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区块链赋能物联网中联合资源分配与控制的智能计算迁移研究
引用本文:陈思光,王倩,张海君,王堃.区块链赋能物联网中联合资源分配与控制的智能计算迁移研究[J].计算机学报,2022,45(3):472-484.
作者姓名:陈思光  王倩  张海君  王堃
作者单位:南京邮电大学江苏省宽带无线通信和物联网重点实验室 南京 210003;北京科技大学通信工程系 北京 100083;加州大学洛杉矶分校电子与计算机工程系 洛杉矶 CA90095 美国
基金项目:国家自然科学基金(61971235,61771258);江苏省“六大人才高峰”高层次人才项目(XYDXXJS-044);江苏省“333高层次人才培养工程”;南京邮电大学‘1311’人才计划;中国博士后科学基金(面上一等)(2018M630590);江苏省博士后科研资助计划(2021K501C);赛尔网络下一代互联网技术创新项目(NGII20190702)资助
摘    要:大数据场景下,远程云服务器通常被部署用于数据处理与价值挖掘,但在面对时延敏感型或需要动态频繁交互的业务时,该种处理模式显得力不从心.作为对云计算模式的补充,雾计算因其可有效降低任务处理时延、能耗与带宽消耗而备受关注;同时,面向雾计算的计算迁移机制因其能有效缓解节点的处理负担并改善用户体验而成为领域研究焦点.在雾计算模式...

关 键 词:计算迁移  雾计算  区块链  深度强化学习  资源分配

Resource Allocation and Control Co-aware Smart Computation Offloading for Blockchain-Enabled IoT
CHEN Si-Guang,WANG Qian,ZHANG Hai-Jun,WANG Kun.Resource Allocation and Control Co-aware Smart Computation Offloading for Blockchain-Enabled IoT[J].Chinese Journal of Computers,2022,45(3):472-484.
Authors:CHEN Si-Guang  WANG Qian  ZHANG Hai-Jun  WANG Kun
Affiliation:(Jiangsu Key Lab of Broadband Wireless Communication and Internet of Things,Nanjing University of Posts and Telecommunications,Nanjing 210003;Department of Communication Engineering,University of Science and Technology Beijing,Beijing 100083;Department of Electrical and Computer Engineering,University of California Los Angeles,Los Angeles CA90095 USA)
Abstract:Under the scenario of big data,the remote cloud server is usually deployed for data processing and value mining,but in the face of delay-sensitive or dynamic and frequent update applications,this processing paradigm appears to be inadequate from reality.As a complement to cloud computing paradigm,fog computing has attracted great attention for which can effectively reduce task processing delay,energy and bandwidth consumptions.At the same time,fog computing based computation offloading mechanism has become a research focus,because it can effectively alleviate the processing burden of nodes and improve the experience of users.Under the fog computing paradigm,in order to better meet the requirements of delay and energy consumption for computation-intensive tasks,based on the blockchain-enabled Internet of Things(IoT)scenario,this paper proposes a resource allocation and control co-aware smart computation offloading scheme.Specifically,an optimization problem is formulated to minimize the total cost of all tasks under the constraints of delay,energy consumption,and communication and computation resources.Based on the comprehensive consideration,the component of the total cost includes the delay,energy consumption and resource mining costs;it can achieve the minimization of the total cost by jointly optimizing the communication resource,computation resource and offloading decision.In order to inspire the active participation of terminals and the fog node in the computation offloading process,and more close to the needs of the real scenario,an incentive mechanism is designed in this paper.That is,to complete the offloading of tasks,the terminal mines(rents)computation resource from the fog node as a miner and the fog node charges a certain fee according to the needed resource of terminal.For the terminals that successfully obtain the resources to complete the tasks efficiently,the system will allocate the corresponding rewards according to the occupation ratios of gained computation resource,which ensures the fairness allocation of the rewards for the successful miners.This mechanism enables that the fog node and terminals both can win benefits in the computation offloading process,which promotes their collaboration.Meanwhile,this blockchain-based incentive mechanism guarantees the security of the transaction process.For the sake of solving the above formulated optimization problem(i.e.,a mixed integer nonlinear programming problem),we propose a communication,computation and control co-aware smart computation offloading algorithm(3CC-SCO).By integrating the concept of deep deterministic policy gradient(DDPG)algorithm,our algorithm designs an inverting gradient update based double actor-critic neural networks structure to improve the stable and convergence rate in the training process.At the same time,it is more suitable for solving the mixed integer optimization problem by adopting probabilistic discrete operation of continuous action output.Finally,the simulation results demonstrate that the proposed scheme can converge to the optimal value quickly,and the total cost of the proposed scheme is the lowest compared with other three benchmark schemes,for example,as compared with the best-performing scheme,i.e.,deep Q-learning network(DQN)based computation offloading scheme,the total cost can be reduced by an average of 15.2%.
Keywords:computation offloading  fog computing  blockchain  deep reinforcement learning  resource allocation
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