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Can I read Resource Recommender for Cloud-Edge Engineering on EtoBox?
Resource Recommender for Cloud-Edge Engineering by Pasdar, Amirmohammad (author);Lee, Young Choon (author);Hassanzadeh, Tahereh (author);Almi’ani, Khaled (author) is a Computer Science article available to read on EtoBox.
What is Resource Recommender for Cloud-Edge Engineering about?
The interaction between artificial intelligence (AI), edge, and cloud is a fast-evolving realm in which pushing computation close to the data sources is increasingly adopted. Captured data may be processed locally (i.e., on the edge) or remotely in the clouds where abundant resources are available. While many emerging applications are processed in situ due primarily to their data intensiveness and short-latency requirement, the capacity of edge resources remains limited. As a result, the collaborative use of edge and cloud resources is of great practical importance. Such collaborative use should take into account data privacy, high latency and high bandwidth consumption, and the cost of cloud usage. In this paper, we address the problem of resource allocation for data processing jobs in the edge-cloud environment to optimize cost efficiency. To this end, we develop Cost Efficient Cloud Bursting Scheduler and Recommender (CECBS-R) as an AI-assisted resource allocation framework. In particular, CECBS-R incorporates machine learning techniques such as multi-layer perceptron (MLP) and long short-term memory (LSTM) neural networks. In addition to preserving privacy due to employing edge
Who reads Resource Recommender for Cloud-Edge Engineering?
It is typically read by researchers, students, and practitioners in Computer Science.
- Author
- Pasdar, Amirmohammad (author);Lee, Young Choon (author);Hassanzadeh, Tahereh (author);Almi’ani, Khaled (author)
- Publisher
- MDPI AG
- Published
- 2021
- Language
- EN
- Field
- Computer Science (Physical Sciences)