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    郭静, 胡存琛, 包云岗. 面向高密度混部的动态资源分配方法[J]. 计算机研究与发展. DOI: 10.7544/issn1000-1239.202221043
    引用本文: 郭静, 胡存琛, 包云岗. 面向高密度混部的动态资源分配方法[J]. 计算机研究与发展. DOI: 10.7544/issn1000-1239.202221043
    Guo Jing, Hu Cunchen, Bao Yungang. A Dynamic Resource Allocation Method for High-Density Colocation Scenario[J]. Journal of Computer Research and Development. DOI: 10.7544/issn1000-1239.202221043
    Citation: Guo Jing, Hu Cunchen, Bao Yungang. A Dynamic Resource Allocation Method for High-Density Colocation Scenario[J]. Journal of Computer Research and Development. DOI: 10.7544/issn1000-1239.202221043

    面向高密度混部的动态资源分配方法

    A Dynamic Resource Allocation Method for High-Density Colocation Scenario

    • 摘要: 当前的无服务计算提供商采用了一种灵活度低、固定CPU和内存分配比例的耦合式资源分配策略. 随着更多类型应用被部署在无服务计算平台中,该策略已无法满足函数应用的多样化资源需求. 由于函数应用的资源分配粒度小、部署密度高,若将CPU与内存资源的分配进行解耦,需解决资源配置空间爆炸问题. 提出Semi-Share,一个面向无服务计算的解耦式资源管理系统,为函数寻找最优资源配置的同时降低混部函数之间的干扰. 为解决资源配置空间爆炸问题,Semi-Share构建了一个2层资源分配架构,将资源配置空间划分为多个子空间来降低问题复杂度. 第1层是函数分组,基于函数的资源使用特征和历史负载信息进行函数分组,根据分组将资源配置空间划分为多个子空间. 第2层是资源分配,利用贝叶斯优化和加权打分函数来指导模型在资源配置空间中朝正确的方向搜索,降低时间开销. 实验结果显示,Semi-Share相较于被广泛使用的梯度下降搜索法降低了平均85.77%的时间开销,并为函数带来平均42.72%的性能提升;与同样使用贝叶斯优化的耦合式资源分配系统COSE相比,Semi-Share能带来平均32.25%的性能提升.

       

      Abstract: Current serverless computing providers use a coupled resource allocation strategy with low flexibility and a fixed CPU-to-memory allocation ratio. As more types of functions are deployed to the serverless computing platform, the coupled strategy can not satisfy the wide range of resource requirements for these functions. Due to the small granularity of resource allocation and high deployment density in serverless functions, if CPU and memory resource allocation are decoupled, the problem of resource configuration space explosion needs to be solved. In this paper, we present Semi-Share, a decoupled resource manager for serverless functions, which can find the optimal resource configurations for functions while reducing the interference between co-located functions. To solve the resource configuration space explosion problem, Semi-Share builds a two-layer resource allocation architecture, which divides the resource configuration space into multiple subspaces to reduce problem complexity. The first layer is the function cluster, which is based on the resource preference and historical load information of the functions. The resource configuration space is divided according to these clusters. The second layer is resource allocation, which leverages the Bayesian optimization and weighted scoring function to guide Semi-Share to search in the right direction in the configuration space and reduce the time overhead. The experiment results show that Semi-Share greatly reduces the search time of searching the optimal resource configuration by using the two-layer architecture, reduces the average configurations sample by 85.77% compared with the widely used gradient descent search method, and improves the function performance by 42.72% on average. Compared with COSE, a coupled resource allocation system that also uses Bayesian optimization, Semi-Share can improve the function performance by 32.25% on average.

       

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