Resource management is a vital factor for better performance in cloud systems and many resource allocation algorithms have been studied. In this work, focusing on applications with timing constraints (i.e., deadlines) running on resource-constrained clouds that have multiple heterogeneous nodes of computing resources (e.g., CPU cores and memory), we propose TIMER-Cloud, a time-sensitive resource allocation and virtual machine (VM) provisioning framework. As a key component of the framework, user requests (of running certain applications) are prioritized according to their deadlines and resource demands (in the form of VM and its operation time). Specifically, in addition to the intuitive Earliest Deadline First (EDF) ordering of requests, we propose three prioritization heuristics: a) one based on the Time-Sensitive Resource Factor (TSRF) that incorporates a request's deadline and usage efficiency of all its resources; b) the Dominant Share (DS) extension of TSRF that emphasizes the most demanded resource of a request aiming at obtaining balanced resource usage among the nodes; and c) a unified k-EDF scheme that integrates the ideas of EDF and TSRF/DS to balance the needs of meeting imminent deadlines of requests and improving resource usage efficiency. Then, for the mapping of the prioritized user requests to the heterogeneous nodes, we propose a novel request-to-node mapping algorithm based on the idea of euclidean Distance that finds the node with the best match of its resource requirements for each request. TIMER-Cloud has been implemented and validated on a cloud testbed powered by OpenStack with a few heterogeneous nodes. The proposed VM provisioning schemes are further evaluated through extensive simulations using the execution data of benchmark applications. The results show that the proposed schemes can outperform the state-of-the-art deadline oblivious scheme by serving up to 12 percent more user requests and achieving up to 8 percent more system rewards for the over-loaded scenario with 140 percent system load.

中文翻译：

---

TIMER-Cloud：资源受限云中的时间敏感 VM 配置

资源管理是在云系统中获得更好性能的重要因素，并且已经研究了许多资源分配算法。在这项工作中，针对在具有多个异构计算资源节点（例如 CPU 内核和内存）的资源受限云上运行的具有时序约束（即截止日期）的应用程序，我们提出了 TIMER-Cloud，一种时间敏感的资源分配和虚拟机 (VM) 配置框架。作为框架的一个关键组件，用户请求（运行某些应用程序）根据其截止日期和资源需求（以 VM 及其运行时间的形式）进行优先级排序。具体来说，除了直观的最早截止日期优先 (EDF) 请求排序之外，我们还提出了三个优先级启发式方法：a) 基于时间敏感资源因子 (TSRF) 的一种，该因子包含请求的截止日期和其所有资源的使用效率；b) TSRF 的主导共享 (DS) 扩展，强调请求中最需要的资源，旨在获得节点之间的平衡资源使用；c) 统一的 k-EDF 方案，融合了 EDF 和 TSRF/DS 的思想，以平衡满足迫在眉睫的请求期限和提高资源使用效率的需求。然后，为了将优先的用户请求映射到异构节点，我们提出了一种基于欧氏距离思想的请求到节点映射算法，该算法为每个请求找到与其资源需求最匹配的节点。TIMER-Cloud 已在由 OpenStack 提供支持的云测试平台上实施和验证，其中包含一些异构节点。通过使用基准应用程序的执行数据的广泛模拟，进一步评估了所提出的 VM 配置方案。结果表明，所提出的方案可以通过服务多达 12% 的用户请求并在 140% 系统负载的过载场景中实现多达 8% 的系统奖励，从而优于最先进的期限不经意的方案。