Distributed Graph Database Load Balancing Method Based on Deep Reinforcement Learning

This paper focuses on the scheduling problem of workflow tasks that exhibit interdependencies.Unlike indepen-dent batch tasks,workflows typically consist of multiple subtasks with intrinsic correlations and dependencies.It necessitates the distribution of various computational tasks to appropriate computing node resources in accor-dance with task dependencies to ensure the smooth completion of the entire workflow.Workflow scheduling must consider an array of factors,including task dependencies,availability of computational resources,and the schedulability of tasks.Therefore,this paper delves into the distributed graph database workflow task scheduling problem and proposes a workflow scheduling methodology based on deep reinforcement learning(DRL).The method optimizes the maximum completion time(makespan)and response time of workflow tasks,aiming to enhance the responsiveness of workflow tasks while ensuring the minimization of the makespan.The experimental results indicate that the Q-learning Deep Reinforcement Learning(Q-DRL)algorithm markedly diminishes the makespan and refines the average response time within distributed graph database environments.In quantifying makespan,Q-DRL achieves mean reductions of 12.4%and 11.9%over established First-fit and Random scheduling strategies,respectively.Additionally,Q-DRL surpasses the performance of both DRL-Cloud and Improved Deep Q-learning Network(IDQN)algorithms,with improvements standing at 4.4%and 2.6%,respectively.With reference to average response time,the Q-DRL approach exhibits a significantly enhanced performance in the scheduling of workflow tasks,decreasing the average by 2.27%and 4.71%when compared to IDQN and DRL-Cloud,respectively.The Q-DRL algorithm also demonstrates a notable increase in the efficiency of system resource utilization,reducing the average idle rate by 5.02%and 9.30%in comparison to IDQN and DRL-Cloud,respectively.These findings support the assertion that Q-DRL not only upholds a lower average idle rate but also effectively curtails the average response time,thereby substantially improving processing efficiency and optimizing resource utilization within distributed graph database systems.

A W-EAP Algorithm for IEC 61850 Protocol against DoS/Replay Attack

Substation automation system uses IEC 61850 protocol for the data transmission between different equipment manufacturers. However, the IEC 61850 protocol lacks an authentication security mechanism, which will make the communication face four threats: eavesdropping, interception, forgery, and alteration. In order to verify the IEC 61850 protocol communication problems, we used the simulation software to build the main operating equipment in the IEC 61850 network environment of the communication system. We verified IEC 61850 transmission protocol security defects, under DoS attack and Reply attack. In order to enhance security agreement, an improved algorithm was proposed based on identity authentication (W-EAP, Whitelist Based ECC & AES Protocol). Experimental results showed that the method can enhance the ability to resist attacks.