SmartSlice: Dynamic, Self-optimization of Application’s QoS requests to 5G networks

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Publication Date: 12/6/2021

Event: The 5th International Symposium on 5G Emerging Technologies (5GET 2021)

Reference: pp. 1-7, 2021

Authors: Kunal Rao, NEC Laboratories America, Inc.; Murugan Sankaradas, NEC Laboratories America, Inc.; Vivek Aswal, NEC Laboratories America, Inc., Carnegie Mellon University; Srimat T. Chakradhar, NEC Laboratories America, Inc.

Abstract: Applications can tailor a network slice by specifying a variety of QoS attributes related to application-specific performance, function or operation. However, some QoS attributes like guaranteed bandwidth required by the application do vary over time. For example, network bandwidth needs of video streams from surveillance cameras can vary a lot depending on the environmental conditions and the content in the video streams. In this paper, we propose a novel, dynamic QoS attribute prediction technique that assists any application to make optimal resource reservation requests at all times. Standard forecasting using traditional cost functions like MAE, MSE, RMSE, MDA, etc. don’t work well because they do not take into account the direction (whether the forecasting of resources is more or less than needed), magnitude (by how much the forecast deviates, and in which direction), or frequency (how many times the forecast deviates from actual needs, and in which direction). The direction, magnitude and frequency have a direct impact on the application’s accuracy of insights, and the operational costs. We propose a new, parameterized cost function that takes into account all three of them, and guides the design of a new prediction technique. To the best of our knowledge, this is the first work that considers time-varying application requirements and dynamically adjusts slice QoS requests to 5G networks in order to ensure a balance between application’s accuracy and operational costs. In a real-world deployment of a surveillance video analytics application over 17 cameras, we show that our technique outperforms other traditional forecasting methods, and it saves 34% of network bandwidth (over a ~24 hour period) when compared to a static, one-time reservation.

Publication Link: https://ieeexplore.ieee.org/document/9732102

Additional Publication Link: https://arxiv.org/pdf/2111.09955.pdf