点击次数：

影响因子：7.5

所属单位：福建理工大学计算机科学与数学学院

发表刊物：Expert Systems with Applications

摘要：Cloud computing systems depend on elasticity to adapt resource allocation to fluctuating workload. Although traditional metrics effectively measure resource scaling, they fail to capture how these adjustments impact user-perceived service quality, which is a critical gap for providers and consumers alike. To bridge this gap, we introduce a novel performance metric that uses the probability distribution of task response times to complement the existing elasticity measures. This metric defines service quality as the likelihood that response times meet preset service-level objectives (SLOs) within a given timeframe. We developed a framework linking resource allocation, workload patterns, and this metric to optimize performance in various scenarios. We propose a decision-making algorithm to improve service quality without sacrificing cost efficiency. The experiments show that integrating this user-focused metric improves resource utilization by 23 % and reduces SLO violations by 31 % in the tested e-commerce workloads.

论文类型：期刊论文

学科门类：工学

卷号：296

期号：2026-01

页面范围：129081

是否译文：否

发表时间：2025-07-22

收录刊物：SCI

发布期刊链接：https://www-sciencedirect-com-443.webvpn.fjut.edu.cn/science/article/abs/pii/S0957417425026983

第一作者：Liwen Chen

合写作者：Chunmao Jiang,Qiaoping Zhong