Contextual bandits improve 5G network slice resource allocation decisions
Contextual Bandit-Based Decomposition of Network Slice Requirements under Cumulative Resource Budget Constraints
Networking and Internet Architecture
Summary
5G networks divide resources into slices to serve different users with specific needs. The authors study how to better split these resource requests across parts of the network to avoid refusals or bottlenecks. They propose an online learning method using contextual bandits that predicts and balances resource use according to each request's goals and resource limits. Their approach helps keep resource use balanced and meets service targets more reliably than previous methods in simulated 5G scenarios.
What this means in practice
- •For 5g network operators: Allocate network slice resources across multiple domains efficiently while meeting service agreement targets and managing limited resource budgets.
- •For cloud infrastructure teams: Use adaptive online resource allocation methods to balance container or VM resource requests under aggregate budget constraints.
Authors
Masaki Kobayashi, Akito Suzuki, Ryoichi Kawahara, Masahiro Kobayashi
Abstract
End-to-end (E2E) network slices (NSs) are provisioned across multiple domains of the 5G network. In hierarchical NS management, a tenant submits a network slice request (NSR), which specifies E2E service level agreement (SLA) requirements. Rather than managing these domains directly, an E2E controller decomposes each NSR into domain-level SLA requirements and delegates resource allocation to domain-specific controllers, which return feasibility and resource-consumption feedback. A poor decomposition policy can therefore cause rejection of the current request by producing infeasible requirements or reduce future admission opportunities by concentrating resource consumption in bottleneck domains. We call this decomposition-policy optimization problem the network slice request decomposition problem (NSR-DP). For practical operation, online approaches to NSR-DP have been proposed. Such approaches must jointly meet two requirements: (R1) control long-term resource budgets and (R2) adapt each decomposition to the performance targets and guarantee levels specified in the arriving NSR's SLA. To meet these requirements, we introduce contextual constrained kernel bandits (CCKB) as an online solution for NSR-DP. To address (R1), CCKB raises penalties for using resources that become tight, thereby discouraging decompositions that consume bottleneck resources. To address (R2), it uses Gaussian processes (GPs) to predict, for the current NSR, the reward and resource usage of candidate decompositions, allowing it to select a decomposition suited to the performance targets and guarantee levels. We establish high-probability guarantees for the resulting formulation and show through extensive 5G simulations across topology, bottleneck, and traffic-mixture settings that CCKB outperforms the baselines in the large majority of conditions.