Experimental Evaluation on Priority-Aware Guaranteed Resource Allocation for Resource Pool Based Reconfigurable Hardware
Pages 298 - 307
Abstract
This paper proposes a priority-aware guaranteed hardware resource allocation in virtual packet optical nodes (VPONs) and describes experimental evidence of service provisioning with the proposed method on testbed. A network based on the VPON brings solution of service diversification and traffic explosion because it provides multiple services by hardware level separated virtual networks such as IP/Ethernet/ Multi-Protocol Label Switching. The VPON has reconfigurable hardware which is dedicatedly allocated for each service and service-specific logical functions are implemented respectively. However, a previous hardware resource allocation method is not efficient. In previous work, the hardware resources once allocated to a service are not reallocated until the service terminated whenever the hardware is not optimally used. In recent years, hardware reconfiguration time has become shorter and it has enabled to apply for service demand change by releasing hardware from services with low demand and reallocating it to other services with high demand. Thus, this paper proposes preemptive dynamic hardware resource reallocation algorithm for VPON and a priority-aware guaranteed hardware resource reallocation method to realize efficient resource usage and high service capacity. Results of the computer simulation show improving service capability. Furthermore, to demonstrate the feasibility of service provisioning with the proposed method, emulators of the virtual packet optical node were constructed and performed resource reallocation. Results of the experiment show that the virtual packet optical node can configure function chains for providing services based on calculation results of the proposed resource reallocation method.
References
[1]
M. Li, “Review of advanced CMOS technology for post-Moore era,” Sci. China Phys., Mech. Astron., vol. 55, no. 12, pp. 2316–2325, Nov. 2012.
[2]
S. Okamoto, N. Yamanaka, C. Hara, N. Sumita, and T. Muranaka, “Reconfigurable communication processor for future smart and connected community network,” in Proc. Int. Symp. Adv. Comput. Inf. Technol., Aug. 2019.
[3]
N. Yamanaka, S. Okamoto, K. Yarita, K. Sugai, and T. Muranaka, “Optically interconnected resource pool architecture for future backbone network with service scalability,” in Proc. Int. Conf. Comput., Netw. Commun., Mar. 2018.
[4]
S. Okamoto, J. Matsumoto, T. Sato, and N. Yamanaka, “Proposal of the photonic programmable node architecture using virtual reconfigurable communication processors,” IEICE Tech. Rep., vol. 116, no. 205, pp. 59–64, Sep. 2016.
[5]
N. Sumita, M. Murakami, T. Kurimoto, S. Okamoto, and N. Yamanaka, “Proposal of the resource addressing for virtual reconfigurable communication processors,” in Proc. IEICE General Conf., Mar. 2020, p. 12.
[6]
A. N. Corp, “Reconfigurable communication processor over lambda project,” in Proc. 15th Int. Conf. IP+Opt. Netw., May 2019. [Online]. Available: https://www.pilab.jp/ipop2019/exhibition/panel/iPOP2019_ALAXALA_panel.pdf
[7]
C. Hara, K. Yarita, S. Okamoto, and N. Yamanaka, “Biological attractor selection and SDN control interworking in the virtual packet optical node network control,” in Proc. Opt. Fiber Commun. Conf. Exhibit. (OFC), Mar. 2019, pp. 1–3.
[8]
C. Hara, M. Murakami, S. Okamoto, and N. Yamanaka, “Experimental deployment of dynamic resource allocation using biological attractor selection in virtual packet optical node,” in Proc. 24th OptoElectron. Commun. Conf. (OECC) Int. Conf. Photon. Switching Comput. (PSC), Jul. 2019, pp. 1–3.
[9]
K. Leibnitz, N. Wakamiya, and M. Murata, “Biologically inspired self-adaptive multi-path routing in overlay networks,” Commun. ACM, vol. 49, no. 3, pp. 62–67, Mar. 2006.
[10]
H. Wu, X. Wen, Z. Lu, and Q. Pan, “Multi-access selection with attractor selection algorithm in heterogeneous network,” in Proc. IEEE/CIC Int. Conf. Commun. China (ICCC), Nov. 2015, pp. 1–5. 10.1109/ICCChina.2015.7448758.
[11]
W. Gong, X. Yang, M. Zhang, and K. Long, “A bio-inspired OSPF path selection scheme based on an adaptive attractor selection model,” Int. J. Commun. Syst., vol. 30, no. 3, p. e2963, Feb. 2017. 10.1002/dac.2963.
[12]
A. Kashiwagi, I. Urabe, K. Kaneko, and T. Yomo, “Adaptive response of a gene network to environmental changes by fitness-induced attractor selection,” PLoS ONE, vol. 1, no. 1, p. e49, Dec. 2006. 10.1371/journal.pone.0000049.
[13]
N. Wakamiya and M. Murata, “Autonomous and adaptive wireless networking with bio-inspired algorithms,” in Proc. 10th Int. Symp. Auto. Decentralized Syst., Mar. 2011, pp. 597–602. 10.1109/ISADS.2011.85.
[14]
Xilinx, Inc. Spartan-6 FPGA Configuration User Guide. [Online]. Available: https://docs.xilinx.com/v/u/en-U.S./ug380
[15]
Y. Liet al., “A scalable priority-aware approach to managing data center server power,” in Proc. IEEE Int. Symp. High Perform. Comput. Archit. (HPCA), Washington, DC, USA, Mar. 2019, pp. 701–714. 10.1109/HPCA.2019.00067.
[16]
J. Son and R. Buyya, “Priority-aware VM allocation and network bandwidth provisioning in software-defined networking (SDN)-enabled clouds,” IEEE Trans. Sustain. Comput., vol. 4, no. 1, pp. 17–28, Jan. 2019. 10.1109/TSUSC.2018.2842074.
[17]
J. Sócrates-Dantas, R. M. Silveira, D. Careglio, J. R. Amazonas, J. Solé-Pareta, and W. V. Ruggiero, “Novel differentiated service methodology based on constrained allocation of resources for transparent WDM backbone networks,” in Proc. Brazilian Symp. Comput. Netw. Distrib. Syst., May 2014, pp. 420–427. 10.1109/SBRC.2014.50.
[18]
A. Mohamad and H. S. Hassanein, “PSVShare: A priority-based SFC placement with VNF sharing,” in Proc. IEEE Conf. Netw. Function Virtualization Softw. Defined Netw. (NFV-SDN), Nov. 2020, pp. 25–30. 10.1109/NFV-SDN50289.2020.9289837.
[19]
N. Siasi and A. Jaesim, “Priority-aware SFC provisioning in fog computing,” in Proc. IEEE 17th Annu. Consum. Commun. Netw. Conf. (CCNC), Las Vegas, NV, USA, Jan. 2020, pp. 1–6. 10.1109/CCNC46108.2020.9045275.
[20]
Segment Routing over IPv6 (SRv6) Network Programming, document RFC8986, Feb. 2021.
[21]
K. Christodoulopoulos, K. Manousakis, and E. Varvarigos, “Offline routing and wavelength assignment in transparent WDM networks,” IEEE/ACM Trans. Netw., vol. 18, no. 5, pp. 1557–1570, Oct. 2010. 10.1109/TNET.2010.2044585.
[22]
B. Wen, R. Shenai, and K. Sivalingam, “Routing, wavelength and time-slot-assignment algorithms for wavelength-routed optical WDM/TDM networks,” J. Lightw. Technol., vol. 23, no. 9, pp. 2598–2609, Sep. 15, 2005. 10.1109/JLT.2005.854039.
[23]
M. Xia, L. Song, M. Batayneh, and B. Mukherjee, “Event-triggered reprovisioning with resource preemption in WDM mesh networks: A traffic engineering approach,” in Proc. Conf. Opt. Fiber Commun./Nat. Fiber Optic Eng. Conf., Feb. 2008, pp. 1–3. 10.1109/OFC.2008.4528565.
[24]
X. Wei, L. Li, H. Yu, and D. Xu, “Dynamic preemptive multi-class routing scheme under dynamic traffic in survivable WDM mesh networks,” in Proc. 3rd Int. Conf. High Perform. Comput. Commun., 2007, pp. 744–754.
[25]
P. Wette and H. Karl, “Incorporating feedback from application layer into routing and wavelength assignment algorithms,” in Proc. IEEE Conf. Comput. Commun. Workshops (INFOCOM WKSHPS), Apr. 2013, pp. 51–52. 10.1109/INFCOMW.2013.6970733.
[26]
H. Moens and F. D. Turck, “VNF-P: A model for efficient placement of virtualized network functions,” in Proc. 10th Int. Conf. Netw. Service Manage. (CNSM) Workshop, Nov. 2014, pp. 418–423. 10.1109/CNSM.2014.7014205.
[27]
JGN NICT. JGN High Speed R&D Network Testned. Accessed: Oct. 13, 2020. [Online]. Available: https://testbed.nict.go.jp/jgn/english/info/index.html
[28]
T. Tanaka, S. Kuwabara, T. Inui, Y. Yamada, and S. Kobayashi, “A high-availability scheme for bandwidth-variable multi-links with flex ethenet,” in Proc. 13th Int. Conf. IP+Opt. Netw., vol. 3, May 2017, pp. 1–15.
[29]
J. S. Trowbridge, “Flex ethernet implementation agreement 1.0,” Optical Internetworking Forum (OIF), Brandon, U.K., Tech. Rep., Mar. 2016.
Index Terms
- Experimental Evaluation on Priority-Aware Guaranteed Resource Allocation for Resource Pool Based Reconfigurable Hardware
Index terms have been assigned to the content through auto-classification.
Recommendations
Resource reconstruction algorithms for on-demand allocation in virtual computing resource pool
Resource reconstruction algorithms are studied in this paper to solve the problem of resource on-demand allocation and improve the efficiency of resource utilization in virtual computing resource pool. Based on the idea of resource virtualization and ...
Hardware resource allocation for hardware/software partitioning in the LYCOS system
DATE '98: Proceedings of the conference on Design, automation and test in EuropeThis paper presents a novel hardware resource allocation technique for hardware/software partitioning. It allocates hardware resources to the hardware data-path using information such as data-dependencies between operations in the application, and ...
Hardware Resource Virtualization for Dynamically Partially Reconfigurable Systems
The dynamic partial reconfiguration technology enables an embedded system to adapt its hardware functionalities at run-time to changing environment conditions. However, reconfigurable hardware functions are still managed as conventional hardware devices,...
Comments
Information & Contributors
Information
Published In
1558-2566 © 2023 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See https://www.ieee.org/publications/rights/index.html for more information.
Publisher
IEEE Press
Publication History
Published: 26 June 2023
Published in TON Volume 32, Issue 1
Qualifiers
- Research-article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 13Total Downloads
- Downloads (Last 12 months)13
- Downloads (Last 6 weeks)0
Reflects downloads up to 08 Feb 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in