Financial application as a software service on cloud

In this work, we propose a SaaS model that provides service to ordinary investors, unfamiliar with finance models, to evaluate the price of an option that is currently being traded before taking a decision to enter into a contract. In this model, investors may approach a financial Cloud Service Provider (CSP) to compute the option price with time and/or accuracy constraints. The option pricing algorithms are not only computationally intensive but also communication intensive. Therefore, one of the key components of the methodology presented in this paper is the topology-aware communication between tasks and scheduling of tasks in virtual machines with the goal of reducing the latency of communication between tasks. We perform various experiments to evaluate how our model can map the tasks efficiently to reduce communication latency, hide network latency ensuring that all virtual machines are busy increasing response time of users.