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首页> 外文期刊>Computing and informatics >STOCHASTIC MODELING AND PERFORMANCE ANALYSIS OF ENERGY-AWARE CLOUD DATA CENTER BASED ON DYNAMIC SCALABLE STOCHASTIC PETRI NET
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STOCHASTIC MODELING AND PERFORMANCE ANALYSIS OF ENERGY-AWARE CLOUD DATA CENTER BASED ON DYNAMIC SCALABLE STOCHASTIC PETRI NET

机译：基于动态可伸缩随机培养网的能量感知云数据中心的随机建模与性能分析

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摘要

The characteristics of cloud computing, such as large-scale, dynamics, heterogeneity and diversity, present a range of challenges for the study on modeling and performance evaluation on cloud data centers. Performance evaluation not only finds out an appropriate trade-off between cost-benefit and quality of service (QoS) based on service level agreement (SLA), but also investigates the influence of virtualization technology. In this paper, we propose an Energy-Aware Optimization (EAO) algorithm with considering energy consumption, resource diversity and virtual machine migration. In addition, we construct a stochastic model for Energy-Aware Migration-Enabled Cloud (EAMEC) data centers by introducing Dynamic Scalable Stochastic Petri Net (DSSPN). Several performance parameters are defined to evaluate task backlogs, throughput, reject rate, utilization, and energy consumption under different runtime and machines. Finally, we use a tool called SPNP to simulate analytical solutions of these parameters. The analysis results show that DSSPN is applicable to model and evaluate complex cloud systems, and can help to optimize the performance of EAMEC data centers.
机译：云计算的特征，如大规模，动态，异质性和多样性，为云数据中心的建模和性能评估研究提供了一系列挑战。绩效评估不仅在基于服务级别协议（SLA）的成本效益和服务质量（QoS）之间的适当权衡，而且还调查了虚拟化技术的影响。在本文中，我们提出了一种考虑能耗，资源分集和虚拟机迁移的能量感知优化（EAO）算法。此外，我们通过引入动态可扩展的随机Petri网（DSSPN）来构建能量感知迁移的云（EMEC）数据中心的随机模型。定义了几个性能参数，以评估不同运行时和机器下的任务积压，吞吐量，抑制率，利用率和能量消耗。最后，我们使用一个名为SPNP的工具来模拟这些参数的分析解决方案。分析结果表明，DSSPN适用于模型和评估复杂的云系统，并有助于优化EMEC数据中心的性能。

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