skip to main content

Explore Research Products in the NSF-PAR

Title: A hardware-in-the-loop emulation testbed for high fidelity and reproducible network experiments
The transformation of innovative research ideas to production systems is highly dependent on the capability of performing realistic and reproducible network experiments. In this work, we present a network testbed consisting of container-based network emulation and physical devices to advocate high fidelity and reproducible networking experiments. The testbed integrates network emulators (Mininet), a distributed control environment (ONOS), and physical switches (Pica8). The testbed (1) offers functional fidelity through unmodified code execution in emulated networks, (2) supports large-scale network experiments using lightweight OS-level virtualization techniques and capable of running across distributed physical machines, (3) provides the topology flexibility, and (4) enhances the repeatability and reproducibility of network experiments. We validate the testbed fidelity through extensive experiments under different network conditions (e.g., varying topology and traffic pattern). We also use the testbed to reproduce key results from published network experiments, such as Hedera, a scalable and adaptive network traffic flow scheduling system.  more » « less
Award ID(s):
1730488
NSF-PAR ID:
10063958
Author(s) / Creator(s):
; ; ; ;
Date Published:
Journal Name:
2017 Winter Simulation Conference (WSC)
Page Range / eLocation ID:
408 to 418
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; ( , 2020 29th International Conference on Computer Communications and Networks (ICCCN))
    null (Ed.)
    Network traffic modeling plays an important role in the generation of realistic network traffic in test environments. Especially in cases where researchers carry out experiments with real production-like traffic, as seen in specific home, enterprise, campus, LAN, or WAN networks. We present our ongoing work on a new framework that enables the methodical creation of application-agnostic traffic models from given network traces of a known network topology. The framework then uses these models to generate realistic traffic on a given network topology. We share a preliminary evaluation of the framework based on repeatable experiments where we model a typical web application traffic and then regenerate the traffic based on the model in a test network on our VTS (Virtual Topology Services) testbed. 
    more » « less
  2. ; ; ; ; ; ( , Journal of Optical Communications and Networking)

    With the ever-increasing size of training models and datasets, network communication has emerged as a major bottleneck in distributed deep learning training. To address this challenge, we propose an optical distributed deep learning (ODDL) architecture. ODDL utilizes a fast yet scalable all-optical network architecture to accelerate distributed training. One of the key features of the architecture is its flow-based transmit scheduling with fast reconfiguration. This allows ODDL to allocate dedicated optical paths for each traffic stream dynamically, resulting in low network latency and high network utilization. Additionally, ODDL provides physically isolated and tailored network resources for training tasks by reconfiguring the optical switch using LCoS-WSS technology. The ODDL topology also uses tunable transceivers to adapt to time-varying traffic patterns. To achieve accurate and fine-grained scheduling of optical circuits, we propose an efficient distributed control scheme that incurs minimal delay overhead. Our evaluation on real-world traces showcases ODDL’s remarkable performance. When implemented with 1024 nodes and 100 Gbps bandwidth, ODDL accelerates VGG19 training by 1.6× and 1.7× compared to conventional fat-tree electrical networks and photonic SiP-Ring architectures, respectively. We further build a four-node testbed, and our experiments show that ODDL can achieve comparable training time compared to that of anidealelectrical switching network.

     
    more » « less
  3. ; ; ; ( , IEEE systems journal)
    The ongoing electrification of the transportation fleet will increase the load on the electric power grid. Since both the transportation network and the power grid already experience periods of significant stress, joint analyses of both infrastructures will most likely be necessary to ensure acceptable operation in the future. To enable such analyses, this paper presents an open- source testbed that jointly simulates high-fidelity models of both the electric distribution system and the transportation network. The testbed utilizes two open-source simulators, OpenDSS to simulate the electric distribution system and the microscopic traffic simulator SUMO to simulate the traffic dynamics. Electric vehicle charging links the electric distribution system and the transportation network models at vehicle locations determined using publicly available parcel data. Leveraging high-fidelity synthetic electric distribution system data from the SMART-DS project and transportation system data from OpenStreetMap, this testbed models the city of Greensboro, NC down to the household level. Moreover, the methodology and the supporting scripts released with the testbed allow adaption to other areas where high-fidelity geolocated OpenDSS datasets are available. After describing the components and usage of the testbed, we exemplify applications enabled by the testbed via two scenarios modeling the extreme stresses encountered during evacuations. 
    more » « less
  4. ; ; ; ; ; ( , ACM SIGCOMM'17)
    This paper promotes convertible data center network architectures, which can dynamically change the network topology to combine the benefits of multiple architectures. We propose the flat-tree prototype architecture as the first step to realize this concept. Flat-tree can be implemented as a Clos network and later be converted to approximate random graphs of different sizes, thus achieving both Clos-like implementation simplicity and random-graph-like transmission performance. We present the detailed design for the network architecture and the control system. Simulations using real data center traffic traces show that flat-tree is able to optimize various workloads with different topology options. We implement an example flat-tree network on a 20-switch 24-server testbed. The traffic reaches the maximal throughput in 2.5s after a topology change, proving the feasibility of converting topology at run time. The network core bandwidth is increased by 27.6% just by converting the topology from Clos to approximaterandom graph. This improvement can be translated into acceleration of applications as we observe reduced communication time in Spark and Hadoop jobs. 
    more » « less
  5. ; ; ; ; ; ; ; ; ; ; et al ( , IEEE Transactions on Mobile Computing)
    Synthetic traffic generation can produce sufficient data for model training of various traffic analysis tasks for IoT networks with few costs and ethical concerns. However, with the increasing functionalities of the latest smart devices, existing approaches can neither customize the traffic generation of various device functions nor generate traffic that preserves the sequentiality among packets as the real traffic. To address these limitations, this paper proposes IoTGemini, a novel framework for high-quality IoT traffic generation, which consists of a Device Modeling Module and a Traffic Generation Module. In the Device Modeling Module, we propose a method to obtain the profiles of the device functions and network behaviors, enabling IoTGemini to customize the traffic generation like using a real IoT device. In the Traffic Generation Module, we design a Packet Sequence Generative Adversarial Network (PS-GAN), which can generate synthetic traffic with high fidelity of both per-packet fields and sequential relationships. We set up a real-world IoT testbed to evaluate IoTGemini. The experiment result shows that IoTGemini can achieve great effectiveness in device modeling, high fidelity of synthetic traffic generation, and remarkable usability to downstream tasks on different traffic datasets and downstream traffic analysis tasks. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email