云应用组件的受限优化分组

Marta Różańska, Geir Horn
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引用次数: 0

摘要

云应用程序由一组组件构建而成,通常以容器的形式部署,这些组件可以单独部署在不同的虚拟机(VM)上,也可以组合在一组较小的虚拟机上。此外,应用程序所有者可能对组件的共用位置有限制。找到部署应用程序的最佳方法意味着要找到最佳的组件组和最佳的虚拟机,而由于可能的选项数量众多,这并不是一件容易的事。这个问题可以映射到已知的组合问题上,如 binpacking 和 knapsack 公式。然而,这些方法通常假定资源是均质的,并且没有纳入抑制约束。本文的主要贡献首先是将分组问题新颖地表述为受约束联盟结构生成(CSG)问题,包括指定满足特征函数博弈(CFG)标准的值函数。CSG 问题的目的是确定稳定且互不相交的玩家群体,通过合作优化博弈的共同结果,而 CFG 是 CSG 的常见表示形式,其中每个群体都被赋予一个值,博弈的值是各群体贡献的总和。其次,对整数分区(IP)CSG 算法进行了修改和扩展,以处理约束条件。建议的方法与扩展的 IP 算法以及建立最佳分组的新型穷举搜索算法进行了比较评估。评估结果表明,与 CSG 最先进的算法相比,我们的方法与修改后的算法所评估的组合平均要少得多。由于修改后的 IP 算法可以优化解决可实现规模的受限分组问题,因此建议的方法有望用于优化受限云应用管理。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Constrained optimal grouping of cloud application components
Cloud applications are built from a set of components often deployed as containers, which can be deployed individually on separate Virtual Machines (VMs) or grouped on a smaller set of VMs. Additionally, the application owner may have inhibition constraints regarding the co-location of components. Finding the best way to deploy an application means finding the best groups of components and the best VMs, and it is not trivial because of the complexity coming from the number of possible options. The problem can be mapped onto may known combinatorial problems as binpacking and knapsack formulations. However, these approaches often assume homogeneus resources and fail to incorporate the inhibition constraints. The main contribution of this paper are firstly a novel formulation of the grouping problem as constrained Coalition Structure Generation (CSG) problem, including the specification of the value function which fulfills the criteria of a Characteristic Function Game (CFG). The CSG problem aims to determine stable and disjoint groups of players collaborating to optimize the joint outcome of the game, and a CFG is a common representation of a CSG, where each group is assigned a value and where the value of the game is the sum of the groups’ contributions. Secondly, the Integer-Partition (IP) CSG algorithm has been modified and extended to handle constraints. The proposed approach is evaluated with the extended IP algorithm, and a novel exhaustive search algorithm establishing the optimum grouping for comparison. The evaluation shows that our approach with the modified algorithm evaluates on average significantly less combinations than the CSG state-of-the-art algorithm. The proposed approach is promising for optimized constrained Cloud application management as the modified IP algorithm can optimally solve constrained grouping problems of attainable sizes.
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