NFVnice: Dynamic Backpressure and Scheduling for NFV Service Chains
NFVnice: Dynamic Backpressure and Scheduling for NFV Service Chains
复制标题
DOI：
10.1109/tnet.2020.2969971
复制
发表时间：
2017-08
期刊：
影响因子：
--
通讯作者：
Sameer G. Kulkarni;Wei Zhang-;Jinho Hwang;Shriram Rajagopalan;K. Ramakrishnan;Timothy Wood;M. Arumaithurai-M.-Arumaith
中科院分区：
文献类型：
--
作者：
Sameer G. Kulkarni;Wei Zhang-;Jinho Hwang;Shriram Rajagopalan;K. Ramakrishnan;Timothy Wood;M. Arumaithurai-M.-Arumaith
管理网络功能（NF）服务链需要仔细的系统资源管理监视高频（1000Hz）的服务链上的负载和员工的背压以在服务链中的早期减轻负载，从而防止浪费工作。通过考虑NFS的异质数据包处理成本，I/O和流量到达特征来计算CPU的借用概念，例如硬件数据包调度程序，CPU共享。 ，NFVNICE能够在安排网络功能时控制NFVNICE。但是，不需要更改OS的调度机制。使用默认的操作系统调度程序。
Managing Network Function (NF) service chains requires careful system resource management. We propose NFVnice, a user space NF scheduling and service chain management framework to provide fair, efficient and dynamic resource scheduling capabilities on Network Function Virtualization (NFV) platforms. The NFVnice framework monitors load on a service chain at high frequency (1000Hz) and employs backpressure to shed load early in the service chain, thereby preventing wasted work. Borrowing concepts such as rate proportional scheduling from hardware packet schedulers, CPU shares are computed by accounting for heterogeneous packet processing costs of NFs, I/O, and traffic arrival characteristics. By leveraging cgroups, a user space process scheduling abstraction exposed by the operating system, NFVnice is capable of controlling when network functions should be scheduled. NFVnice improves NF performance by complementing the capabilities of the OS scheduler but without requiring changes to the OS’s scheduling mechanisms. Our controlled experiments show that NFVnice provides the appropriate rate-cost proportional fair share of CPU to NFs and significantly improves NF performance (throughput and latency) by reducing wasted work across an NF chain, compared to using the default OS scheduler. NFVnice achieves this even for heterogeneous NFs with vastly different computational costs and for heterogeneous workloads.