基于累积资源预算约束的上下文赌博机网络切片需求分解

中文摘要 AI 辅助

端到端（E2E）网络切片（NSs）在5G网络的多个域中提供。在分层网络切片管理中，租户提交网络切片请求（NSR），该请求指定端到端服务等级协议（SLA）要求。E2E控制器不直接管理这些域，而是将每个NSR分解为域级SLA要求，并将资源分配委托给特定于域的控制器，这些控制器返回可行性和资源消耗反馈。因此，较差的分解策略可能通过产生不可行的要求而导致当前请求被拒绝，或通过将资源消耗集中在瓶颈域而减少未来的接纳机会。我们将此分解策略优化问题称为网络切片请求分解问题（NSR-DP）。为了实际运行，已提出了NSR-DP的在线方法。此类方法必须同时满足两个要求：（R1）控制长期资源预算，（R2）使每次分解适应到达的NSR的SLA中指定的性能目标和保证级别。为满足这些要求，我们引入了上下文约束核赌博机（CCKB）作为NSR-DP的在线解决方案。为解决（R1），CCKB对使用变得紧张的资源提高惩罚，从而阻止消耗瓶颈资源的分解。为解决（R2），它使用高斯过程（GPs）为当前NSR预测候选分解的奖励和资源使用情况，使其能够选择适合性能目标和保证级别的分解。我们为所得公式建立了高概率保证，并通过跨拓扑、瓶颈和流量混合设置的广泛5G模拟表明，CCKB在绝大多数条件下优于基线。

英文摘要

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.

发表机构

• Network Service Systems Laboratories, NTT, Inc.（NTT公司网络服务系统实验室）
• Faculty of Information Networking for Innovation and Design, Toyo University（东洋大学信息创新设计学院）

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