Abstract: Some embodiments are directed to a computing scheduler for a market-oriented hybrid cloud infrastructure composed of private and public machines and wherein services specified in a contract, comprising the steps of: predicting the workload of requests of services, sampling the service workload by dividing the day into slots of a finished period of time, the period of a slot being a parameter; deducting a pool of virtual machines (VMs) from the sampled service workload for a day; assigning the service requests to the pool of VMs according to each slot of the day; initializing, for a slot k, a population of VMs assignments; applying a genetic algorithm to compute the solutions of VMs scheduling for each slot; storing the solutions in a Pareto archive; selecting a solution according to a chosen policy; saving the current state; repeating the operations until all the slots of a day have been processed.

Abstract: It is proposed a computing method, providing a new multi-objective Continuous Genetic Algorithm (GA) for Portfolio Management. First, it retrieves both the historical prices of the dealt with stocks and the specifications of the user. Second, it computes the return and risk of each stock. The third step is the processing of a specific GA to find the portfolios with the best tradeoff between return and risk. The GA uses a crossover and a mutation operator to improve the obtained solutions quality. After a fixed number of generations, a set of Pareto optimum solutions is stored in a Pareto archive. Next, it selects one solution among the set of proposed Pareto. This selection process is done according to the user's specifications and profile. This solution will be the portfolio determining the strategy of investment until the next processing cycle of the next period of time.