具有 SLA 约束条件的多变量和多步骤移动流量预测:比较研究

IF 4.4 3区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Evren Tuna , Asude Baykal , Alkan Soysal
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引用次数: 0

摘要

本文提出了一种预测移动网络下行链路流量的新方法,旨在最大限度地减少超额配置,同时满足指定的服务级别协议(SLA)违规率。我们介绍了一种多变量、多步骤预测方法,并比较了四种机器学习(ML)架构:长短期记忆(LSTM)、卷积神经网络(CNN)、变换器和轻梯度提升机(LightGBM)。我们的模型最多可提前 24 步进行预测,并在单步和多步条件下进行了评估。此外,我们还提出了参数损失函数,以遵守 SLA 违反率约束。我们发现,当 LSTM 与我们定制的多变量特征集搭配使用时,它在提前 4 小时以内的短期预测方面优于变压器架构。在这些短期预测中,我们证明了基于领域知识的方法(如我们的自定义特征集与 LSTM 等较简单模型的组合)超越了变压器等较复杂的模型。不过,在长期预测(提前 8 到 24 小时)方面,变换器的表现优于所有其他模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multivariate and multistep mobile traffic prediction with SLA constraints: A comparative study

This paper proposes a new method for predicting downlink traffic volume in mobile networks, aiming to minimize overprovisioning while meeting specified service-level agreement (SLA) violation rates. We introduce a multivariate and multi-step prediction approach and compare four machine learning (ML) architectures: long short-term memory (LSTM), convolutional neural network (CNN), transformer, and light gradient-boosting machine (LightGBM). Our models predict up to 24 steps ahead and are evaluated under both single-step and multi-step conditions. Additionally, we propose parametric loss functions to adhere to SLA violation rate constraints.

Our results emphasize the importance of using parametric loss functions to meet SLA constraints. We discovered that LSTM when paired with our custom multivariate feature sets, outperforms the transformer architecture in short-term forecasting up to 4 h ahead. For these short-term predictions, we demonstrate that methods based on domain knowledge, like our custom feature sets combined with simpler models such as LSTM, surpass more complex models like transformers. However, for long-term forecasting (8 to 24 h ahead), transformers outperform all other models.

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来源期刊
Ad Hoc Networks
Ad Hoc Networks 工程技术-电信学
CiteScore
10.20
自引率
4.20%
发文量
131
审稿时长
4.8 months
期刊介绍: The Ad Hoc Networks is an international and archival journal providing a publication vehicle for complete coverage of all topics of interest to those involved in ad hoc and sensor networking areas. The Ad Hoc Networks considers original, high quality and unpublished contributions addressing all aspects of ad hoc and sensor networks. Specific areas of interest include, but are not limited to: Mobile and Wireless Ad Hoc Networks Sensor Networks Wireless Local and Personal Area Networks Home Networks Ad Hoc Networks of Autonomous Intelligent Systems Novel Architectures for Ad Hoc and Sensor Networks Self-organizing Network Architectures and Protocols Transport Layer Protocols Routing protocols (unicast, multicast, geocast, etc.) Media Access Control Techniques Error Control Schemes Power-Aware, Low-Power and Energy-Efficient Designs Synchronization and Scheduling Issues Mobility Management Mobility-Tolerant Communication Protocols Location Tracking and Location-based Services Resource and Information Management Security and Fault-Tolerance Issues Hardware and Software Platforms, Systems, and Testbeds Experimental and Prototype Results Quality-of-Service Issues Cross-Layer Interactions Scalability Issues Performance Analysis and Simulation of Protocols.
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