<p>The rapid expansion of cloud computing presents challenges for users in selecting suitable cloud service providers (CSPs). The CSPs provide numerous services based on industry-leading quality of service (QoS) attributes like availability, best practices, compliance, durability, latency, reliability, response time, successability, throughput, etc. Researchers have applied multi-attribute decision-making (MADM) algorithms to evaluate various CSPs based on QoS attributes. However, it is unrealistic for all CSPs to meet every QoS attribute. As a result, the performance measure value of CSPs on attributes called the decision matrix, is incomplete. On the other hand, the decision matrix includes some unavailable performance measure values. It makes selecting a suitable CSP more challenging. This phenomenon inspires us to propose a QoS-centric MADM (QMADM) strategy and apply it to seven well-known and recent algorithms to determine a suitable CSP. QMADM strategy handles the unavailable performance measure values using three imputation techniques: min, max, and mean. The performance of the proposed strategy is assessed by employing two cases, one with unavailable performance measure values and another with available performance measure values, to demonstrate the impact. The simulation results of the proposed strategy are presented using three imputation techniques across seven algorithms and compared with the traditional seven algorithms in terms of QoS for web services (QWS) dataset to provide a comprehensive analysis. The results demonstrate the consistent performance of the proposed strategy in 6 out of 7 algorithms, regardless of the imputation techniques used for handling unavailable performance measure values. We also perform sensitivity analysis to provide valuable insights and show the robustness of the proposed strategy. The proposed strategy creates a reliable decision-support system for complex cloud service selection.</p>