Cloud Risk Management With OWA-LSTM and Fuzzy Linguistic Decision Making

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.

[1]  J. Merigó,et al.  Integrated AHP-IOWA, POWA Framework for Ideal Cloud Provider Selection and Optimum Resource Management , 2023, IEEE Transactions on Services Computing.

[2]  Muhammad Raheel Raza,et al.  Predictive intelligence using ANFIS‐induced OWAWA for complex stock market prediction , 2021, Int. J. Intell. Syst..

[3]  José M. Merigó,et al.  A new QoS prediction model using hybrid IOWA-ANFIS with fuzzy C-means, subtractive clustering and grid partitioning , 2021, Inf. Sci..

[4]  Wenan Tan,et al.  A novel service level agreement model using blockchain and smart contract for cloud manufacturing in industry 4.0 , 2021, Enterp. Inf. Syst..

[5]  Asaf Varol,et al.  Sentiment Analysis using Deep Learning in Cloud , 2021, 2021 9th International Symposium on Digital Forensics and Security (ISDFS).

[6]  Walayat Hussain,et al.  Trends in Using IoT with Machine Learning in Health Prediction System , 2021, Forecasting.

[7]  Yi Xie,et al.  An energy-efficient task migration scheme based on genetic algorithms for mobile applications in CloneCloud , 2020, J. Supercomput..

[8]  Seyedeh Zahra Hosseinifard,et al.  Disruption risk management in service-level agreements , 2020, Int. J. Prod. Res..

[9]  Shahid Raza,et al.  Security and trust preserving inter‐ and intra‐cloud VM migrations , 2020, Int. J. Netw. Manag..

[10]  Cheng-Feng Cheng,et al.  Knowledge management capabilities and organizational risk-taking for business model innovation in SMEs , 2020, Journal of Business Research.

[11]  Asma Alkalbani,et al.  Cloud service discovery method: A framework for automatic derivation of cloud marketplace and cloud intelligence to assist consumers in finding cloud services , 2021, Int. J. Commun. Syst..

[12]  Walayat Hussain,et al.  Managing SLA Violation in the cloud using Fuzzy re-SchdNeg Decision Model , 2020, 2020 15th IEEE Conference on Industrial Electronics and Applications (ICIEA).

[13]  P. Alli,et al.  Trust Based Approach and Risk Management for IT Systems in Cloud Service Selection , 2020, Wireless Personal Communications.

[14]  Abhishek Gupta,et al.  RETRACTED ARTICLE: SLA-aware load balancing using risk management framework in cloud , 2020, Journal of Ambient Intelligence and Humanized Computing.

[15]  Ning Li,et al.  Cloud reliability and efficiency improvement via failure risk based proactive actions , 2020, J. Syst. Softw..

[16]  Mohammad Masdari,et al.  Resource provisioning using workload clustering in cloud computing environment: a hybrid approach , 2020, Cluster Computing.

[17]  Xianghan Zheng,et al.  Self-adaptive resource allocation for cloud-based software services based on iterative QoS prediction model , 2020, Future Gener. Comput. Syst..

[18]  Seema Bawa,et al.  A comparative review of meta-heuristic approaches to optimize the SLA violation costs for dynamic execution of cloud services , 2020, Soft Comput..

[19]  Mohammed Rebbah,et al.  Fault tolerance for a scientific workflow system in a Cloud computing environment , 2020, International Journal of Computers and Applications.

[20]  Honghao Gao,et al.  Cloud Marginal Resource Allocation: A Decision Support Model , 2019, Mobile Networks and Applications.

[21]  Soo-Young Shin,et al.  ERMOCTAVE: A Risk Management Framework for IT Systems Which Adopt Cloud Computing , 2019, Future Internet.

[22]  Walayat Hussain,et al.  Analysing Cloud QoS Prediction Approaches and Its Control Parameters: Considering Overall Accuracy and Freshness of a Dataset , 2019, IEEE Access.

[23]  Walayat Hussain,et al.  A Centralised Cloud Services Repository (CCSR) Framework for Optimal Cloud Service Advertisement Discovery From Heterogenous Web Portals , 2019, IEEE Access.

[24]  Elizabeth Chang,et al.  Comparing time series with machine learning-based prediction approaches for violation management in cloud SLAs , 2018, Future Gener. Comput. Syst..

[25]  Omprakash Kaiwartya,et al.  Adaptive Energy-Aware Algorithms for Minimizing Energy Consumption and SLA Violation in Cloud Computing , 2018, IEEE Access.

[26]  Amir Masoud Rahmani,et al.  Reliability and high availability in cloud computing environments: a reference roadmap , 2018, Human-centric Computing and Information Sciences.

[27]  Javad Akbari Torkestani,et al.  A learning automata-based algorithm for energy and SLA efficient consolidation of virtual machines in cloud data centers , 2018, J. Parallel Distributed Comput..

[28]  Keqin Li,et al.  Minimizing SLA violation and power consumption in Cloud data centers using adaptive energy-aware algorithms , 2017, Future Gener. Comput. Syst..

[29]  S. Bannink.  Risk Management in Cloud Environments: Towards the Adoption of Continuous Auditing and Assurance With EU-SEC , 2018 .

[30]  Elizabeth Chang,et al.  Formulating and managing viable SLAs in cloud computing from a small to medium service provider's viewpoint: A state-of-the-art review , 2017, Inf. Syst..

[31]  R. Preston McAfee,et al.  Usage Patterns and the Economics of the Public Cloud , 2017, WWW.

[32]  Hassan Taheri,et al.  Novel fuzzy multi objective DVFS-aware consolidation heuristics for energy and SLA efficient resource management in cloud data centers , 2017, J. Netw. Comput. Appl..

[33]  Elizabeth Chang,et al.  Provider-Based Optimized Personalized Viable SLA (OPV-SLA) Framework to Prevent SLA Violation , 2016, Comput. J..

[34]  Farookh Khadeer Hussain,et al.  Risk Management Framework to Avoid SLA Violation in Cloud from a Provider's Perspective , 2016, 3PGCIC.

[35]  Farookh Khadeer Hussain,et al.  Comparative analysis of consumer profile-based methods to predict SLA violation , 2015, 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).

[36]  Mansoor Alam,et al.  Evaluation and design of highly reliable and highly utilized cloud computing systems , 2015, Journal of Cloud Computing.

[37]  J. Merigó,et al.  Linguistic group decision making with induced aggregation operators and probabilistic information , 2014, Appl. Soft Comput..

[38]  Kevin Lee,et al.  Empirical prediction models for adaptive resource provisioning in the cloud , 2012, Future Gener. Comput. Syst..

[39]  José M. Merigó,et al.  Fuzzy induced generalized aggregation operators and its application in multi-person decision making , 2011, Expert Syst. Appl..

[40]  Yao Sun,et al.  A file assignment strategy independent of workload characteristic assumptions , 2009, TOS.

[41]  Ying-Ming Wang,et al.  A minimax disparity approach for obtaining OWA operator weights , 2005, Inf. Sci..

[42]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[43]  Ronald R. Yager,et al.  On ordered weighted averaging aggregation operators in multicriteria decision-making , 1988 .

[44]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[45]  L. A. ZADEH,et al.  The concept of a linguistic variable and its application to approximate reasoning - I , 1975, Inf. Sci..