Novel Abstractions for High Performance Network Functions Public
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The past decade has witnessed the pervasive adoption of cloud computing by enterprises, startups, and individual users. Massive amounts of data are flowing into data centers from a diverse range of applications and devices, generating unprecedented demands and new challenges on networking infrastructure. Today's networks are responsible for more than routing packets and perform a wide range of functionality such as firewalls, proxies, and caches to improve performance and security. Specialized hardware devices are used to run these functions but are no longer a great choice for cloud providers and large carriers due to the high cost and low flexibility. Software-based solutions including Network Function Virtualization (NFV) and Software Defined Networking (SDN) are proposed to renovate current networks but pose many challenging and interesting systems problems. In this thesis, I investigate challenges related to building NFV platforms. I first study performance problems related to the Network Function (NF) communication, and I demonstrate how shared-memory and Remote Direct Memory Access (RDMA) can be used to achieve zero-copy packet delivery, improving the throughput and reducing the latency. I next study how NFV and SDN can be combined to build a non-intrusive monitoring platform, which allows for monitoring and debugging network and application performance in real time. I finally study how a new service chain abstraction with modular TCP stacks and a publish/subscribe event interface can be built to improve flexibility and performance for transport and application layers.
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