摘要
在容器技术和微服务框架的普及背景下,无服务器计算为开发者提供了一种无需关注服务器操作以及硬件资源管理的云计算范式.与此同时,无服务器计算通过弹性扩缩容实时地适应动态负载变化,能够有效降低请求响应延时并且减少服务成本,满足了客户对于云服务成本按需付费的需求.然而,无服务器计算中面临着弹性扩缩容需求导致的冷启动延迟问题.提前预热函数实例能够有效地降低冷启动发生频率和延时.然而,在云环境中流量突发问题极大地增加了预测预热函数实例数的难度.针对上述挑战,提出了一种基于概率分布的弹性伸缩算法(probability distribution based auto-scaling algorithm,PDBAA),利用监控指标历史数据预测未来请求的概率分布,以最小化请求响应延时为目的计算预热函数实例的最佳数量,并且PDBAA能够有效地结合深度学习技术的强大预测功能进一步提升性能.在Knative框架中,通过NASA和WSAL数据集对算法进行了验证,仿真实验表明,相比于Knative弹性伸缩算法以及其他预测算法,所提出的算法弹性性能提升了31%以上,平均响应时间降低了16%以上,能够更好地解决流量突发问题,有效地降低了无服务器计算请求的响应延时.
Serverless computing provides developers a cloud computing paradigm,which does not require that developers focus on the server operation and hardware resource management in the context of the popularity of container technology and micro-service framework.At the same time,serverless computing can adapt to dynamic load changes in real time through elastic expansion and contraction,which can effectively reduce the request response delay and the service cost,and meet the customer’s demand for pay-as-you-go cloud service expense.However,serverless computing faces the issue of cold start delay caused by the demand for elastic expansion and contraction.Creating the instances of warm-up function in advance can reduce the frequency and delay of cold start effectively.Nevertheless,the traffic burst problem in the cloud environment greatly increases the difficulty of predicting the number of warm-up function instances.To solve the above-mentioned challenges,a probability distribution based autoscaling algorithm(PDBAA)is proposed.By using the historical data of monitoring indicators to predict the probability distribution of future requests,the optimal number of warm-up function instances is calculated for minimizing the request response delay.PDBAA can effectively combine the powerful prediction capability of deep learning technology to further improve performance.Under the Knative framework,the performance of PDBAA is verified by NASA and WSAL datasets.The simulation results show that,compared with the Knative auto-scaling algorithm and other prediction algorithms,PDBAA improves the elastic performance by over 31%,and reduces the average response time by over 16%,which can better solve the traffic burst problem,and effectively reduce the response delay of serverless computing requests.
作者
李威
李光辉
赵庆林
代成龙
陈思
Li Wei;Li Guanghui;Zhao Qinglin;Dai Chenglong;Chen Si(School of Artificial Intelligence and Computer Science,Jiangnan University,Wuxi,Jiangsu 214122;School of Computer Science and Engineering,Macao University of Science and Technology,Macao 999078,China)
出处
《计算机研究与发展》
北大核心
2025年第2期503-516,共14页
Journal of Computer Research and Development
基金
国家自然科学基金项目(62072216)。
