Kubernetes上工作流容器化的自适应资源分配
Adaptive Resource Allocation for Workflow Containerization on Kubernetes
AI总结：
针对Kubernetes工作流引擎资源分配缺乏敏捷性和可预测性导致效率低与浪费的问题，提出自适应资源分配方案ARAS，通过资源扩展策略应对高并发场景，实验证明其显著缩短工作流执行时间并提升资源使用率。
摘要：
在云原生时代，基于Kubernetes的工作流引擎通过Kubernetes的固有能力实现了工作流容器化执行。然而，在遇到持续的工作流请求和突发的资源请求激增时，该引擎仅限于根据当前工作流负载信息进行资源分配，缺乏资源分配的敏捷性和可预测性，导致资源过度配置和配置不足。这种机制严重阻碍了工作流执行效率，并导致大量资源浪费。为了克服这些缺陷，我们为基于Kubernetes的工作流引擎提出了一种名为ARAS的自适应资源分配方案。考虑到当前任务pod生命周期内未来潜在的工作流任务请求，ARAS使用资源扩展策略来分配资源，以应对高并发工作流场景。ARAS提供资源发现、资源评估和分配功能，并作为我们定制工作流引擎（KubeAdaptor）的关键组件。通过将ARAS集成到KubeAdaptor中进行工作流容器化执行，我们展示了KubeAdaptor的实际能力和ARAS的优势。与基线算法相比，在三种不同工作流到达模式下的实验评估表明，ARAS在所有工作流的平均总持续时间上节省了9.8%至40.92%的时间，在单个工作流的平均持续时间上节省了26.4%至79.86%的时间，并使CPU和内存资源使用率提高了1%至16%。
英文摘要：
In a cloud-native era, the Kubernetes-based workflow engine enables workflow containerized execution through the inherent abilities of Kubernetes. However, when encountering continuous workflow requests and unexpected resource request spikes, the engine is limited to the current workflow load information for resource allocation, which lacks the agility and predictability of resource allocation, resulting in over and under-provisioning resources. This mechanism seriously hinders workflow execution efficiency and leads to high resource waste. To overcome these drawbacks, we propose an adaptive resource allocation scheme named ARAS for the Kubernetes-based workflow engines. Considering potential future workflow task requests within the current task pod's lifecycle, the ARAS uses a resource scaling strategy to allocate resources in response to high-concurrency workflow scenarios. The ARAS offers resource discovery, resource evaluation, and allocation functionalities and serves as a key component for our tailored workflow engine (KubeAdaptor). By integrating the ARAS into KubeAdaptor for workflow containerized execution, we demonstrate the practical abilities of KubeAdaptor and the advantages of our ARAS. Compared with the baseline algorithm, experimental evaluation under three distinct workflow arrival patterns shows that ARAS gains time-saving of 9.8% to 40.92% in the average total duration of all workflows, time-saving of 26.4% to 79.86% in the average duration of individual workflow, and an increase of 1% to 16% in CPU and memory resource usage rate.
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。
