Uç Veri Merkezlerini Optimize Etmek İçin Toroidal K-ary Izgaralarını Uygulama
Yıl 2024,
Cilt: 27 Sayı: 5, 1743 - 1760, 02.10.2024
Pedro Juan Roig
,
Salvador Alcaraz
,
Katja Gılly
,
Cristina Bernad
,
Carlos Juiz
Öz
IoT dağıtımları katlanarak artıyor ve uç bilgi işlem sistemlerinde büyük bir artışa yol açıyor. Bu tür bir taleple başa çıkabilmek için veri merkezlerinin, uç bilgi işlemin az sayıda fiziksel sunucu ve belirli bir zamanda çalışan trafik akışlarına göre ölçekleme ve ölçeği kaldırma yeteneği gibi özel gereksinimlerine göre özelleştirilmeleri gerekir. Bu bağlamda yapay zeka, mevcut trafiği inceleyerek trafik hacminin artıp artmayacağına ya da geçmiş verileri ve ağın baz referans değerleri gibi diğer faktörleri irdeleyerek tahmin edebildiği için önemli bir rol oynar. Bu çalışmada, küçük veri merkezlerini organize etmek ve optimize etmek için toroidal k-ary ızgaralarına dayanan dinamik bir çerçeve ana hatlarıyla açıklanmakta ve IoT tarafından üretilen trafik akışlarının mevcut ve tahmin edilen kapasitesine göre artmalarına veya azalmalarına izin verilmektedir.
Kaynakça
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Applying Toroidal k-ary Grids for Optimizing Edge Data Centers
Yıl 2024,
Cilt: 27 Sayı: 5, 1743 - 1760, 02.10.2024
Pedro Juan Roig
,
Salvador Alcaraz
,
Katja Gılly
,
Cristina Bernad
,
Carlos Juiz
Öz
IoT deployments are growing exponentially, leading to a huge increase in edge computing facilities. In order to cope with such a demand, data centers need to get customized for the specific requirements of edge computing, such as a small number of physical servers and the ability to scale and unscale according to the traffic flows running at a given time. In this context, artificial intelligence plays a key part as it may anticipate when traffic throughput will increase or otherwise by scrutinizing current traffic whilst considering other factors like historical data and network baselines. In this paper, a dynamic framework is outlined based on toroidal k-ary grids so as to organize and optimize small data centers, allowing them to increase or decrease according to the current and predicted capacity of IoT-generated traffic flows.
Kaynakça
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- [39] Lawal K. and Rafsanjani N. “Trends, benefits, risks, and challenges of IoT implementation in residential and commercial buildings”, Energy and Built Environment, 3(3): 251–266, (2022).
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- [41] Huang C. and Shen S.H., “Enabling Service Cache in Edge Clouds”, ACM Transactions on Internet of Things, 2(3): 18, (2021).
- [42] Wang H. et al., “Error-Compensated Sparsification for Communication-Efficient Decentralized Training in Edge Environment”, IEEE Transactions on Parallel and Distributed Systems, 33(1): 14–25, (2022).
- [43] Girolami M. et al., “A mobility-based deployment strategy for edge data centers”, Journal of Parallel and Distributed Computing, 164: 133–141, (2022).
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- [46] Liu J. et al., “Exploring Query Processing on CPU-GPU Integrated Edge Device”, IEEE Transactions on Parallel and Distributed Systems, 33(12): 4057–4070, (2022).
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- [54] Karaahmetoglu E., Ersöz S., Türker A.K., Ates V. and Inal A.F., “Evaluation of Profession Predictions for Today and the Future with Machine Learning Methods: Emperical Evidence From Turkey”, Journal of Polytechnic, 26(1): 107–124, (2023).
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