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EVRM: Elastic Virtual Resource Management framework for cloud virtual instances
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AI
DOI：10.1016/j.future.2024.107569.png)
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Abstract
En 中文
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.
Keywords:
Multiple virtual resources
Elastic resource management
Deep reinforcement learning
OpenStack
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6.1
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