
NSF Org: |
CNS Division Of Computer and Network Systems |
Recipient: |
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Initial Amendment Date: | September 9, 2014 |
Latest Amendment Date: | January 18, 2017 |
Award Number: | 1422362 |
Award Instrument: | Standard Grant |
Program Manager: |
Darleen Fisher
CNS Division Of Computer and Network Systems CSE Directorate for Computer and Information Science and Engineering |
Start Date: | October 1, 2014 |
End Date: | September 30, 2019 (Estimated) |
Total Intended Award Amount: | $247,899.00 |
Total Awarded Amount to Date: | $279,899.00 |
Funds Obligated to Date: |
FY 2015 = $16,000.00 FY 2017 = $16,000.00 |
History of Investigator: |
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Recipient Sponsored Research Office: |
1918 F ST NW WASHINGTON DC US 20052-0042 (202)994-0728 |
Sponsor Congressional District: |
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Primary Place of Performance: |
801 22nd Street NW Washington DC US 20052-0058 |
Primary Place of
Performance Congressional District: |
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Unique Entity Identifier (UEI): |
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Parent UEI: |
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NSF Program(s): |
Special Projects - CNS, Networking Technology and Syst |
Primary Program Source: |
01001516DB NSF RESEARCH & RELATED ACTIVIT 01001718DB NSF RESEARCH & RELATED ACTIVIT |
Program Reference Code(s): |
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Program Element Code(s): |
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Award Agency Code: | 4900 |
Fund Agency Code: | 4900 |
Assistance Listing Number(s): | 47.070 |
ABSTRACT
Traditional computer networks have been built from hardware appliances, such as routers, firewalls, and switches, to implement functionality. These devices can process network packets at high speed, but provide little flexibility since they are based on purpose-built hardware. Recent improvements in multi-core processors and high-speed network interface cards have enabled Network Function Virtualization (NFV), which allows these network components to run instead on commodity compute servers. NFV makes the network data processing elements run as software, allowing them to be deployed dynamically or easily modified and tuned with changes in network workloads. At the same time, Software Defined Networking (SDN) has grown in popularity as a way to manage more easily network services by centralizing control plane functions. This research investigates how the convergence of NFV and SDN can enable a new breed of highly dynamic network services for customers of Internet Service Providers (ISPs), and also grant cloud computing customers far greater control over data center resources. The work will explore both the software mechanisms needed to support network components running at speeds well beyond 10 Gbps inside of virtual machines, and the algorithms and control architectures required to coordinate these components with high performance and low cost.
The project targets two application areas for Software Defined Network Function Virtualization (SDNFV). The first is dynamic services for network providers for which the principal investigators (PIs) are developing a SDNFV platform that enables line-rate packet processing within virtual machines by exploiting network interface controller (NIC) polling and shared memory for zero-copy communication. This flexible infrastructure will allow packets to be redirected based on complex policies, packet data, or service state, which is not currently possible in hardware-based solutions. The second focus area is on cloud computing data centers in which SDNFV will enable cloud data center operators to easily partition and multiplex network resources in the same way they currently virtualize servers and storage devices. In this application area the PIs are developing virtualization-layer trust boundaries that provide strict performance and data isolation, while still permitting the optimizations required for SDNFV?s fast packet processing. They will also study the new resource management and scheduling algorithms required to ensure a group of virtual machine-based network services can meet their strict latency requirements. Finally the PIs will evaluate their ideas by building prototypes and testing them using realistic benchmark workloads and traces.
The proposed work has the potential to redefine how networks are built and managed, by transitioning away from single-purpose hardware to flexible software-based network components. This research could make the connected, digital world we rely on more efficient and more responsive to workload changes, attacks, and policy decisions. The research will be paired with an educational program to enhance the networking and distributed systems curriculum at the researchers' institutions. This will help prepare undergraduate, Masters, and Ph.D. students to enter the work force with highly sought-after experience in the latest networking technologies.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
Networks are moving to be more software based, enabling them to be more flexible and nimble, and exploiting the capability of common-off-the-shelf (COTS) platforms to take advantage of their lower cost-performance curve. The overall goal of our project was to better integrate Software Defined Networks (SDN) and Network Function Virtualization (NFV) to create more efficient and powerful networks. We expect more of the traditional (and evolving) network middlebox network functionality will migrate to high-performance NFV middleboxes running in cloud data centers, emphasizing the need for efficient software solutions.
In this project we sought to build a "Smart Dataplane" that takes advantage of efficient NFV processing to allow middleboxes to both transform and reroute network flows. To build a smart data plane that is efficient and intelligent enough to make localized decisions, requires new systems techniques that allow software-based networks to perform comparably to traditional hardware devices, and new protocols between SDN and NFV.
To this end, we developed the OpenNetVM NFV platform which uses the Data Plane Development Kit (DPDK) for efficient I/O, and Docker lightweight containers to provide the virtualization framework for Network Functions (like deep-packet inspection, caches, firewalls, intrusion detection systems, switches, etc.). OpenNetVM has been released as an open-source (BSD license) software. This activity has been a joint effort involving both George Washington University and the University of California, Riverside, with further contributions from outside collaborators. OpenNetVM provides high-level abstractions on an underlying framework that offers wire-speed performance. OpenNetVM runs network functions in lightweight containers, easily combined to form complex "service chains". The code is available at https://github.com/sdnfv/openNetVM and continues to be updated as we add new features and users report bugs.
Leveraging OpenNetVM as a base, we have explored a variety of research topics related to the management, performance, deployment, and reliability of smart data plane platforms. Notable outcomes include our NFVNice paper at SIGCOMM 2017 which introduced new techniques for ensuring the performance of NFV service chains, the Microboxes paper at SIGCOMM 2018 which presented a new more flexible TCP stack design for middlebox applications, and the REINFORCE paper at CoNext 2018 which provided high reliability services for middlebox service chains without incurring a high performance penalty.
In total we have published 19 conference and workshop papers, 4 journal papers, 2 demos, and 2 posters at conferences throughout the project. We have also given a total of 6 tutorials at conferences and other venues to increase awareness of our smart data plane techniques. The project also had collaborations with researchers from industry, including those from HP, AT&T, Huawei, IBM, and Intel. A number of other researchers use the OpenNetVM platform for their research, as evidenced by the OpenNetVM Github repository's activities. The project supported nine Ph.D. students at both George Washington University and UC Riverside (including visiting students from France and Germany), six MS students, and nearly a dozen undergraduate students. This diverse group of students has gone on to work at top software companies, joined Ph.D. programs, post doc positions, or faculty jobs. A Ph.D. student from GW was selected as one of the 10 N2Women Rising Stars of 2019 based in part on her work completed under this project.
Last Modified: 01/28/2020
Modified by: Timothy Wood
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