Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Several emerging large-scale Internet applications such as Content Distribution Networks, and Peer-to-Peer networks could potentially benefit from knowing the underlying Internet topology and the distances (i.e., round-trip-time) beten different hosts. Most existing techniques for distance estimation either use a dedicated infrastructure or use on-line measurements in which probe packets are injected into the network during estimation. Our goal in this paper is to study off-line techniques for distance estimation that do not require a dedicated infrastructure. To this end,  propose a metric termed ''depth'' and  observe that together with a quadratic function on the geographic distance, it can predict the network distance with high accuracy using multi-variable regression. When used for closest server selection, our approach performs much better than random server selection, and similar to the on-line metrics. Our approach incurs low overhead and can be deployed easily with some DNS extensions.
['Prasun Sinha', 'Danny Raz', 'Nidhan Choudhuri']
I am writing a research paper on a idea Estimation of network distances using off-line measurements
[{"from": "human", "value": "I am writing a research paper on a idea Estimation of network distances using off-line measurements"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Several emerging large-scale Internet applications such as Content Distribution Networks, and Peer-to-Peer networks could potentially benefit from knowing the underlying Internet topology and the distances (i.e., round-trip-time) beten different hosts. Most existing techniques for distance estimation either use a dedicated infrastructure or use on-line measurements in which probe packets are injected into the network during estimation. Our goal in this paper is to study off-line techniques for distance estimation that do not require a dedicated infrastructure. To this end,  propose a metric termed ''depth'' and  observe that together with a quadratic function on the geographic distance, it can predict the network distance with high accuracy using multi-variable regression. When used for closest server selection, our approach performs much better than random server selection, and similar to the on-line metrics. Our approach incurs low overhead and can be deployed easily with some DNS extensions."}]