Autonomic Dominant Resource Fairness (A-DRF) in Cloud Computing

Authors: Fakhartousi, A., Meacham, S. and Phalp, K.

Journal: Proceedings - 2022 IEEE 46th Annual Computers, Software, and Applications Conference, COMPSAC 2022

Pages: 1626-1631

ISBN: 9781665488105

DOI: 10.1109/COMPSAC54236.2022.00258

Abstract:

In the world of information technology and the Internet, which has become a part of human life today and is constantly expanding, Attention to the users' requirements such as information security, fast processing, dynamic and instant access, and costs savings has become essential. The solution that is proposed for such problems today is a technology that is called cloud computing. Today, cloud computing is considered one of the most essential distributed tools for processing and storing data on the Internet. With the increasing using this tool, the need to schedule tasks to make the best use of resources and respond appropriately to requests has received much attention, and in this regard, many efforts have been made and are being made. To this purpose, various algorithms have been proposed to calculate resource allocation, each of which has tried to solve equitable distribution challenges while using maximum resources. One of these calculation methods is the DRF algorithm. Although it offers a better approach than previous algorithms, it faces challenges, especially with time-consuming resource allocation computing. These challenges make the use of DRF more complex than ever in the low number of requests with high resource capacity as well as the high number of simultaneous requests. This study tried to reduce the computations costs associated with the DRF algorithm for resource allocation by introducing a new approach to using this DRF algorithm to automate calculations by machine learning and artificial intelligence algorithms (Autonomic Dominant Resource Fairness or A-DRF).

http://eprints.bournemouth.ac.uk/37412/

Source: Scopus

Autonomic Dominant Resource Fairness (A-DRF) in Cloud Computing

Authors: Fakhartousi, A., Meacham, S. and Phalp, K.

Conference: COMPSAC 2022 Computers, Software & Applications in an Uncertain World

Dates: 27 June-1 July 2022

http://eprints.bournemouth.ac.uk/37412/

Source: Manual

Autonomic Dominant Resource Fairness (A-DRF) in Cloud Computing

Authors: Fakhartousi, A., Meacham, S. and Phalp, K.T.

Conference: COMPSAC 2022 Computers, Software & Applications in an Uncertain World

Abstract:

In the world of information technology and the Internet, which has become a part of human life today and is constantly expanding, Attention to the users' requirements such as information security, fast processing, dynamic and instant access, and costs savings has become essential. The solution that is proposed for such problems today is a technology that is called cloud computing. Today, cloud computing is considered one of the most essential distributed tools for processing and storing data on the Internet. With the increasing using this tool, the need to schedule tasks to make the best use of resources and respond appropriately to requests has received much attention, and in this regard, many efforts have been made and are being made.

To this purpose, various algorithms have been proposed to calculate resource allocation, each of which has tried to solve equitable distribution challenges while using maximum resources. One of these calculation methods is the DRF algorithm. Although it offers a better approach than previous algorithms, it faces challenges, especially with time-consuming resource allocation computing. These challenges make the use of DRF more complex than ever in the low number of requests with high resource capacity as well as the high number of simultaneous requests. This study tried to reduce the computations costs associated with the DRF algorithm for resource allocation by introducing a new approach to using this DRF algorithm to automate calculations by machine learning and artificial intelligence algorithms (Autonomic Dominant Resource Fairness or A-DRF).

http://eprints.bournemouth.ac.uk/37412/

Source: BURO EPrints