{"title":"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","authors":"Evren Tuna , Asude Baykal , Alkan Soysal","doi":"10.1016/j.adhoc.2024.103594","DOIUrl":null,"url":null,"abstract":"<div><p>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.</p><p>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.</p></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":null,"pages":null},"PeriodicalIF":4.4000,"publicationDate":"2024-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Multivariate and multistep mobile traffic prediction with SLA constraints: A comparative study\",\"authors\":\"Evren Tuna , Asude Baykal , Alkan Soysal\",\"doi\":\"10.1016/j.adhoc.2024.103594\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>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.</p><p>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.</p></div>\",\"PeriodicalId\":55555,\"journal\":{\"name\":\"Ad Hoc Networks\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.4000,\"publicationDate\":\"2024-07-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Ad Hoc Networks\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1570870524002051\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ad Hoc Networks","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1570870524002051","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
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.
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.