Abstract: In one embodiment, it is proposed a method for storing input data on a set of DNA strands, said input data being represented in a numeral system. This method is remarkable in that it comprises: formatting said input data into a set of blocks of data, each block of data having a size inferior to a size of one DNA strand; applying a first encoding with an erasure code on said set of blocks of data, defining a first set of modified blocks of data, each modified block of data having a size inferior to a size of one DNA strand; applying a second encoding using an error correcting code on each modified block of data of said first set, defining a second set of modified blocks of data, each modified block having a size inferior to a size of one DNA strand; encoding each modified block of data of said second set into a nucleotides block sequence; generating a set of DNA strands, each DNA strand comprising a nucleotides block sequence obtained through said encoding.

Abstract: In one embodiment, it is proposed a method for storing input data on a set of DNA strands, said input data being represented in a numeral system. This method is remarkable in that it comprises: formatting said input data into a set of blocks of data, each block of data having a size inferior to a size of one DNA strand; applying a first encoding with an erasure code on said set of blocks of data, defining a first set of modified blocks of data, each modified block of data having a size inferior to a size of one DNA strand; applying a second encoding using an error correcting code on each modified block of data of said first set, defining a second set of modified blocks of data, each modified block having a size inferior to a size of one DNA strand; encoding each modified block of data of said second set into a nucleotides block sequence; generating a set of DNA strands, each DNA strand comprising a nucleotides block sequence obtained through said encoding.

Abstract: A content-classification model is constructed using sampling methods to create training sets of classifiers using imbalanced and/or large-volume training data; the model maps network source addresses and/or flow sizes to target applications and is applied to network traffic to identify contents thereof and estimate a tonnage of traffic corresponding to a given application.

Abstract: To improve network reliability and management in today's high-speed communication networks, we propose an intelligent system using adaptive statistical approaches. The system learns the normal behavior of the network. Deviations from the norm are detected and the information is combined in the probabilistic framework of a Bayesian network. The proposed system is thereby able to detect unknown or unseen faults. As demonstrated on real network data, this method can detect abnormal behavior before a fault actually occurs, giving the network management system (human or automated) the ability to avoid a potentially serious problem.

Abstract: A cardinality of an incoming data stream is maintained in real time; the cardinality is maintained in a data structure that is represented by an unsorted list at low cardinalities, a linear counter at medium cardinalities, and a PCSA at high cardinalities. The conversion to the linear counter makes use of the data in the unsorted list, after which that data is discarded. The conversion to the PCSA uses only the data in the linear counter.

Abstract: A cardinality of an incoming data stream is maintained in real time; the cardinality is maintained in a data structure that is represented by an unsorted list at low cardinalities, a linear counter at medium cardinalities, and a PCSA at high cardinalities. The conversion to the linear counter makes use of the data in the unsorted list, after which that data is discarded. The conversion to the PCSA uses only the data in the linear counter.

Abstract: To improve network reliability and management in today's high-speed communication networks, we propose an intelligent system using adaptive statistical approaches. The system learns the normal behavior of the network. Deviations from the norm are detected and the information is combined in the probabilistic framework of a Bayesian network. The proposed system is thereby able to detect unknown or unseen faults. As demonstrated on real network data, this method can detect abnormal behavior before a fault actually occurs, giving the network management system (human or automated) the ability to avoid a potentially serious problem.

Abstract: A system and apparatus for control and distribution of data files in large-scale distributed networks. Members of a group of servers in a multicast network elect a group leader whenever a new group leader is required, as detected by absence of a periodic message published by the leader. Election is carried out by a system of voting by each candidate whereby each candidate has a priority calculated from its configuration, and the server with the highest priority is configured to claim the leadership faster than the other candidates. As part of the claim, each candidate multicasts its priority. Each candidate that receives a multicast claim for leadership from another candidate compares its own priority against the claimant and only votes for itself if its own priority is higher. After a preconfigured period of hearing no other claimants with higher priority, the candidate with the highest priority becomes the new leader.