DATESSO：基于债务感知两级约束推理自适应的服务组合
DATESSO: Self-Adapting Service Composition with Debt-Aware Two Levels Constraint Reasoning
摘要：
基于服务的系统的快速变化的工作负载可以容易地导致组件服务上的利用不足/过度利用，这可以因此影响总体服务质量（QoS），诸如延迟。自适应服务组合纠正了这个问题，但提出了几个挑战：（i）适应的有效性可能会恶化，由于过度乐观的假设的延迟和利用率的限制，在本地和全球的水平;和（ii）每个组合计划带来的好处往往是短期的，往往不是长期的利益-一个自然的先决条件，维持系统。为了解决这些问题，我们提出了一个两个层次的约束推理框架，可持续的自适应服务组合，称为DATESSO。特别地，DATESSO由一个严格的公式组成，该公式将延迟/利用率约束的“严格性”区分为两个级别。为了争取长期利益，DATESSO利用技术债务和时间序列预测的概念来建模组合中组件服务的效用贡献。该方法在DATESSO中嵌入债务感知的两级约束推理算法，以提高自适应服务组合的效率、有效性和可持续性。我们评估DATESSO基于服务的系统与现实世界的WS-DREAM数据集，并将其与其他国家的最先进的方法。实验结果表明，DATESSO在利用率、延迟和运行时间方面优于其他算法，同时可能更具可持续性。
英文摘要：
The rapidly changing workload of service-based systems can easily cause under-/over-utilization on the component services, which can consequently affect the overall Quality of Service (QoS), such as latency. Self-adaptive services composition rectifies this problem, but poses several challenges: (i) the effectiveness of adaptation can deteriorate due to over-optimistic assumptions on the latency and utilization constraints, at both local and global levels; and (ii) the benefits brought by each composition plan is often short term and is not often designed for long-term benefits -- a natural prerequisite for sustaining the system. To tackle these issues, we propose a two levels constraint reasoning framework for sustainable self-adaptive services composition, called DATESSO. In particular, DATESSO consists of a re ned formulation that differentiates the "strictness" for latency/utilization constraints in two levels. To strive for long-term benefits, DATESSO leverages the concept of technical debt and time-series prediction to model the utility contribution of the component services in the composition. The approach embeds a debt-aware two level constraint reasoning algorithm in DATESSO to improve the efficiency, effectiveness and sustainability of self-adaptive service composition. We evaluate DATESSO on a service-based system with real-world WS-DREAM dataset and comparing it with other state-of-the-art approaches. The results demonstrate the superiority of DATESSO over the others on the utilization, latency and running time whilst likely to be more sustainable.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
