Towards a Heterogeneous and Elastic Cloud Service System With a Correlation-Based Universal Resource Matching Strategy

Abstract

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.