services to satisfy the ever-increasing computation demands of modern UEs. As in previous works [4], [5], [6], many approaches using MEC have been proposed to improve IoT offloading performances from various aspects, such as computation efficiency, resource allocation and user selection.…”

Section: Introductionmentioning

confidence: 99%

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Blockchain as a Service for Multi-Access Edge Computing: A Deep Reinforcement Learning Approach

Nguyen¹,

Pathirana²,

Ding³

et al. 2020

Preprint

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Recently, blockchain has gained momentum in the academic community thanks to its decentralization, immutability, transparency and security. As an emerging paradigm, Multiaccess Edge Computing (MEC) has been widely used to provide computation and storage resources to mobile user equipments (UE) at the edge of the network for improving the performance of mobile applications. In this paper, we propose a novel blockchain-based MEC architecture where UEs can offload their computation tasks to the MEC servers. In particular, a blockchain network is deployed and hosted on the MEC platform as Blockchain as a Service (BaaS) that supports smart contract-based resource trading and transaction mining services for mobile task offloading. To enhance the performance of the blockchain-empowered MEC system, we propose a joint scheme of computation offloading and blockchain mining. Accordingly, an optimization problem is formulated to maximize edge service revenue and blockchain mining reward while minimizing the service computation latency with respect to constraints of user service demands and hash power resource. We then propose a novel Deep Reinforcement Learning (DRL) approach using a double deep Q-network (DQN) algorithm to solve the proposed problem. Numerical results demonstrate that the proposed scheme outperforms the other baseline methods in terms of better system utility with computational efficiency. Experiment results also verify that the trading contract design is efficient with low operation cost, showing the feasibility of the proposed scheme.

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Section: A Blockchain As a Service For Mecmentioning

confidence: 99%

Section: B Proposed Drl-based Algorithmmentioning

confidence: 99%