Container Group Scheduling Optimization Strategy Based on DDQN in Edge Scenarios
The industrial Internet is populated with a large number of on/offline container services deployed on edge servers.On the on hand,these container services bear the demand for low latency and high response,and on the other hand,they have intricate invocation re-lationships.The usual scheduling strategies for edge clusters do not take into account the dependencies between container services,leading to dependent container services possibly being dispersed across different edge nodes during scheduling,thereby generating a large number of cross-node calls and causing additional resource loss.We propose an optimization strategy for container group scheduling in edge scenarios for containers with dependencies.Firstly,the CDSC(Container Dependency Spectral Clustering)is used to divide dependent containers into one or more container groups,maximizing the dependency strength within groups and minimizing it between groups,to reduce the frequency of cross-node calls.Then,by introducing the Double Deep Q-Network model(Double DQN),the container group is used as the basic scheduling unit,with container dependency overhead,cluster and intra-node load as optimization targets.The strategy adaptively learns and optimizes scheduling strategies according to the actual situation of edge nodes,enabling it to cope with complex and changing edge cluster situations.Experimental results show that compared to traditional heuristic algorithms and deep reinforcement learning algorithms,the proposed algorithm has significant advantages in terms of container service response time,cluster and node load.
国家科技期刊平台
NSTL
万方数据
