We propose a novel MLaaS Dataset Generator (MDG) framework that creates configurable and reproducible datasets for evaluating Machine Learning as a Service (MLaaS) selection and composition. MDG simulates realistic MLaaS behaviour by training and evaluating diverse model families across multiple real-world datasets and data distribution settings. It records detailed functional attributes, quality of service metrics, and composition-specific indicators, enabling systematic analysis of service performance and cross-service behaviour. Using MDG, we generate more than ten thousand MLaaS service instances and construct a large-scale benchmark dataset suitable for downstream evaluation. We also implement a built-in composition mechanism that models how services interact under varied Internet of Things conditions. Experiments demonstrate that datasets generated by MDG enhance selection accuracy and composition quality compared to existing baselines. MDG provides a practical and extensible foundation for advancing data-driven research on MLaaS selection and composition.

We propose a novel Infrastructure-as-a-Service composition framework that selects an optimal set of consumer requests according to the provider’s qualitative preferences on long-term service provisions. Decision variables are included in the temporal conditional preference networks to represent qualitative preferences for both short-term and long-term consumers. The global preference ranking of a set of requests is computed using a k -d tree indexing-based temporal similarity measure approach. We propose an extended three-dimensional Q-learning approach to maximize the global preference ranking. We design the on-policy-based sequential selection learning approach that applies the length of request to accept or reject requests in a composition. The proposed on-policy-based learning method reuses historical experiences or policies of sequential optimization using an agglomerative clustering approach. Experimental results prove the feasibility of the proposed framework.

We present a new two-level composition model for crowdsourced Sensor-Cloud services based on dynamic features such as spatio-temporal aspects. The proposed approach is defined based on a formal Sensor-Cloud service model that abstracts the functionality and non-functional aspects of sensor data on the cloud in terms of spatio-temporal features. A spatio-temporal indexing technique based on the 3D R-tree to enable fast identification of appropriate Sensor-Cloud services is proposed. A novel quality model is introduced that considers dynamic features of sensors to select and compose Sensor-Cloud services. The quality model defines Coverage as a Service which is formulated as a composition of crowdsourced Sensor-Cloud services. We present two new QoS-aware spatio-temporal composition algorithms to select the optimal composition plan. Experimental results validate the performance of the proposed algorithms.