Abstract: Techniques for automatically recommending a new data center, such as a point-of-presence (POP) center are provided. In one technique, multiple candidate locations for a new POP center are considered. An impact of adding a new POP to each candidate location is estimated and a score is generated. Each candidate location is ranked based on the score. In a related technique, an impact score for a candidate location is based on a prediction of whether and how much a new POP center at the candidate location would reduce response times of clients that would connect to the new POP center. The prediction may be based on a model that is generated based on response data generated by clients that are connecting to existing POP centers. The model may take into account multiple factors, such as geographic distance, network distance, type of browser executing on the clients, type of operating system, etc.

Abstract: Techniques for automatically recommending a new data center, such as a point-of-presence (POP) center are provided. In one technique, multiple candidate locations for a new POP center are considered. An impact of adding a new POP to each candidate location is estimated and a score is generated. Each candidate location is ranked based on the score. In a related technique, an impact score for a candidate location is based on a prediction of whether and how much a new POP center at the candidate location would reduce response times of clients that would connect to the new POP center. The prediction may be based on a model that is generated based on response data generated by clients that are connecting to existing POP centers. The model may take into account multiple factors, such as geographic distance, network distance, type of browser executing on the clients, type of operating system, etc.