A new learning automata based approach for increasing utility of service providers

Summary Utility is an important factor for serviceproviders, and they try to increase their utilities through adopting different policies and strategies. Because of unpredictable failures in systems, there are many scenarios in which the failures may cause random losses for service providers. Loss sharing can decrease negative effects of unexpected random losses. Because of capabilities of learning automata in random and stochastic environments, in this paper, a new learning automaton based method is presented for loss sharing purpose. It is illustrated that the loss sharing can be useful for service providers and helps them to decrease negative effect of the random losses. The presented method can be used especially in collaborative environments such as federated clouds. Results of the conducted experiments show the usefulness of the presented approach to improve utility of service providers. This work presents a learning automaton based approach for reaching a Pareto optimal loss sharing agreement. The agreement improves utility of service providers and decrease negative effect of the random losses. The results of the conducted experiments illustrate that risk averse service providers will be interested to share their random losses.