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Cloud risk management with OWA-LSTM and fuzzy linguistic decision making
Hussain, Walayat Raza, Muhammad Raheel Jan, Mian Ahmad Merigó, Jose M. Gao, Honghao
Hussain, Walayat
Raza, Muhammad Raheel
Jan, Mian Ahmad
Merigó, Jose M.
Gao, Honghao
Abstract
In a cloud environment, the indemnity of service level agreement (SLA) violations has an adverse effect on the service provider. It leads to the penalty fee, credit amount, license extension, and reputation decline that could significantly impact future business outcomes. Existing approaches are unable to handle complex predictions that can accommodate the temporal influence of Quality of Service (QoS) data. Moreover, no method in a cloud environment considers all possible attitudinal behavior of the service provider to mitigate the risk of an actual violation. This article proposes an SLA violation risk mitigation model that uses ordered weighted average (OWA) in long short-term memory for complex QoS prediction. The OWA operator is weighted with a minimax disparity approach to manage the risk of SLA violation. The approach intelligently predicts deviation in custom prioritized QoS parameter and recommend exigency of mitigating action by considering all possible attitudinal behavior of the service provider. This article uses linguistic variables, fuzzy and interval numbers to handle imprecise information. The analysis results demonstrate the applicability and efficiency of the proposed approach to address complex risk mitigation actions.
Keywords
cloud computing, cloud risk management, fuzzy numbers (FNs), fuzzy-based decision making, intelligent risk management, linguistic decision making, predictive intelligence, quality of service (QoS)
Date
2022
Type
Journal article
Journal
IEEE Transactions on Fuzzy Systems
Book
Volume
30
Issue
11
Page Range
4657-4666
Article Number
ACU Department
Peter Faber Business School
Faculty of Law and Business
Faculty of Law and Business
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Relation URI
Source URL
Event URL
Open Access Status
License
All rights reserved
File Access
Controlled