On Biasing towards Optimized Application Placement in the Cloud
We consider a cloud environment, consisting of physical entities, subjected to user application requests, consisting of logical entities with relationship constraints among them, such as location constraints. We are concerned with the application placement problem, which is a mapping of logical to physical entities that satisfies the constraints and optimizes an objective function, which combines system and user performance. We describe an efficient technique that is based on random search methods and uses biased statistical sampling methods and demonstrate the feasibility of our methodology using a large-size simulation experiment. We note that the magnitude of biasing has an important impact on the quality of placement and investigate the tradeoff between biasing and optimality of placement solutions.