Automated and Massive-scale CCNx Experiments with Software-Defined SmartX Boxes

Transcription

1 Network Research Workshop Proceedings of the Asia-Pacific Advanced Network 2014 v. 38, p ISSN Automated and Massive-scale CCNx Experiments with Software-Defined SmartX Boxes Sun Park, Taeheum Na and JongWon Kim School of Information and Communications Gwangju Institute of Science and Technology, Gwangju, South Korea s: * Tel.: Abstract: Experimental research in Content-Centric Networking (CCN) is important to the evaluation of large-scale information centric multimedia services for nextgeneration network architecture. This paper presents a new experiment support tool over software-defined SmartX Boxes that enables the automated and massive-scale experimental validations of devised CCN model and service prototypes, which aims to fill-in the existing gap between real and simulated (including emulated) experiments. Keywords: CCN; Automated Experimentation; Testbed; Prototyping; OF@TEIN; OpenStack; VLC. 1. Introduction Content-Centric Networking (CCN) is being investigated by targeting a data-oriented architecture for Future Internet. For this reason, it is important to verify and understand the potential features and limitations of CCN model and service prototypes in realistic Internet environments [1]. However, most of CCN experimentation has been evaluated in either simulated or emulated environments [2, 3] and those kinds of CCN experiments are limited in mimicking the complex diversity of realistic Internet traffics [1]. In this paper, we present a new experiment support tool that enables the automated and massive-scale experimental validations of diverse CCN model and service prototypes. By leveraging the OF@TEIN software-defined SmartX Boxes (located in 7 international and 5 domestic sites) [4], the proposed tool simplifies the challenges of conducting massive-scale real- 29

2 network experiments. Note that the main SmartX Boxes in 5 domestic sites are connecting Intel ONP (open networking platform) servers (each with Xeon E5 20-core CPU, 96GB RAM, 1.6TB SSD) via 10Gbps links. Figure 1. OF@TEIN testbed with SmartX Boxes. 2. Automated and Massive-scale Experiment Support Tool Although the proposed tool is for generalized experimental validations, in this paper, we discuss it only for the evaluation of CCN models and services, as shown in Fig. 2. The developed experiment support tool is mainly driven by Chef DevOps automation scripts and other bash scripts and leverages the virtualized computing and networking capability built over the software-defined SmartX Boxes. Basically it takes advantage of OpenFlow-enabled SDN (Software-Defined Networking) and OpenStack-based CC (Cloud Computing) over the 12 interconnected SmartX Boxes. Currently we use CCNx version as the base software to evaluate the devised CCN models and services. Also, OVS (Open vswitch) OpenFlow virtual switches and OpenStackdriven VMs (virtual machines) for CCNx nodes. The CCNx VM images are created by placing CCNx software over Ubuntu Linux OS. The proposed support tool helps experimenter to describe the needed application attributes (for initialization and others) and inter-connection topology among CCNx VMs. Also, special attention has been made to automate the placement and execution of CCNx VMs over OF@TEIN SmartX Boxes. The support tool is capable of collecting the performance results from execution logging files. 30

3 Figure 2. The workflow of automated CCN experiment support tool. Now we exemplify how the proposed tool can conduct CCNx video experiments, which could be played out by VLC media player (version 2.0.8). For example, when broadcasting a 5-min video (672x288 WMV and stereo 44.1kHz WMA2) from a single source to 175 distributed consumers over 5 domestic sites (i.e., 2 Seoul, 1 Gwangju, 1 Jeju, and 1 Pohang), VLC can play broadcasted video over the Internet by using any of 7 topologies depicted in Fig. 3. Figure 3. Supported topologies for experiments. 31

4 3. Results Table 1 shows the time of VM launch and deployment with respect to 5 default VM flavors. Flavor refers to the logical hardware templates that define the sizes for RAM, disk, core counts, and so on. The max number of VMs represents the maximum number to create VMs in all 5 domestic sites. The VM launch time means how long it takes to finish the instantiations of all VMs, where you may mix-and-match the VM flavors with to-be-matched CCN models and services. 4. Conclusions Table 1. VM launch and deployment time comparison. Flavor (vcore/vmemory/vdisk) Max # of VM supported VM launch time Tiny (1/512MB/0GB) min Small (1/2GB/20GB) min 5 sec Medium (2/4GB/40GB) min Large (4/8GB/80GB) min 8 sec Xlarge (8/16GB/160GB) min 5 sec We are currently building an experimenter friendly tool to empirically evaluate the diverse CCN models and services. It means the automated and massively-scaled testbed that includes several configuration options (e.g., flavor size, application functions, topologies, etc.). In addition, the DevOps-based CLI of the proposed tool can simplify the task of conducting massive-scale CCN experiments Acknowledgements This work was supported by one of KOREN projects of National Information Society Agency ( ). Also, this work was supported in part by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. 2012R1A2A2A ). References 1. Q. Alina, C. Daniel, T. Thierry, and D. Walid, Experimentation with large scale ICN multimedia services on Internet made easy, IEEE COMSOC MMTC E-letter. vol. 8, no. 4, M. S. C. Carlos, E. R. Christian, and F. M. Mauricio, Mini-CCNx: Fast prototyping for Named Data Networking, in Proceedings of the SIGCOMM, Aug

5 3. A. Alexander, M. Ilya, and Z. Lixia, ndnsim: NDN simulator for NS-3, NDN Technical Report NDN-0005, J. Kim and others, An OpenFlow-enabled SDN testbed over international SmartX Rack sites, in Proceedings of the APAN Network Research Workshop, Aug by the authors; licensee Asia-Pacific Advanced Network. This article is an open-access article distributed under the terms and conditions of the Creative Commons Attribution license ( 33