System and method for determining a network performance property in at least one network

1. A method of determining a network performance property in at least one computer network, the method comprising:

sampling, by a processor, traffic in active communication with the at least one computer network, in a location of the at least one network, wherein the sampling comprises retrieving copies of randomly chosen packets from at least one network device of the at least one network, wherein packets are randomly chosen for sampling at a predefined sampling rate of retrieving one packet out of every ‘R’ packets, and wherein each packet of the randomly chosen packets has the same probability to be sampled;

analyzing, by the processor, the sampled traffic to group the randomly chosen packets to flows, wherein a flow is defined as a set of packets with a common property selected from the group consisting of: communication protocol, port number, source IP address and destination IP address; and

predicting, by the processor, at least one network property of the at least one network based on the grouped randomly chosen packets and based on at least one traffic parameter in the at least one network;

wherein the at least one traffic parameter is selected from the group consisting of: union of packet streams and intersection of packet streams, and

wherein the predicted at least one network property is selected from the group consisting of: total number of flows, number of flows with a predefined characteristic, number of packets, and volume of packets.

2. The method of claim 1 , further comprising comparing network properties between at least two network devices of the at least one network.

3. The method of claim 1 , further comprising storing, in a first database, the number of packets for each flow having a predefined characteristic.

4. The method of claim 3 , further comprising storing, in a second database, the number of flows having the predefined characteristic from the first database with packets that appear a number of times in the sampled traffic.

5. The method of claim 4 , further comprising storing, in a third database, the flows from the second database with number of appearances adjusted to total number of appearances at that location in the at least one network.

6. The method of claim 5 , comprising:

filtering values with a number of appearances larger than a predefined value;

fitting the filtered values to at least one function selected from a group consisting of:

exponential function, power law function and polynomial function; and

extrapolating the fitted function to values not covered in the sampled traffic.

7. The method of claim 1 , wherein the at least one traffic parameter is selected as an arbitrary traffic parameter.

8. The method of claim 1 , further comprising analyzing the sampled traffic to determine one or more frequencies of flows in the at least one network.

9. The method of claim 1 , wherein the sampling rate of the sampled traffic is tuned automatically according to the traffic volume of at least one network device of the at least one network.

10. The method of claim 1 , further comprising tracking each flow with a unique identifier.

11. The method of claim 1 , further comprising autonomously onboarding a new network device to send traffic samples.

12. A device for determination of a network performance property in at least one network, the device comprising:

a memory; and

a processor in active communication with the at least one network, wherein the processor is configured to:

sample traffic in a location of the at least one network, wherein the sampled traffic comprises copies of randomly chosen packets from at least one network device of the at least one network, wherein packets are randomly chosen for sampling at a predefined sampling rate of retrieving one packet out of every ‘R’ packets, and wherein each packet of the randomly chosen packets has the same probability to be sampled;

analyze the sampled traffic to group the randomly chosen packets to flows, wherein a flow is defined as a set of packets with a common property selected from the group consisting of: communication protocol, port number, source IP address and destination IP address; and

predict at least one network property of the at least one network based on the grouped the randomly chosen packets and based on at least one traffic parameter in the at least one network,

wherein the at least one traffic parameter is selected from the group consisting of: union of data packet streams and intersection of data packet streams, and

wherein the predicted at least one network property is selected from the group consisting of: total number of flows, number of flows with a predefined characteristic, number of packets, and volume of packets.

13. The device of claim 12 , further comprising a first database in communication with the processor, wherein the first database is configured to store the number of packets for each flow having a predefined characteristic.

14. The device of claim 13 , further comprising a second database in communication with the processor, wherein the second database is configured to store the number of flows having the predefined characteristic from the first database that appear a predefined number of times in the sampled traffic.

15. The device of claim 14 , further comprising a third database in communication with the processor, wherein the first database is configured to store the flows from the second database with number of appearances adjusted to total number of appearances in the at least one network.

16. The device of claim 15 , wherein the processor is configured to:

filter values with a number of appearances larger than a predefined value;

fit the filtered values to at least one function selected from a group consisting of:

exponential function, power law function and polynomial function; and

extrapolate fitted values from the third database to values not covered in the sampled traffic.

17. The device of claim 16 , wherein the processor is configured to integrate the extrapolated values over time.

18. The device of claim 12 , wherein the sampling rate of the sampled traffic is tuned automatically according to the traffic volume of at least one network device of the at least one network.

19. The method of claim 6 , wherein predicting the total number of flows comprises:

integrating the fitted function to obtain E 1 ;

summing the entries in the second database to obtain E 2 ; and

summing E 1 and E 2 to obtain a prediction of the total number of flows.

20. The system of claim 14 , wherein the processor is configured to predict the total number of flows by:

integrating the fitted function to obtain E 1 ;

summing the entries in the second database to obtain E 2 ; and

summing E 1 and E 2 to obtain a prediction of the total number of flows.