Smart City Gnosys
Smart city article details
| Title | Replicating File Segments Between Multi-Cloud Nodes In A Smart City: A Machine Learning Approach |
|---|---|
| ID_Doc | 45085 |
| Authors | Mostafa N.; Kotb Y.; Al-Arnaout Z.; Alabed S.; Shdefat A.Y. |
| Year | 2023 |
| Published | Sensors, 23, 10 |
| DOI | http://dx.doi.org/10.3390/s23104639 |
| Abstract | The design and management of smart cities and the IoT is a multidimensional problem. One of those dimensions is cloud and edge computing management. Due to the complexity of the problem, resource sharing is one of the vital and major components that when enhanced, the performance of the whole system is enhanced. Research in data access and storage in multi-clouds and edge servers can broadly be classified to data centers and computational centers. The main aim of data centers is to provide services for accessing, sharing and modifying large databases. On the other hand, the aim of computational centers is to provide services for sharing resources. Present and future distributed applications need to deal with very large multi-petabyte datasets and increasing numbers of associated users and resources. The emergence of IoT-based, multi-cloud systems as a potential solution for large computational and data management problems has initiated significant research activity in the area. Due to the considerable increase in data production and data sharing within scientific communities, the need for improvements in data access and data availability cannot be overlooked. It can be argued that the current approaches of large dataset management do not solve all problems associated with big data and large datasets. The heterogeneity and veracity of big data require careful management. One of the issues for managing big data in a multi-cloud system is the scalability and expendability of the system under consideration. Data replication ensures server load balancing, data availability and improved data access time. The proposed model minimises the cost of data services through minimising a cost function that takes storage cost, host access cost and communication cost into consideration. The relative weights between different components is learned through history and it is different from a cloud to another. The model ensures that data are replicated in a way that increases availability while at the same time decreasing the overall cost of data storage and access time. Using the proposed model avoids the overheads of the traditional full replication techniques. The proposed model is mathematically proven to be sound and valid. © 2023 by the authors. |
| Author Keywords | data replication; edge servers; formal methods; internet of things (IoT); machine learning; multi-cloud; partial replication; regression; system soundness |
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