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A Resource-Efficient Predictive Resource Provisioning System in Cloud Systems
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AI
DOI：10.1109/TPDS.2022.3172493.png)
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Abstract
En 中文
In cloud systems, demand-prediction based resource provisioning schemes help assure the SLOs (service level objectives) of cloud tenants. We notice that if a provisioning scheme does not exclude bursts from historical resource demands in normal demand prediction or always uses a large padding to correct under-prediction, it will lead to resource over-provisioning and low resource utilization. To improve the previous schemes, in this paper, we present a Resource-efficient Predictive Resource Provisioning system in cloud systems (RPRP) that excludes bursts in demand prediction and has algorithms to specifically handle bursts to avoid resource over-provisioning. Rather than setting padding to a possibly high value, RPRP has a load-dependent padding algorithm that adaptively determines padding based on predicted demands. To handle bursts, RPRP has a burst-resilient shared padding algorithm that reserves resource shared by multiple co-located VMs rather than for individual VMs. It also embodies a responsive padding algorithm that adaptively adjusts padding to recover from both under-provisioning and over-provisioning. We implemented RPRP on top of Xen and conducted both trace-driven simulation and real-world testbed experiments. The experimental results show that RPRP achieves higher resource utilization, more accurate demand predictions, and fewer SLO violations than previous schemes.
Keywords:
Resource management
Prediction algorithms
Time series analysis
Cloud computing
Heuristic algorithms
System analysis and design
Monitoring
Cloud
resource management
resource provisioning
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