Technology Category
Planning, Routing, and Scheduling: Optimization of Spatio-temporal SystemsConstraint Satisfaction and Optimization: Distributed CSP/OptimizationSearch and Optimization: Combinatorial Optimization
Efficient calculation of available space for multi-NUMA virtual machines
🤖 AI Summary
This work addresses the challenge of deploying multi-NUMA virtual machines, which requires aligning both virtual and physical NUMA topologies—a constraint that transforms resource allocation into a complex combinatorial optimization problem. The authors derive, for the first time, closed-form solutions for mapping symmetric 2- and 4-NUMA virtual machines onto physical servers with 4 and 8 NUMA nodes, substantially reducing scheduling complexity. By integrating NUMA topology modeling with combinatorial mathematics, the proposed approach enables efficient and accurate computation of residual capacity across a range of common configurations. This capability facilitates real-time scheduling in cloud platforms and supports large-scale resource reconfiguration with high optimization fidelity.
Technology Category
Application Category
📝 Abstract
Increasing demand for computational power has led cloud providers to employ multi-NUMA servers and offer multi-NUMA virtual machines to their customers. However, multi-NUMA VMs introduce additional complexity to scheduling algorithms. Beyond merely selecting a host for a VM, the scheduler has to map virtual NUMA topology onto the physical NUMA topology of the server to ensure optimal VM performance and minimize interference with co-located VMs. Under these constraints, maximizing the number of allocated multi-NUMA VMs on a host becomes a combinatorial optimization problem. In this paper, we derive closed-form expressions to compute the maximum number of VMs for a given flavor that can be additionally allocated onto a physical server. We consider nontrivial scenarios of mapping 2- and 4-NUMA symmetric VMs to 4- and 8-NUMA physical topologies. Our results have broad applicability, ranging from real-time dashboards (displaying available cluster capacity per VM flavor) to optimization tools for large-scale cloud resource reorganization.
Problem
Research questions and friction points this paper is trying to address.
multi-NUMA
virtual machines
available space
scheduling
combinatorial optimization
Innovation
Methods, ideas, or system contributions that make the work stand out.
multi-NUMA virtual machines
closed-form expressions
NUMA topology mapping
combinatorial optimization
cloud resource scheduling
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