Guyue Liu

OpenNetVM: A Platform for High Performance Network Service Chains

Network middleboxes are growing in number and diversity. Middleboxes have been deployed widely to complement the basic end-to-end functionality provided by the Internet Protocol suite that depends only on the minimal functionality of a best-effort network layer. The move from purpose-built hardware middleboxes to software appliances running in virtual machines provides much needed deployment flexibility, but significant challenges remain. Just as Software Defined Networking (SDN) research and product development was greatly accelerated with the release of several open source SDN platforms, we believe that Network Function Virtualization (NFV) research can see similar growth with the development of a flexible platform that enables high performance NFV prototypes. Towards this end we have been building OpenNetVM, an efficient packet processing framework that greatly simplifies the development of network functions, as well as research into their management and optimization. OpenNetVM runs network functions in lightweight Docker containers, enabling fast startup and reducing memory overheads. The OpenNetVM platform manager provides load balancing, flexible flow management, and service name abstractions. OpenNetVM efficiently routes packets through dynamically created service chains, achieving throughputs of 10 Gbps even when traversing a chain of 6 NFs. In this paper we describe our architecture and evaluate its performance compared to existing NFV platforms.

Virtual function placement and traffic steering in flexible and dynamic software defined networks

The integration of network function virtualization (NFV) and software defined networks (SDN) seeks to create a more flexible and dynamic software-based network environment. The line between entities involved in forwarding and those involved in more complex middle box functionality in the network is blurred by the use of high-performance virtualized platforms capable of performing these functions. A key problem is how and where network functions should be placed in the network and how traffic is routed through them. An efficient placement and appropriate routing increases system capacity while also minimizing the delay seen by flows. In this paper, we formulate the problem of network function placement and routing as a mixed integer linear programming problem. This formulation not only determines the placement of services and routing of the flows, but also seeks to minimize the resource utilization. We develop heuristics to solve the problem incrementally, allowing us to support a large number of flows and to solve the problem for incoming flows without impacting existing flows.

CloudNet: dynamic pooling of cloud resources by live WAN migration of virtual machines

Virtualization technology and the ease with which virtual machines (VMs) can be migrated within the LAN have changed the scope of resource management from allocating resources on a single server to manipulating pools of resources within a data center. We expect WAN migration of virtual machines to likewise transform the scope of provisioning resources from a single data center to multiple data centers spread across the country or around the world. In this paper, we present the CloudNet architecture consisting of cloud computing platforms linked with a virtual private network (VPN)-based network infrastructure to provide seamless and secure connectivity between enterprise and cloud data center sites. To realize our vision of efficiently pooling geographically distributed data center resources, CloudNet provides optimized support for live WAN migration of virtual machines. Specifically, we present a set of optimizations that minimize the cost of transferring storage and virtual machine memory during migrations over low bandwidth and high-latency Internet links. We evaluate our system on an operational cloud platform distributed across the continental US. During simultaneous migrations of four VMs between data centers in Texas and Illinois, CloudNets optimizations reduce memory migration time by 65% and lower bandwidth consumption for the storage and memory transfer by 19 GB, a 50% reduction.

Cloud-Scale Application Performance Monitoring with SDN and NFV

In cloud data centers, more and more services are deployed across multiple tiers to increase flexibility and scalability. However, this makes it difficult for the cloud provider to identify which tier of the application is the bottleneck and how to resolve performance problems. Existing solutions approach this problem by constantly monitoring either in end-hosts or physical switches. Host based monitoring usually needs instrumentation of application code, making it less practical, while network hardware based monitoring is expensive and requires special features in each physical switch. Instead, we believe network wide monitoring should be flexible and easy to deploy in a non-intrusive way by exploiting recent advances in software-based network services. Towards this end we are developing a distributed software-based network monitoring framework for cloud data centers. Our system leverages knowledge of topology and routing information to build relationships between each tier of the application, and detect and locate performance bottlenecks by monitoring the network inside software switches.

SDNFV: Flexible and Dynamic Software Defined Control of an Application- and Flow-Aware Data Plane

Software Defined Networking (SDN) promises greater flexibility for directing packet flows, and Network Function Virtualization promises to enable dynamic management of software-based network functions. However, the current divide between an intelligent control plane and an overly simple, stateless data plane results in the inability to exploit the flexibility of a software based network. In this paper we propose SDNFV, a framework that expands the capabilities of network processing-and-forwarding elements to flexibly manage packet flows, while retaining both a high performance data plane and an easily managed control plane. SDNFV proposes a hierarchical control framework where decisions are made across the SDN controller, a host-level manager, and individual VMs to best exploit state available at each level. This increases the networks flexibility compared to existing SDNs where controllers often make decisions solely based on the first packet header of a flow. SDNFV intelligently places network services across hosts and connects them in sequential and parallel chains, giving both the SDN controller and individual network functions the ability to enhance and update flow rules to adapt to changing conditions. Our prototype demonstrates how to efficiently and flexibly reroute flows based on data plane state such as packet payloads and traffic characteristics.

NetAlytics: Cloud-Scale Application Performance Monitoring with SDN and NFV

In cloud data centers, more and more services are deployed across multiple tiers to increase flexibility and scalability. However, this makes it difficult for the cloud provider to identify which tier of the application is the bottleneck and how to resolve performance problems. Existing solutions approach this problem by constantly monitoring either in end-hosts or physical switches. Host based monitoring usually needs instrumentation of application code, making it less practical, while network hardware based monitoring is expensive and requires special features in each physical switch. Instead, we believe network wide monitoring should be flexible and easy to deploy in a non-intrusive way by exploiting recent advances in software-based network services. Towards this end we are developing a distributed software-based network monitoring framework for cloud data centers. Our system leverages knowledge of topology and routing information to build relationships between each tier of the application, and detect and locate performance bottlenecks by monitoring the network inside software switches.

Design Challenges for High Performance, Scalable NFV Interconnects

Software-based network functions (NFs) have seen growing interest. Increasingly complex functionality is achieved by having multiple functions chained together to support the required network-resident services. Network Function Virtualization (NFV) platforms need to scale and achieve high performance, potentially utilizing multiple hosts in a cluster. Efficient data movement is crucial, a cornerstone of kernel bypass. Moving packet data involves delivering the packet from the network interface to an NF, moving it across functions on the same host, and finally across yet another network to NFs running on other hosts in a cluster/data center. In this paper we measure the performance characteristics of different approaches for moving data at each of these levels. We also introduce a new high performance inter-host interconnect using InfiniBand. We evaluate the performance of Open vSwitch and the Open Net VM NFV platform, considering a simple forwarding function and Snort, a popular intrusion detection system.

OpenNetVM: Flexible, high performance NFV (Demo)

Network Function Virtualization promises to enable dynamic management of software-based network functions. We envision a dynamic and flexible network that can support a smarter data plane than just simple switches that forward packets. This network architecture supports complex stateful rourtng of flows where processing by network functions (NFs) can transform packet data, customized on a per-flow basis, as it moves between end points. This demo will present OpenNetVM, a highly efficient packet processing framework that greatly simplifies the development of network functions, as well as their management and optimization. OpenNetVM runs network functions in lightweight Docker containers that start in less than a second. The OpenNetVM platform manager provides load balancing, flexible flow management, and service name abstractions. OpenNetVM uses DPDK for high performance I/O, and efficiently routes packets through dynamically created service chains. We will demonstrate how the research community can easily build new network functions and rapidly deploy them to see their effectiveness in high performance network environments.