skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


Title: DeepContext: An OpenFlow-Compatible, Host-Based SDN for Enterprise Networks
The software-defined networking (SDN) paradigm promises greater control and understanding of enterprise network activities, particularly for management applications that need awareness of network-wide behavior. However, the current focus on switch-based SDNs raises concerns about data-plane scalability, especially when using fine-grained flows. Further, these switch-centric approaches lack visibility into end-host and application behaviors, which are valuable when making access control decisions. In recent work, we proposed a host-based SDN in which we installed software on the end-hosts and used a centralized network control to manage the flows. This improve scalability and provided application information for use in network policy. However, that approach was not compatible with OpenFlow and had provided only conservative estimates of possible network performance. In this work, we create a high performance host-based SDN that is compatible with the OpenFlow protocol. Our approach, DeepContext, provides details about the application context to the network controller, allowing enhanced decision-making. We evaluate the performance of DeepContext, comparing it to traditional networks and Open vSwitch deployments. We further characterize the completeness of the data provided by the system and the resulting benefits.  more » « less
Award ID(s):
1422180
PAR ID:
10055773
Author(s) / Creator(s):
;
Date Published:
Journal Name:
IEEE Conference on Local Computer Networks (LCN)
Page Range / eLocation ID:
112 to 119
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; ; (, IEEE International Conference on Computer Communications (INFOCOM))
    Network operators can better understand their networks when armed with a detailed understanding of the network traffic and host activities. Software-defined networking (SDN) techniques have the potential to improve enterprise security, but the current techniques have well-known data plane scalability concerns and limited visibility into the host's operating context. In this work, we provide both detailed host-based context and fine-grained control of network flows by shifting the SDN agent functionality from the network infrastructure into the end-hosts. We allow network operators to write detailed network policy that can discriminate based on user and program information associated with network flows. In doing so, we find our approach scales far beyond the capabilities of OpenFlow switching hardware, allowing each host to create over 25 new flows per second with no practical bound on the number of established flows in the network. 
    more » « less
  2. ; ; ; ; (, IEEE Network of the Future (NoF))
    The software-defined networking (SDN) paradigm offers significant flexibility for network operators. However, the SDN community has focused on switch-based implementations, which pose several challenges. First, some may require significant hardware costs to upgrade a network. Further, fine-grained flow control in a switch-based SDN results in well-known, fundamental scalability limitations. These challenges may limit the reach of SDN technologies. In this work, we explore the extent to which host-based SDN agents can achieve feature parity with switch-based SDNs. Prior work has shown the potential of host-based SDNs for security and access control. Our study finds that with appropriate preparation, a host-based agent offers the same capabilities of switch-based SDNs in the remaining key area of traffic engineering, even in a legacy managed-switch network. We find the approach offers comparable performance to switch-based SDNs while eliminating the flow table scalability and cost concerns of switch-based SDN deployments. 
    more » « less
  3. ; ; ; ; (, PEARC '18 Proceedings of the Practice and Experience on Advanced Research Computing)
    HPC networks and campus networks are beginning to leverage various levels of network programmability ranging from programmable network configuration (e.g., NETCONF/YANG, SNMP, OF-CONFIG) to software-based controllers (e.g., OpenFlow Controllers) to dynamic function placement via network function virtualization (NFV). While programmable networks offer new capabilities, they also make the network more difficult to debug. When applications experience unexpected network behavior, there is no established method to investigate the cause in a programmable network and many of the conventional troubleshooting debugging tools (e.g., ping and traceroute) can turn out to be completely useless. This absence of troubleshooting tools that support programmability is a serious challenge for researchers trying to understand the root cause of their networking problems. This paper explores the challenges of debugging an all-campus science DMZ network that leverages SDN-based network paths for high-performance flows. We propose Flow Tracer, a light-weight, data-plane-based debugging tool for SDN-enabled networks that allows end users to dynamically discover how the network is handling their packets. In particular, we focus on solving the problem of identifying an SDN path by using actual packets from the flow being analyzed as opposed to existing expensive approaches where either probe packets are injected into the network or actual packets are duplicated for tracing purposes. Our simulation experiments show that Flow Tracer has negligible impact on the performance of monitored flows. Moreover, our tool can be extended to obtain further information about the actual switch behavior, topology, and other flow information without privileged access to the SDN control plane. 
    more » « less
  4. (, IEEE Conference, on Future Internet of Things and Cloud)
    Edge Cloud (EC) is poised to brace massive machine type communication (mMTC) for 5G and IoT by providing compute and network resources at the edge. Yet, the EC being regionally domestic with a smaller scale, faces the challenges of bandwidth and computational throughput. Resource management techniques are considered necessary to achieve efficient resource allocation objectives. Software Defined Network (SDN) enabled EC architecture is emerging as a potential solution that enables dynamic bandwidth allocation and task scheduling for latency sensitive and diverse mobile applications in the EC environment. This study proposes a novel Heuristic Reinforcement Learning (HRL) based flowlevel dynamic bandwidth allocation framework and validates it through end-to-end implementation using OpenFlow meter feature. OpenFlow meter provides granular control and allows demand-based flow management to meet the diverse QoS requirements germane to IoT traffics. The proposed framework is then evaluated by emulating an EC scenario based on real NSF COSMOS testbed topology at The City College of New York. A specific heuristic reinforcement learning with linear-annealing technique and a pruning principle are proposed and compared with the baseline approach. Our proposed strategy performs consistently in both Mininet and hardware OpenFlow switches based environments. The performance evaluation considers key metrics associated with real-time applications: throughput, end-to-end delay, packet loss rate, and overall system cost for bandwidth allocation. Furthermore, our proposed linear annealing method achieves faster convergence rate and better reward in terms of system cost, and the proposed pruning principle remarkably reduces control traffic in the network. 
    more » « less
  5. ; ; ; ; (, 2019 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN))
    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. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email