Oikonomos-II+ is a hybrid, reinforcement-learning system for recommending optimal cloud-instance types for High-Performance Computing (HPC) and Artificial-Intelligence (AI) applications. Unlike existing approaches that require historical data or repeated job executions, Oikonomos-II+ learns online using user-submitted jobs. It combines a modified Neural-LinUCB algorithm with Gaussian-Process regression to model the relationship between job parameters, instance types, and execution time. This allows it to balance exploration and exploitation efficiently, even in the absence of prior data. We evaluated six configurations of Oikonomos-II+ on a diverse set of HPC and AI workloads, optimizing for cost and speed. Results show that the complete system converges to optimal resource choices, outperforming purely predictive or search-based approaches. By treating deployed applications as a black box and by eliminating the need for preexisting training data or auxiliary runs, Oikonomos-II+ provides a general-purpose, low-overhead solution for dynamic resource selection in heterogeneous cloud environments.

The cloud has become a powerful environment for deploying High-Performance Computing (HPC) applications. However, the size and heterogeneity of cloud-hardware offerings poses a challenge in selecting the optimal cloud instance type. Users often lack the knowledge or time necessary to make an optimal choice. In this work, we propose Oikonomos, a data-driven, opportunistic, resource-recommendation system for HPC applications in the cloud. Oikonomos trains a Multi-layer Perceptron (MLP) to predict the performance of a given HPC application, for different input parameters and instance types. It, then, calculates the cost of executing the application on different instance types and proposes the one best-fitting the user's needs. We deployed Oikonomos on a diverse mix of HPC workloads, and found that for all applications, it approached an optimal policy. The optimal instance type was chosen in 90% of the cases for seven out of eight applications, scoring a Mean Absolute Percentage Error (MAPE) consistently below 20%. This demonstrated that Oikonomos can provide a practical, general-purpose, resource-recommendation system for cloud HPC.

The cloud has become a powerful and useful environment for the deployment of High-Performance Computing (HPC) applications, but the large number of available instance types poses a challenge in selecting the optimal platform. Users often do not have the time or knowledge necessary to make an optimal choice. Recommender systems have been developed for this purpose but current state-of-the-art systems either require large amounts of training data, or require running the application multiple times; this is costly. In this work, we propose Oikonomos-II, a resource-recommendation system based on reinforcement learning for HPC applications in the cloud. Oikonomos-II models the relationship between different input parameters, instance types, and execution times. The system does not require any preexisting training data or repeated job executions, as it gathers its own training data opportunistically using user-submitted jobs, employing a variant of the Neural-LinUCB algorithm. When deployed on a mix of HPC applications, Oikonomos-II quickly converged towards an optimal policy. The system eliminates the need for preexisting training data or auxiliary runs, providing an economical, general-purpose, resource-recommendation system for cloud HPC.