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Software Defined Networking (SDN) and Network Function Virtualization (NFV) are transforming Data Center (DC), Telecom, and enterprise networking. The programmability offered by P4 enables SDN to be more protocol-independent and flexible. Data Centers are increasingly adopting SmartNICs (sNICs) to accelerate packet processing that can be leveraged to support packet processing pipelines and custom Network Functions (NFs). However, there are several challenges in integrating and deploying P4 based SDN control as well as host and sNIC-based programmable NFs. These include configuration and management of the data plane components (Host and sNIC P4 switches) for the SDN control plane and effective utilization of data plane resources. P4NFV addresses these concerns and provides a unified P4 switch abstraction framework to simplify the SDN control plane, reducing management complexities, and leveraging a host-local SDN Agent to improve the overall resource utilization. The SDN agent considers the network-wide, host, and sNIC specific capabilities and constraints. Based on workload and traffic characteristics, P4NFV determines the partitioning of the P4 tables and optimal placement of NFs (P4 actions) to minimize the overall delay and maximize resource utilization. P4NFV uses Mixed Integer Linear Programming (MILP) based optimization formulation and achieves up to 2. 5X increase in system capacity while minimizing the delay experienced by flows. P4NFV considers the number of packet exchanges, flow size, and state dependency to minimize the delay imposed by data transmission over PCI Express interface.
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P4CGO: Control Plane Guided P4 Program Optimization
Software-defined networking (SDN) in conjunction with programmable switches revolutionizes network management, yet crafting optimal switch configurations remains complex. Traditional P4 optimizations rely on data plane level tuning. In this paper, we argue an essential piece for such optimizations is the control plane itself. We present P4CGO, a P4 compilation framework which focuses on realizing specifications based on control policies. P4CGO leverages user-defined objective functions and control plane policies to guide P4 program optimization through table merging and splitting. We have prototyped P4CGO and applied it solving real-world policy optimization problems.
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- PAR ID:
- 10526396
- Publisher / Repository:
- ACM
- Date Published:
- ISBN:
- 9798400707148
- Page Range / eLocation ID:
- 1 to 7
- Format(s):
- Medium: X
- Location:
- Sydney NSW Australia
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3672199.3673892
