BASE：通过堆叠集成实现突发适应性自动扩展以确保服务级别目标和成本效率
BASE: Burst-Adaptive Autoscaling via Stacked Ensembles for SLO Assurance and Cost Efficiency
AI总结：
BASE通过堆叠集成机器学习模型，实现突发适应性自动扩展，有效减少SLO违规并降低成本。
摘要：
自动扩展是一种无需人工干预的技术，用于自动为应用程序分配资源，以确保运行时的服务质量（QoS）并降低成本。然而，面向用户的应用程序服务于动态工作负载，这些工作负载通常表现出可变性和突发性，给自动扩展在维护服务级别目标（SLO）内的QoS带来了挑战。保守的策略可能造成过度配置，而激进的策略可能导致SLO违规，这使得设计有效的自动扩展更加困难。本文介绍了一个名为BASE的突发适应性自动扩展框架，该框架利用机器学习模型的堆叠集成来减轻SLO违规并减少容器化服务和应用程序在时间变化的工作负载下的成本。BASE包含一种基于预测的突发检测机制，该机制能够区分可预测的工作负载峰值和实际不确定的突发。当检测到突发时，BASE会适当高估这些突发并相应地分配资源，以应对资源需求的快速增长。另一方面，BASE利用强化学习来纠正资源估计的潜在不准确，从而在非突发期间实现更精确的资源分配。在十个真实世界工作负载上的实验展示了BASE的有效性，实现了显著减少SLO违规，同时相比其他著名方法具有更低的资源成本。
英文摘要：
Autoscaling is a technology that automatically scales resources for applications without human intervention to ensure runtime Quality of Service (QoS) while reducing costs. However, user-facing cloud applications serve dynamic workloads that often exhibit variability and contain bursts, posing challenges to autoscaling in maintaining QoS within Service-Level Objectives (SLOs). Conservative strategies risk over-provisioning, while aggressive ones may cause SLO violations, making it more challenging to design effective autoscaling. This paper introduces BASE, a burst-adaptive autoscaling framework that leverages a stacked ensemble of machine learning models to mitigate SLO violations and reduce costs for containerized services and applications operating under time-varying workloads. BASE incorporates a novel prediction-based burst detection mechanism that distinguishes between predictable workload spikes and actual uncertain bursts. When bursts are detected, BASE appropriately overestimates them and allocates resources accordingly to address the rapid growth in resource demand. On the other hand, BASE employs reinforcement learning to rectify potential inaccuracies in resource estimation, enabling more precise resource allocation during non-burst periods. Experiments across ten real-world workloads demonstrate BASE's effectiveness, achieving a significant reduction in SLO violations with lower resource costs compared to other prominent methods.
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。
