Scholarlink

Cloud Native Functions (CNFs) support automated and dynamic orchestration of containerized network services, replacing traditional hardware-based architectures. These deployments consist of modular microservices that enable elastic scalability and collaborative service delivery. This paper presents an approximation framework for capacity-constrained CNF resource allocation, modeled as variants of the Group Generalized Assignment Problem (Group GAP). The main contributions are (1) a \(\frac{1}{2}\) -approximation algorithm for CNF placement when each function’s footprint is at most half the cluster capacity and (2) a \(\frac{1}{2}(1 - e^{-1/d})\) -approximation for shared microservices among multiple CNFs, where d is the degree of sharing, supported by experimental evaluation of the algorithm relative error.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Provably Efficient Resource Allocation of Cloud Native Functions For Network Services

  • 会议论文

  • 发表日期：

    2026年

查看全文
  • Nikolaos Lazaropoulos,
  • Ioannis Vaxevanakis,
  • Ioannis Sigalas,
  • Ioannis Lamprou,
  • Vassilis Zissimopoulos

摘要

Cloud Native Functions (CNFs) support automated and dynamic orchestration of containerized network services, replacing traditional hardware-based architectures. These deployments consist of modular microservices that enable elastic scalability and collaborative service delivery. This paper presents an approximation framework for capacity-constrained CNF resource allocation, modeled as variants of the Group Generalized Assignment Problem (Group GAP). The main contributions are (1) a \(\frac{1}{2}\) -approximation algorithm for CNF placement when each function’s footprint is at most half the cluster capacity and (2) a \(\frac{1}{2}(1 - e^{-1/d})\) -approximation for shared microservices among multiple CNFs, where d is the degree of sharing, supported by experimental evaluation of the algorithm relative error.