Modelling exogenous variability in cloud deployments
Modelling exogenous variability in cloud deployments
复制标题
对云部署中的外生变化进行建模
DOI：
10.1145/2479942.2479951
复制
发表时间：
2013
期刊：
影响因子：
--
通讯作者：
Casale G
中科院分区：
文献类型：
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作者：
Casale G
描述云应用程序使用的资源中的外生变化会导致难以解决的随机性能模型。在本文中，我们描述了混合算法，一种新的近似嵌入网络模型沉浸在一个随机的环境。随机环境是对独立于系统状态而演变的时变操作条件的基于马尔可夫链的描述，因此它们是云部署中外源性变化的自然描述符。该算法采用的原则，解决一个单独的瞬态分析子问题的每个状态的随机环境。每个子问题，然后近似的常微分方程系统，制定根据流体极限定理，使该方法可扩展和计算成本低廉。对数百个模型的验证研究表明，与模拟相比，混合可以节省多达两个数量级的计算时间，从而能够有效地探索决策空间，这在设计时特别有用。
Describing exogenous variability in the resources used by a cloud application leads to stochastic performance models that are difficult to solve. In this paper, we describe the blending algorithm, a novel approximation for queueing network models immersed in a random environment. Random environments are Markov chain-based descriptions of timevarying operational conditions that evolve independently of the system state, therefore they are natural descriptors for exogenous variability in a cloud deployment. The algorithm adopts the principle of solving a separate transient-analysis subproblem for each state of the random environment. Each subproblem is then approximated by a system of ordinary differential equations formulated according to a fluid limit theorem, making the approach scalable and computationally inexpensive. A validation study on several hundred models shows that blending can save up to two orders of magnitude of computational time compared to simulation, enabling efficient exploration of a decision space, which is useful in particular at design-time.
两阶段随机环境中排队的流体分析
DOI：
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发表时间：
2011
期刊：
2011 Eighth International Conference on Quantitative Evaluation of SysTems
影响因子：
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作者：
G. Casale;M. Tribastone
通讯作者：
M. Tribastone
OFBench：云资源管理研究的企业应用程序基准
DOI：
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发表时间：
2012
期刊：
2012 14th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing
影响因子：
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作者：
J. Moschetta;G. Casale
通讯作者：
G. Casale