ارائه یک مکانیزم تصمیمگیری چندمعیاره برای برونسپاری ترافیک شبکه سلولی به شبکه مکمل
محورهای موضوعی : مهندسی برق و کامپیوترمجید فلاح خوشبخت 1 , صالح یوسفی 2 , بابک قالبساز جدّی 3
1 - دانشگاه ارومیه
2 - دانشگاه ارومیه
3 - دانشگاه ارومیه
کلید واژه: برونسپاری داده شبکه مکمل تصمیمگیری چندمعیاره مدلهای پیشبینی الگوی اتصال,
چکیده مقاله :
به دلیل گسترش روزافزون استفاده از گوشیهای هوشمند، ترافیک داده شبکه سلولی به صورت انفجاری افزایش یافته و باعث ازدحام در شبکههای سلولی شده است. برونسپاری داده به یک شبکه مکمل مانند WiFi به عنوان یک راه حل منطقی و مقرون به صرفه برای مقابله با ازدحام شبکه سلولی، مطرح شده است. در این مقاله، یک مکانیزم برونسپاری داده با نام MCO ارائه شده که با استفاده از روش تصمیمگیری چندمعیاره TOPSIS و با بهرهگیری از یک مدل پیشبینی الگوی اتصال به شبکه WiFi، مناسبترین روش را از میان سه روش 1) تبادل داده از طریق خود شبکه سلولی، 2) برونسپاری با تحمل تأخیر (DTO) به شبکه مکمل و 3) برونسپاری با کمک گرههای واسط (PAO) انتخاب میکند. معیارهای استفادهشده در تصمیمگیری شامل درصد ترافیک قابل برونسپاری (از کل درخواست کاربر)، هزینه تبادل داده اپراتور سلولی و کاربر، پهنای باند تبادل داده کاربر (در دو شبکه سلولی و مکمل) و مصرف انرژی کاربر میباشند. به منظور ارزیابی روش MCO سناریوهای متعددی از نقطه نظر ویژگیهای کاربران و ترافیک آنها، نرخ پوشش شبکه مکمل و نسبت هزینه تبادل داده شبکه سلولی به شبکه مکمل، شبیهسازی شدهاند. نتایج حاصل از این شبیهسازیها نشان میدهد مکانیزم MCO قادر به در نظر گرفتن ترجیحات اپراتور سلولی و کاربرانش بوده و در نتیجه از لحاظ ایجاد تعادل بار در شبکه، کاهش هزینههای اپراتور سلولی و کاهش مصرف انرژی کاربران، نسبت به روشهای پیشنهادی پیشین عملکرد بهتری دارد.
Due to proliferation of smart phones, data traffic in cellular networks has been significantly increasing, which has resulted in congestions in cellular networks. Data offloading to a complementary network such as Wi-Fi has been identified as a rational and cost-effective solution to these congestions. In this paper, a multi-criteria offloading (MCO) mechanism is proposed to select the best transfer mode among: cellular delivery, delay-tolerant offloading (DTO) to a complementary network, and peer-assisted offloading (PAO). The proposed MCO mechanism utilizes TOPSIS multi-criteria decision analysis method and a prediction model for the Wi-Fi connection pattern. The decision criteria include: the fraction of total users’ request satisfied by offloading, data transfer costs of cellular operator to users, data transfer bandwidth of users in both cellular and complementary networks, and total users’ power consumption. To evaluate the proposed mechanism various scenarios have been simulated, and the results show that the MCO mechanism can successfully take into account the preferences of the cellular operator and its users. Through simulations, the MCO mechanism demonstrated superior performance in comparison with other proposed solutions in the literature in terms of balancing the load on the network, reducing the cost of the cellular operator, and reducing energy consumption of the users.
[1] Cisco Visual Networking Index, Global Mobile Data Traffic Forecast Update, 2016-2021, White Paper, 2017..
[2] K. Lee, J. Lee, Y. Yi, I. Rhee, and S. Chong, "Mobile data offloading: how much can WiFi deliver?," IEEE Trans. Netw., vol. 21, no. 2, pp. 536-551, Apr. 2013.
[3] J. Roh, Y. Ji, Y. G. Lee, and T. Ahn, "Femtocell traffic offload scheme for core networks," in Proc. 4th IFIP Int. Conf. on New Technologies, Mobility and Security, NTMS'11, 5 pp., Paris, France, 7-10 Feb. 2011.
[4] S. M. S. Nirjon, A. Nicoara, C. H. Hsu, J. Singh, and J. Stankovi, "MultiNets: policy oriented real-time switching of wireless interfaces on mobile devices," in Proc. IEEE 18th Real-Time and Embedded Technology and Applications Symp., RTAS'12, pp. 251-260, Beijing, China, 16-19 Apr. 2012.
[5] T. Pering, Y. Agarwal, R. Gupta, and R. Want, "CoolSpots: reducing the power consumption of wireless mobile devices with multiple radio interfaces," in Proc. 4th Int. Conf. on Mobile Systems, Applications, and Services, MobiSys'06, pp. 220-232, Uppsala, Sweden, 19-22 Jun. 2006.
[6] S. Liu and A. Striegel, "Casting doubts on the viability of WiFi offloading," in Proc. ACM SIGCOMM Workshop on Cellular Networks, pp. 25-30, Helsinki, Finland, 13-13 Aug. 2012.
[7] F. Rebecchi, et al., "Data offloading techniques in cellular networks: a survey," IEEE Communications Surveys & Tutorials, vol. 17, no. 2, pp. 580-603, Secondquarter 2015.
[8] M. H. Cheung and J. Huang, "Optimal delayed WiFi offloading," in Proc. 11th Int. Symp. on Modeling & Optimization in Mobile, Ad Hoc & Wireless Networks, WiOpt'13, pp. 564-571, May 2013.
[9] F. Mehmeti and T. Spyropoulos, "Performance analysis of mobile data offloading in heterogeneous networks," IEEE Trans. on Mobile Computing, vol. 16, no. 2, pp. 482-497, Feb. 2017.
[10] M. Amani, A. Aijaz, N. Uddin, and H. Aghvami, "On mobile data offloading policies in heterogeneous wireless networks," in Proc. IEEE 77th Vehicular Technology Conf., 5 pp., Dresden, Germany, 2-5 Jun. 2013.
[11] M. R. Ra, et al., "Energy-delay tradeoffs in smartphone applications," in Proc. of the 8th Int. Conf. on Mobile Systems, Applications, and Services, pp. 255-270, San Francisco, CA, USA, 15-18 Jun. 2010.
[12] V. F. Mota, D. F. Macedo, Y. Ghamri-Doudanez, and J. M. S. Nogueira, "Managing the decision-making process for opportunistic mobile data offloading," in Proc. IEEE Network Operations and Management Symp., NOMS'14, 8 pp., Krakow, Poland, 5-9 May 2014.
[13] S. Dimatteo, P. Hui, B. Han, and V. Li, "Cellular traffic offloading through wi-fi Networks," in Proc. IEEE 8th Int. Conf. on Mobile Adhoc and Sensor Systems, MASS'11, pp. 192-201, Valencia, Spain, 17-22 Oct. 2011.
[14] I. Komnios, F. Tsapeli, and S. Gorinsky, "Cost-effective multi-mode offloading with peer-assisted communications," Ad Hoc Networks, vol. 25, pt. B, pp. 370-382, Feb. 2015.
[15] L. Song, U. Deshpande, U. C. Kozat, D. Kotz, and R. Jain, "Predictability of WLAN mobility and its effects on bandwidth provisioning," in Proc. Int. Conf. on Computer Communications. INFOCOM'06, 13 pp. Barcelona, Spain, 23-29 Apr. 2006.
[16] S. Scellato, M. Musolesi, C. Mascolo, V. Latora, and A. T. Campbell, "NextPlace: a spatio-temporal prediction framework for pervasive systems," in Proc. Int. Conf. on Pervasive Computing, pp. 159-162, San Francisco, CA, USA, 12-15 Jun. 2011.
[17] D. Ashbrook and T. Starner, "Using GPS to learn significant locations and predict movement across multiple users," J. Personal and Ubiquitous Computing, vol. 7, no. 5, pp. 275-286, Oct2003.
[18] A. J. Nicholson and B. D. Noble, "BreadCrumbs: forecasting mobile connectivity," in Proc. 14th Annual Int. Conf. on Mobile Computing and Networking, MobiCom'08, pp. 46-57, San Francisco, CA, USA, 14-19 Sept.. 2008.
[19] A. Ishizaka and P. Nemery, Multi-Criteria Decision Analysis: Methods and Software, Wiley, 2013.
[20] I. Rhee, et al., "On the Levy-walk nature of human hobility," IEEE/ACM Transactions on Networking, vol. 19, no. 3, pp. 630-643, Jun. 2011.
[21] J. Ott, E. Hyytia, P. Lassila, T. Vaegs, and J.Kangasharju., "Floating content: information sharing in urban areas," in Proc. IEEE Int. Conf. on Pervasive Computing and Communications, PerCom'11, pp. 136-146,, Seattle, WA, USA, 21-25 Mar. 2011.
[22] A. Rahmati and L. Zhong, "Context-for-wireless: context-sensitive energy-efficient wireless data transfer," in Proc. 5th Int. Conf. on Mobile Systems, Applications, and Services, MobiSys'07, pp. 165-178, San Juan, Puerto Rico, USA, 11-14 Jun. 2007.