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基于深度学习的容器化Flink上下游负载均衡策略研究
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作者 艾力卡木·再比布拉 甄妞 +1 位作者 黄山 段晓东 《大连民族大学学报》 2023年第1期47-52,共6页
容器化部署Flink时,存在上下游算子的容器内存分配不均衡问题。提出基于深度学习的容器化Flink上下游负载均衡框架,使用CEEMDAN分解方法和BiLSTM相结合的预测方法预测Flink下游容器所需内存,并依据预测结果调整容器内存分配。实验证明:... 容器化部署Flink时,存在上下游算子的容器内存分配不均衡问题。提出基于深度学习的容器化Flink上下游负载均衡框架,使用CEEMDAN分解方法和BiLSTM相结合的预测方法预测Flink下游容器所需内存,并依据预测结果调整容器内存分配。实验证明:提出的上下游负载均衡策略可有效减少上游容器的等待时间,缓解下游容器的资源,计算效率提高约20%。 展开更多
关键词 Flink 容器负载预测 容器伸缩 深度学习
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Study on the Weld-Bonding Process Optimization and Mechanical Performance of Aluminum Alloy Joints 被引量:2
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作者 Mingfeng Li Yanjun Wang +1 位作者 zhen niu Shanglu Yang 《Automotive Innovation》 CSCD 2020年第3期221-230,共10页
The 5754 aluminum alloy has been widely used in the automotive industry to reduce the weight of vehicles.The weld-bonding(WB)process comprising resistance spot welding and adhesive bonding processes effectively improv... The 5754 aluminum alloy has been widely used in the automotive industry to reduce the weight of vehicles.The weld-bonding(WB)process comprising resistance spot welding and adhesive bonding processes effectively improves the mechanical properties of joints.However,it is still a great challenge in the WB process to obtain high-quality and defect-free nuggets of aluminum alloys.In this study,the parameters of the WB process are optimized and the mechanism of generation of defects during WB is analyzed.The results show that the welding parameters have a significant effect on the nugget sizes,among which the welding current plays the most important role.The residual adhesive can easily cause defects during welding,e.g.,expulsion and porosity in the nugget.This can be effectively avoided by optimizing the welding parameters.In addition,the gas in the joints is effectively reduced by adding an appropriate preheating pulse prior to welding,thus lowering the damage degree of the adhesive layer.As a result,welded joints with better weld nugget quality and more stable mechanical properties are obtained. 展开更多
关键词 Aluminum alloy Weld bonding Process optimization Mechanical performance
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