In elastic cloud service systems, it is a challenge to evaluate and match the fluctuating resource demand of workloads. Existing studies typically monitor workload characteristics and build models that map these characteristics to actual demand. However, workload characteristics are multidimensional, and the impact of each dimension on resource demand differs, so it requires differentiated treatment when building models. This paper proposes a Correlation-Based Universal Resource Matching (CBURM) strategy to realize a Heterogeneous and Elastic Cloud Service System (HECSS). CBURM consists of a Correlation-based resource Demand Evaluation (CDE) method and a Universal Resource Measurement (URM) scheme. Specifically, CDE discriminates the relevance of each dimension in workload characteristics, based on the correlations between workload characteristics and the demand. Then, it generates resource demand decisions dimension by dimension, from the most relevant to the least relevant dimensions. After that, it generates a complete decision tree model to evaluate subsequent workload demand for heterogeneous resources. Finally, URM optimizes the resource allocation to achieve a low-overhead resource matching. Experimental results show that, URM reduces the total comprehensive operation cost by 82%+, compared to a normal resource allocation scheme. Additionally, CDE outperforms two state-of-the-art methods (LTP and 2SP), with its performance closer to the ideal baseline. Specifically, CDE achieves a 40.275% overall resource saving rate, which is 38.62% higher than LTP and 8.46% higher than 2SP. Besides, CDE achieves a 92.43% average service quality satisfaction ratio, higher than the 82.9% and 88.83% achieved respectively by LTP and 2SP.

中文翻译：

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迈向具有基于关联的通用资源匹配策略的异构弹性云服务系统


在弹性云服务系统中，评估和匹配工作负载波动的资源需求是一项挑战。现有研究通常会监控工作负载特征，并构建将这些特征映射到实际需求的模型。但是，工作负载特征是多维的，每个维度对资源需求的影响不同，因此在构建模型时需要差异化处理。该文提出了一种基于相关性的通用资源匹配 （CBURM） 策略来实现异构弹性云服务系统 （HECSS）。CBURM 由基于相关性的资源需求评估 （CDE） 方法和通用资源测量 （URM） 方案组成。具体来说，CDE 根据工作负载特征与需求之间的相关性来区分工作负载特征中每个维度的相关性。然后，它会逐个维度生成资源需求决策，从最相关的维度到最不相关的维度。之后，它会生成一个完整的决策树模型，以评估异构资源的后续工作负载需求。最后，URM 优化资源分配以实现低开销资源匹配。实验结果表明，与常规资源分配方案相比，URM 将总综合运营成本降低了 82%+。此外，CDE 优于两种最先进的方法（LTP 和 2SP），其性能更接近理想基线。具体来说，CDE 实现了 40.275% 的整体资源节约率，比 LTP 高 38.62%，比 2SP 高 8.46%。此外，CDE 的平均服务质量满意度为 92.43%，高于 LTP 和 2SP 分别达到的 82.9% 和 88.83%。