The cloud -edge -end architecture satisfies the execution requirements of various workflow applications. However, owing to the diversity of resources, the complex hierarchical structure, and different privacy requirem...
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The cloud -edge -end architecture satisfies the execution requirements of various workflow applications. However, owing to the diversity of resources, the complex hierarchical structure, and different privacy requirements for users, determining how to lease suitable cloud -edge -end resources, schedule multi -privacy -level workflow tasks, and optimize leasing costs is currently one of the key challenges in cloudcomputing. In this paper, we address the scheduling optimization problem of workflow applications containing tasks with multiple privacy levels. To tackle this problem, we propose a heuristic privacy -preserving workflow scheduling algorithm (PWHSA) designed to minimize rental costs which includes time parameter estimation, task sub -deadline division, scheduling sequence generation, task scheduling, and task adjustment, with candidate strategies developed for each component. These candidate strategies in each step undergo statistical calibration across a comprehensive set of workflow instances. We compare the proposed algorithm with modified classical algorithms that target similar problems. The experimental results demonstrate that the PWHSA algorithm outperforms the comparison algorithms while maintaining acceptable execution times.
This paper proposes a multi-critic deep Reinforcement learning framework (MCDRL) and a knowledge-embedded multi-critic deep reinforcement learning(KE-MCDRL) Decision-making method, the method can ensure users' rea...
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ISBN:
(数字)9781665482431
ISBN:
(纸本)9781665482431
This paper proposes a multi-critic deep Reinforcement learning framework (MCDRL) and a knowledge-embedded multi-critic deep reinforcement learning(KE-MCDRL) Decision-making method, the method can ensure users' real-time QoS delay requirements. Compared with implementing the deep reinforcement learning algorithm directly in the communication system, this method can accelerate the convergence and guarantee the initial QoS performance of the system. Simulation results show that the design method can significantly reduce the convergence time compared with traditional deep reinforcement learning, and has nearly optimal decision delay compared with existing decision-making methods, which can actualize real-time decision-making in a time-varying channel environment.
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