As cloud demand for computation and network resources fluctuates, effective resource management becomes essential for optimizing allocation and enhancing performance in virtualization-based applications. Current methods struggle to efficiently schedule multiple virtual resources for dynamic workloads. To address this, we propose a self-adaptive elastic virtual resource management (EVRM) framework that comprises a monitor, analyzer, planner, and executor, enabling dynamic scheduling of CPU, memory, and bandwidth for virtual instances. Central to EVRM is a resource management model employing a novel deep reinforcement learning approach, the deep deterministic policy gradient-based resource allocation (DDPG-RA), which coordinates resource allocation by automatically exploring optimization policies and learning complex relationships between resource allocation and performance. Additionally, DDPG-RA features an action refinement algorithm to derive multiple resource allocations from its outputs. Experimental results using OpenStack demonstrate that EVRM significantly enhances performance, achieving approximately 52.87% faster benchmark completion times and a 41.37% reduction in average time under both light and heavy loads, outperforming three competing approaches while optimizing physical resource utilization.

中文翻译：

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EVRM：适用于云虚拟实例的 Elastic Virtual Resource Management 框架


随着云对计算和网络资源的需求的波动，有效的资源管理对于优化分配和增强基于虚拟化的应用程序的性能变得至关重要。当前的方法难以为动态工作负载有效地安排多个虚拟资源。为了解决这个问题，我们提出了一个自适应的弹性虚拟资源管理 （EVRM） 框架，该框架由监控器、分析器、规划器和执行程序组成，支持对虚拟实例的 CPU、内存和带宽进行动态调度。EVRM 的核心是一种资源管理模型，该模型采用一种新颖的深度强化学习方法，即基于深度确定性策略梯度的资源分配 （DDPG-RA），它通过自动探索优化策略和学习资源分配与性能之间的复杂关系来协调资源分配。此外，DDPG-RA 还具有操作优化算法，可从其输出中派生多个资源分配。使用 OpenStack 的实验结果表明，EVRM 显著提高了性能，在轻负载和重负载下，基准测试完成时间缩短了约 52.87%，平均时间缩短了 41.37%，在优化物理资源利用率的同时优于三种竞争方法。