Large-scale scientific experiments increasingly rely on geo-distributed clouds to serve relevant data to scientists worldwide with minimal latency. State-of-the-art caching systems often require the client to access the data through a caching proxy, or to contact a metadata server to locate the closest available copy of the desired data. Also, such caching systems are inconsistent with the design of distributed hash-table databases such as Dynamo, which focus on allowing clients to locate data independently. We argue there is a gap between existing state-of-the-art solutions and the needs of geographically distributed applications, which require fast access to popular objects while not degrading access latency for the rest of the data. In this paper, we introduce a probabilistic algorithm allowing the user to locate the closest copy of the data efficiently and independently with minimal overhead , allowing low-latency access to non-cached data. Also, we propose a network-efficient technique to identify the most popular data objects in the cluster and trigger their replication close to the clients. Experiments with a real-world data set show that these principles allow clients to locate the closest available copy of data with small memory footprint and low error-rate, thus improving read-latency for non-cached data and allowing hot data to be read locally.