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Energy Efficient Resource Migration Based Load Balance Mechanism for High Traffic Applications IoT

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Abstract

The biggest challenge for the network service providers is the day to day advancement of technologies which makes them difficult to manage the traditional networks. This day to day advancement has worked as a motivation to vendors for developing, deploying and migrating their services, installments of new hardware, trained people and up gradation of infrastructure which involves a huge cost and time. These frequent changes demand a new network architecture which supports future technologies and solves all these issues named as the proposal of networks defined by software. A large amount of data is being generated and through the internet, we interact with the world using our smart devices such as tablets, sensors, and smartphones using the concepts of Internet of Things (IoT). Along with continuous growth and development, there is a continuous heterogenous and ever-increasing demands of services. This leads to a cause of emerging challenge of load balancing of networks for meeting up with highly demanding requirements (e.g., high performance, lower latency, high throughput, and high availability) of IoT and 5G network applications. For meeting up highly increasing demands, various proposal of load balancing techniques comes forward, in which highly dedicated balancers of loads are being required for ever service in some of them, or for every new service, manual recognition of device is required. In the conventional network, on the basis of the local information in the network, load balancing is being established. However, the production of more optimized load balancers and a global view for the network is being contained by SDN controllers. So, these well-known techniques are quite time-consuming, expensive and impractical as well as service types aren’t being considered by various existing load balancing schemes. Through this paper, researchers focus on an SDN based load balancing (SBLB) service, in which minimized response time and maximized resource utilization are being considered for the user on cloud servers. The proposed scheme is being constituted by an application module which runs along with a SDN controller and server pools that connect to the controller through SDN enabled switches. The application module contains a dynamic load balancing module, a monitoring module and a service classification module. All messages are being handled in real time and host pool are being maintained by the Controller. The performance of the proposed scheme has been validated by experimental results. Through various experiments, results are being concluded that usage of SBLB results in significant decrease in average response and reply time.

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References

  1. Remote diagnostics informs wind turbine O&M, Siemens, The Magazine. (2015). [Online]. Available: https://www.siemens.com/customermagazine/en/home/energy/power-transmission-and-distribution/remotediagnostics-informs-wind-turbine-o-and-m.html.

  2. Cisco visual networking index. (2017). Global mobile data traffic forecast update, 2016–2021 White Paper. San Jose, CA, USA: Cisco.

    Google Scholar 

  3. Chandrasekaran, B., Tschaen, B. & Benson, T. (2016). Isolating and tolerating SDN application failures with Lego SDN. In Proceedings of the symposium SDN Res. (p. 7).

  4. HP SDN Dev Center App Store. Accessed: May 1 (2017). [Online]. Available: http://www8.hp.com/us/en/networking/sdn/devcenter-index.html

  5. Hock, D. et al. (2014). POCO-PLC: Enabling dynamic pareto-optimal resilient controller placement in SDN networks. In Proceedings of the IEEE conference on computer communication workshops (INFOCOM WKSHPS) (pp. 115–116).

  6. POCO-Toolset. Accessed: Oct. 18, 2017. [Online]. Available: http://www.comnet.informatik.uni-wuerzburg.de/forschung/projects/ next_generation_networks_projects/poco/

  7. Nakagawa, Y. et al. (2015). Dynamic virtual network configuration between containers using physical switch functions for NFV infrastructure. In IEEE conference network function virtualization software defined network.

  8. Reghu, S., Kumar, S. (2019). Development of Robust Infrastructure in Networking to Survive a Disaster. In 2019 4th international conference on information systems and computer networks (pp. 250–255, 9036244).

  9. Openstack Trio2o Project. (2017). [Online]. Available: https://wiki.openstack.org/wiki/Trio2o

  10. Openstack Tricircle Project. (2017). [Online]. Available: https://wiki.openstack.org/wiki/Tricircle

  11. Shenker, S. (2013). Stanford Seminar—Software-Defined Networking at the Crossroads. [Online]. Available: https://www.youtube.com/watch?v=WabdXYzCAOU

  12. Shahbaz, M. et al. (2016) PISCES: A programmable, protocol-independent software switch. In Proceedings of the conference ACM SIGCOMM conference (SIGCOMM), New York, NY, USA (pp. 525–538).

  13. Kumar, S., Ranjan, P., Ramaswami, R., & Tripathy, M. R. (2017). Energy efficient multichannel MAC protocol for high traffic applications in heterogeneous wireless sensor networks. Recent Advances in Electrical & Electronic Engineering, 10(3), 223–232.

    Google Scholar 

  14. Munoz, R. et al. (2017). IoT-aware multi-layer transport SDN and cloud architecture for traffic congestion avoidance through dynamic distribution of IoT analytics. In European conference on optical communicatio.

  15. Munoz, R., et al. (2015). Transport network orchestration for end-to-end multi-layer provisioning across heterogeneous SDN/OpenFlow and GMPLS/PCE control domains. Journal of Lightwave Technolgy, 33(8), 1540–1548.

    Article  Google Scholar 

  16. Raafat, H. M., et al. (2017). Fog intelligence for real-time IoT sensor data analytics. IEEE Access, 5, 24062–24069.

    Article  Google Scholar 

  17. Yang, S. (Aug. 2017). “IoT stream processing and analytics in the fog”,IEEE Commun. Mag., 55(8), 21–27.

    Google Scholar 

  18. Ta-Shma, P., Akbar, A., Gerson-Golan, G., Hadash, G., Carrez, F., & Moessner, K. An ingestion and analytics architecture for IoT applied to smart city use cases. In IEEE internet things journal. https://doi.org/10.1109/JIOT.2017.2722378.

  19. Puiu, D., Bischof, S., Serbanescu, B., Nechifor, S., Parreira, J., & Schreiner, H. (2017). A public transportation journey planner enabled by IoT data analytics. In proceedings of the 20th conference innovovation clouds, internet network.

  20. Munoz, R. et al. (20017). The ADRENALINE Testbed: An SDN/NFV Packet/optical transport network and edge/core cloud platform for endto-end 5G and IoT services. In European conference network commication, Oulu, Finland.

  21. Kumar, S., Ranjan, P., Ramaswami, R., & Tripathy, M. R. (2017). Resource efficient clustering and next hop knowledge based routing in multiple heterogeneous wireless sensor networks. International Journal of Grid and High Performance Computing (IJGHPC), 9(2), 1–20.

    Article  Google Scholar 

  22. Tempered Networks. (2016). The future of network virtualization and SDN controllers. SDxCentral, Market Rep. Accessed: Oct. 18,

  23. SevOne. (2016). [White Paper] State of SDN: 6 Reasons Why SDN is on the Rise. [Online]. Available: http://info.sevone.com/rs/505-CNP-948/images/SDN%20Whitepaper.pdf

  24. Kumar, S., Ranjan, P., Ramaswami, R., & Tripathy, M. R. (2015). Energy aware distributed protocol for heterogeneous wireless sensor network. International Journal of Control and Automation, 8(10), 421–430.

    Article  Google Scholar 

  25. OpenMUL. High Performanance SDN. Accessed: Jun. 2017. [Online]. Available: http://www.openmul.org/

  26. McKeown, N., et al. (Apr. 2008). ‘OpenFlow: Enabling innovation in campus networks.’ ACM SIGCOMM Comput. Commun. Rev., 38(2), 69–74.

    Article  Google Scholar 

  27. Wang, C., Hu, B., Chen, S., Li, D., & Liu, B. (2017). A switch migration-based decision-making scheme for balancing load in SDN. IEEE Access, vol.5, pp. 4537–4544.

  28. Zhu, R., Wang, H., Gao, Y., Yi, S., & Zhu, F. (2015). Energy saving and load balancing for SDN based on multi-objective particle swarm optimization. In Proceedings of the International Conference on Algorithms Architecture Parallel Progress (pp. 176–189).

  29. Zhang, J., Xi, K., Luo, M., & Chao, H. J. (2014). Load balancing for multiple traffic matrices using SDN hybrid routing. In Proceedings of the IEEE 15th International Conference on High Perform. Switching Routing (HPSR) (pp. 44–49).

  30. Han, T., & Ansari, N. (Apr. 2016). ‘A traffic load balancing framework for softwaredefined radio access networks powered by hybrid energy sources.’ IEEE/ACM Trans. Netw., 24(2), 1038–1051.

    Article  Google Scholar 

  31. Doria, A. et al. Forwarding and Control Element Separation (ForCES) Protocol Specification. [Online]. Available: http://sdn.ieee.org/images/files/pdf/Software_Defined_Infrastructure -IEEE-SDN-Initiative.pdf

  32. Song, H. (2013) Protocol-oblivious forwarding: Unleash the power of SDN through a future-proof forwarding plane. In Proceedings of the 2ndACMSIGCOMM Workshop Hot Topics Softw. Defined Netw., (pp. 127–132).

  33. Kumar, S., Ranjan, P., Ramaswami, R., & Tripathy, M. R., “An NS3 Implementation of physical layer based on 802.11 for utility maximization of WSN”, Proceedings - 2015 International Conference on Computational Intelligence and Communication Networks, CICN 2015 , 2016, pp. 79–84 , 7546060

  34. Shin, M.-K., Nam, K.-H., & Kim, H.-J. (2012). Software-defined networking (SDN): A reference architecture and open APIs. In Proceedings of the International Conference on ICT Convergence (ICTC) (pp. 360–361).

  35. ONF. (Jun. 2012). OpenFlow Switch Specification, Version 1.3.0. [Online]. Available: https://www.opennetworking.org/images/stories/ downloads/sdn-resources/onf-specifications/openflow/openflow-specv1.3.0.pdf

  36. Nolle, T. (2013). Centralized vs. Decentralized SDN Architecture: Which Works for You? [Online]. Available: http://searchsdn.techtarget. com/tip/Centralized-vsdecentralized-SDN-architecture-Which-worksfor-you

  37. Nunes, B. A. A., Mendonca, M., Nguyen, X.-N., Obraczka, K. & Turletti, T. (2014) A survey of software-defined networking: Past, present, and future of programmable networks. IEEE Commun. Surveys Tuts., vol. 16, no. 3, pp. 1617–1634, 3rd Quart., 2014.

  38. KimandN, H., & Feamster, . (2013). ‘ Improving network management with software defined networking.’ IEEE Communications Magazine, 51(2), 114–119.

    Article  Google Scholar 

  39. Lopes, F. A., Santos, M., Fidalgo, R., & Fernandes, S. (2016). A software engineering perspective on SDN programmability. IEEE Communication Surveys Tuts., 18(2), 1255–1272.

    Article  Google Scholar 

  40. Kumar, S., Ranjan, R., & Tripathy, M. R. (2015). A utility maximization approach to MAC layer channel access and forwarding. In Progress in electromagnetics research symposium, 2015, 2015-January (pp. 2363–2367).

  41. Kumar, S., Ranjan, P., Ramaswami, R., & Tripathy, M. R. (2015). EMEEDP: Enhanced multi-hop energy efficient distributed protocol for heterogeneous wireless sensor network. In Proceedings - 2015 5th international conference on communication systems and network technologies, CSNT 2015, 2015 (pp. 194–200, 7279908).

  42. M. Robuck. (Sep. 2015). ONF Debuts Northbound Interfaces for Intent-Based Networking. [Online]. Available: https://www.sdxcentral.com/articles/news/onf-debuts-northbound-interfaces-for-intent-basednetworking/2015/09/

  43. . Cox, J. H., Clark, R. J., & Owen, H. L. (2016). Security policy transition framework for software defined networks. In Proceedings of the 1st international workshop security NFV-SDN (SNS) (pp. 56–61).

  44. Dixit, A., Hao, F., Mukherjee, S., Lakshman, T. V., & Kompella, R. (Oct. 2013). ‘Towards an elastic distributed SDN controller.’ ACM SIGCOMM Computer Communication Review, 43(4), 7–12.

    Article  Google Scholar 

  45. Matsumoto, S., Hitz, S., & Perrig, A. (2014). Fleet: Defending SDNs from malicious administrators. In Proceedings of the 3rd workshop hot topics software defined network (pp. 103–108).

  46. Kumar, S., Ranjan, P., & Ramaswami, R. Energy optimization in distributed localized wireless sensor networks. In Proceedings of the international conference on issues and challanges in intelligent computing techniques (ICICT). https://doi.org/10.1109/ICICICT.2044.6781306.

  47. Bosshart, P., et al. ( 2013). ‘Forwarding metamorphosis: Fast programmable match-action processing in hardware for SDN. ACM SIGCOMM Computer Communication Review, 43(4), 99–110.

    Article  Google Scholar 

  48. Katta, N., Alipourfard, O., Rexford, J., & Walker, D. (2014). InfiniteCacheFlow in software-defined networks. In Third workshop on hot topics in software defined networks (pp. 175–180).

  49. Pfaff, B. et al. (2015). The design and implementation of open vSwitch. In Proceedings of the 12th USENIX symposium network system on design implement (NSDI) (pp. 117–130).

  50. Huang, X., Bian, S., Shao, Z., & Xu, H. (2017). Dynamic switch-controller association and control devolution for SDN systems. In Proceedings of the IEEE International Conference Communication (ICC) (pp. 1–6).

  51. Cheng, G., Chen, H., Hu, H., & Wang, Z. (2017). Toward a scalable SDN control mechanism via switch migration. China Communication, 14(1), 111–123.

    Article  Google Scholar 

  52. Boero, L., Cello, M., Garibotto, C., Marchese, M., & Mongelli, M. (2016). BeaQoS: Load balancing and deadline management of queues in an OpenFlowSDNswitch. Computer Network, 106, 161–170.

    Article  Google Scholar 

  53. Rangisetti, A. K., & Tamma, B. R. (2017). QoS Aware load balance in software defined LTE networks. Computer Communication, 97, 52–71.

    Article  Google Scholar 

  54. Lin, Y.-D., Wang, C. C., Lu, Y.-J., Lai, Y.-C., & Yang, H.-C. (2017). Two-tier dynamic load balancing in SDN-enabled Wi-Fi networks. Wireless Network, 23, 1–13.

    Google Scholar 

  55. Qazi, Z. A., Tu, C.-C., Chiang, L., Miao, R., Sekar, V., & Yu, M. (2013). ‘SIMPLE-fying Middlebox policy enforcement using SDN.’ ACM SIGCOMM Comput. Commun. Rev., 43(4), 27–38.

    Article  Google Scholar 

  56. Pakzad, F., Portmann, M., Tan, W. L., & Indulska, J. (Mar. 2016). ‘Efficient topology discovery in OpenFlow-based software defined networks.’ Computer Communications, 77, 52–61.

    Article  Google Scholar 

  57. Al-Najjar, A., Layeghy, S., & Portmann, M. (2016). Pushing SDN to the end host, network load balancing using OpenFlow. In Proceedings of the IEEE international conference on pervasive computer communication (pp. 1–6).

  58. Xu, H., Li, X.-Y., Huang, L., Deng, H., Huang, H., & andH. Wang, . (2017). Incremental deployment and throughput maximization routing for a hybrid SDN. IEEE/ACM Transactions on Networking, 25(3), 1861–1875.

    Article  Google Scholar 

  59. Cengiz, K., & Dag, T. (2016). Multi-hop low energy fixed clustering algorithm (M-LEFCA) for WSNs. In IEEE 3rd International Symposium on Telecommunication Technologies (ISTT) (pp. 31–34).

  60. Cengiz, K., & Dag, T. (2016). Improving energy-efficiency of WSNs through LEFCA. International Journal of Distributed Sensor Networks, 12, 8.

    Article  Google Scholar 

  61. Cengiz, K., & Dag, T. (2015). Low energy fixed clustering algorithm (LEFCA) for wireless sensor networks. In IEEE international conference on computing and network communications (CoCoNet) (pp. 79–84).

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Kumar, S., Cengiz, K., Vimal, S. et al. Energy Efficient Resource Migration Based Load Balance Mechanism for High Traffic Applications IoT. Wireless Pers Commun 127, 385–403 (2022). https://doi.org/10.1007/s11277-021-08269-7

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