Decision making in cloud environments: an approach based on multiple-criteria decision analysis and stochastic models

Araújo, Julian; Maciel, Paulo; Andrade, Ermeson; Callou, Gustavo; Alves, Vandi; Cunha, Paulo Roberto Freire

doi:10.1186/s13677-018-0106-7

Cited by 33 publications

(12 citation statements)

References 36 publications

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“…Araujo et al [60] presented an MCDM approach for selecting cloud computing infrastructures, in terms of dependability and cost that best suits both company and customer needs. Nawaz et al [7] developed a cloud broker architecture for cloud service selection by finding a pattern of the changing priorities of User Preferences (UPs).…”

Section: Literature Reviewmentioning

confidence: 99%

An Integrated MCDM Approach for Cloud Service Selection Based on TOPSIS and BWM

Youssef

2020

IEEE Access

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Cloud Computing (CC) has become increasingly popular since it provides a wide variety of customized and reliable computational services. With the rapid growth of this technology, more and more IT services providers compete to offer high-quality and cost-effective cloud services that best fulfill their customers' needs. Given the vast diversity of these offers, the choice of the most appropriate Cloud Service Provider (CSP) became a dilemma that confuses most cloud customers. Many diverged criteria have to be considered to precisely evaluate services offered by several CSPs, some of these criteria cannot be quantified easily such as usability and security. The selection of the best CSP is thus a complex Multi-Criteria Decision Making (MCDM) problem that needs to be addressed efficiently. Previous studies of this problem employed MCDM methods that are either unfeasible when it is difficult or meaningless to quantify alternatives over criteria or computationally expensive and inconsistent when relative preferences of alternatives and criteria are used instead. In this paper, we propose a novel MCDM approach that is feasible, efficient and consistent using relative preferences of criteria and alternatives. The proposed approach incorporates Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and the Best Worst Method (BWM) to rank CSPs using evaluation criteria characterizing their services. The integrated approach has been tested and validated through a use-case scenario which demonstrates its effectiveness and correctness. We have also compared the proposed approach to the most commonly used MCDM approach, Analytical Hierarchical Process (AHP). The results clearly show that the proposed approach outperforms AHP in terms of computational complexity and consistency; hence, it is more efficient and reliable than AHP. INDEX TERMS Cloud computing (CC), cloud service providers (CSPs), multiple-criteria decision-making (MCDM), best worst method (BWM), technique for order of preference by similarity to ideal solution (TOPSIS), analytical hierarchical process (AHP).

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Section: Literature Reviewmentioning

confidence: 99%

An Integrated MCDM Approach for Cloud Service Selection Based on TOPSIS and BWM

Youssef

2020

IEEE Access

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“…Where single quality attribute selection is based on performance or cost-benefit analysis, multidimensional QoS selection takes into account user's requirements, performance attributes, as well as business profitability. Araujo et al (Araujo et al, 2018) uses a range of attributes primarily in the reliability domain to enable the comparison between complex cloud services with different redundancy and maintenance policies. The process involves first creation of a hierarchical cloud base model using Reliability Block Diagrams (RBDs) and Stochastic Petri Nets (SPNs).…”

Section: Analysis Of Academic Research Studiesmentioning

confidence: 99%

Modelling and Simulation of QoS-Aware Service Selection in Cloud Computing

Eisa

Younas

Basu

et al. 2020

Simulation Modelling Practice and Theory

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Cloud service selection process is significantly challenging and complicated as there are various QoS factors to consider when selecting cloud services. This paper proposes a new QoS-aware selection model systematically and succinctly representing QoS attributes which cloud consumers can easily use and understand when selecting cloud services. In order to ensure the credibility of the cloud service selection, the proposed model collects QoS data from different sources including cloud providers, users' reviews and cloud monitoring tools. It implements Multi-Criteria Decision Making models in order to rank services based on various QoS attributes. The proposed model is implemented as a simulation tool which is deployed on Amazon cloud platform. Using the simulation tool, the proposed model is rigorously evaluated through a number of experiments by taking into account data from widely used commercial cloud service providers. The experimental results show that the proposed model ranks and selects cloud services according to the QoS requirements of cloud service consumers. Unlike existing approaches the proposed model takes into account multi-level QoS attributes and data from real cloud providers when ranking cloud services.

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“…The efficient and accurate assessment of cloud-based infrastructure is essential for guaranteeing both business continuity and uninterrupted services for computing jobs [21]. However, efficient resource management of the cloud infrastructures is challenging [22].…”

Section: Literature Reviewmentioning

confidence: 99%

An optimization approach to capacity evaluation and investment decision of hybrid cloud: a corporate customer’s perspective

Lee

2019

J Cloud Comp

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While the rapid growth of cloud computing is driven by the surge of big data, the Internet of Things, and social media applications, an evaluation and investment decision for cloud computing has been challenging for corporate managers due to a lack of proper decision models. This paper attempts to identify critical variables for making a cloud capacity decision from a corporate customer's perspective and develops a base mathematical model to aid in a hybrid cloud investment decision under probabilistic computing demands. The identification of the critical variables provides a means by which a corporate customer can effectively evaluate various cloud capacity investment opportunities. Critical variables included in this model are an actual computing demand, the amount of private cloud capacity purchased, the purchase cost of the private cloud capacity, the price of the public cloud, and the default downtime loss/penalty cost. Extending the base model developed, this paper also takes into consideration the interoperability cost incurred in cloud bursting to the public cloud and derives the optimal investment. The interoperable cloud systems require time and investment by the users and/or cloud providers and there exists a diminishing return on the investment. Hence, the relationship between the interoperable cloud investment and return on investment is also investigated.

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Decision making in cloud environments: an approach based on multiple-criteria decision analysis and stochastic models

Cited by 33 publications

References 36 publications

An Integrated MCDM Approach for Cloud Service Selection Based on TOPSIS and BWM

An Integrated MCDM Approach for Cloud Service Selection Based on TOPSIS and BWM

Modelling and Simulation of QoS-Aware Service Selection in Cloud Computing

An optimization approach to capacity evaluation and investment decision of hybrid cloud: a corporate customer’s perspective

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