Cloud Service Selection and Evaluation Techniques

Cloud service selection and evaluation encompass a suite of systematic methods designed to guide organisations and developers in choosing the most appropriate cloud offerings from a proliferating market of providers. Central to this endeavour is the need to balance multiple quantitative and qualitative criteria, including performance, cost, scalability, security, compliance and reliability. Approaches typically adopt multi-criteria decision analysis frameworks to model stakeholder preferences and trade-offs, employing techniques such as Analytic Hierarchy Process, Technique for Order of Preference by Similarity to Ideal Solution and hybridised algorithms that integrate fuzzy logic, linguistic variables or probabilistic terms to handle uncertainty. Emerging methods seek greater automation and agility through parameter-ranking priority weightage schemes and sensitivity analyses, while domain-specific adaptations—for instance in precision farming or scientific computing—demonstrate the global applicability of these techniques. Practical implementations often feature decision support tools and brokering mechanisms that streamline data collection, comparison and visualisation, thereby empowering users to make informed, unbiased choices aligned with their operational goals.

Research from Nature Portfolio

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Research from all publishers

Recent work has introduced a parameter-ranking priority level weightage algorithm that simplifies vendor evaluation by categorising criteria into priority tiers and computing weighted averages across metrics such as service breadth, infrastructure resilience, pricing options and market reputation. Experimental results confirm that this approach delivers an objective, time-efficient ranking of cloud service providers tailored to specific software development needs. In the agricultural sector, an integrated framework combining Analytic Hierarchy Process and TOPSIS has been developed to aid precision-farming stakeholders in selecting data-analytics platforms. The model encompasses preparatory requirement elicitation, AHP-based weight derivation and TOPSIS-driven platform ranking, validated via multi-case sensitivity tests that underscore its robustness and consistency in distinguishing between major public clouds. Another significant advance merges the Best Worst Method with TOPSIS to form a novel multi-criteria decision-making tool. By leveraging relative preference judgments to determine criterion weights and computing similarity to ideal solutions, this hybrid method outperforms traditional AHP in computational efficiency and ranking stability, making it well suited to dynamic cloud environments where decision speed and consistency are paramount.

Cloud Service Selection and Evaluation Techniques publication trend

The graph below shows the total number of articles in cloud service selection and evaluation techniques across all publications each year (not limited to Nature Index journals).

Technical terms

Multi-Criteria Decision Analysis (MCDA): A family of formal methods for evaluating and ranking alternatives when multiple, often conflicting, criteria are involved.

Analytic Hierarchy Process (AHP): A structured technique for decomposing decision problems into hierarchies, assigning weights via pairwise comparisons, and synthesising scores for alternatives.

Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS): A ranking method that identifies solutions closest to an ideal point and furthest from a nadir point based on normalised criteria distances.

Best Worst Method (BWM): A weighting technique wherein decision-makers specify the most and least important criteria, facilitating consistent derivation of criterion weights.

Parameter-Ranking Priority Level Weightage (PRPLW): An algorithm that assigns criteria to priority levels and computes weighted averages to simplify and accelerate vendor selection.

Quality of Service (QoS): A set of measurable performance attributes—such as latency, throughput, availability and security—that characterise a cloud service’s operational quality.

References

1. Cloud-Based Software Development Lifecycle: A Simplified Algorithm for Cloud Service Provider Evaluation with Metric Analysis. Big Data Mining and Analytics (2023).
2. Multi-Criteria decision analysis approach for selecting feasible data analytics platforms for precision farming. Computers and Electronics in Agriculture (2023).
3. An Integrated MCDM Approach for Cloud Service Selection Based on TOPSIS and BWM. IEEE Access (2020).