Service plane optimizations with learning-enabled flow identification
The disclosed technology relates to a process for optimizing data flow within a computer network. The technology utilizes shared memory and machine learning logic to improve the efficiency of how computing resources are used during a transmission of data packets in the computer network. The shared memory is implemented during the transmission of data packets between the data plane and the service plane so that the copying of data packets after the data packets have been received and processed by an application is not necessary. The machine learning logic is implemented during the processing of the data packets in order to adjust a frequency or extent that the data packets (and corresponding source of the data packets) need to be evaluated to ensure that malicious content is not being transmitted across the computer network.
1. A method for optimizing data flow in a computer network, the method comprising:
storing data plane identifying information about a data packet into shared memory to yield stored identifying information, wherein the shared memory is accessible only between a data plane of the computer network where the data packet originates and a service plane of the computer network associated with an application hosted on the computer network; and
matching the stored identifying information in the shared memory with the data plane identifying information to identify the data packet.
2. The method of claim 1 , wherein the data plane identifying information is stored in a ring buffer associated with the shared memory.
3. The method of claim 1 , further comprising:
calculating a confidence level using machine learning logic, wherein the confidence level is calculated based on the data packet; and
assigning a source of the data packet with the confidence level, wherein the confidence level identifies a level of trustworthiness of the source and its corresponding data packets.
4. The method of claim 3 , wherein the confidence level defines a frequency of performing subsequent evaluations on other data packets associated with the source, wherein the frequency identifies a period of time until a next evaluation.
5. The method of claim 4 , wherein the frequency also identifies a number of data packets that are transmitted without evaluations until the next evaluation.
6. The method of claim 3 , wherein the confidence level defines an extent of evaluating other data packets associated with the source, wherein the extent identifies a number of data packets that are evaluated during a next evaluation.
7. A non-transitory computer-readable medium comprising instructions that optimize data flow in a computer network, the instructions, when executed by a computing system, cause the computing system to perform operations comprising:
storing data plane identifying information about a data packet into shared memory to yield stored identifying information, wherein the shared memory is accessible only between a data plane of the computer network where the data packet originates and a service plane of the computer network associated with an application hosted on the computer network; and
matching the stored identifying information in the shared memory with data plane identifying information to identify the data packet.
8. The non-transitory computer-readable medium of claim 7 , wherein the non-transitory computer-readable medium stores additional instructions which, when executed by the computing system, cause the computing system to perform operations comprising:
storing the data plane identifying information in a ring buffer associated with the shared memory.
9. The non-transitory computer-readable medium of claim 7 , wherein the non-transitory computer-readable medium stores additional instructions which, when executed by the computing system, cause the computing system to perform operations comprising:
calculating a confidence level using machine learning logic, wherein the confidence level is calculated based on the data packet; and
assigning a source of the data packet with the confidence level, wherein the confidence level identifies a level of trustworthiness of the source and its corresponding data packets.
10. The non-transitory computer-readable medium of claim 9 , wherein the confidence level defines a frequency of performing subsequent evaluations on other data packets associated with the source, wherein the frequency identifies a period of time until a next evaluation.
11. The non-transitory computer-readable medium of claim 10 , wherein the frequency also identifies a number of data packets that are transmitted without evaluations until the next evaluation.
12. The non-transitory computer-readable medium of claim 9 , wherein the confidence level defines an extent of evaluating other data packets associated with the source, wherein the extent identifies a number of data packets that are evaluated during a next evaluation.
13. A system optimizing data flow in a computer network, the system comprising:
a processor; and
a memory storing instructions that, when executed by the system, cause the system to perform operations comprising:
storing data plane identifying information about a data packet being transmitted in a computer network into shared memory to yield stored identifying information, wherein the shared memory is accessible only between a data plane of the computer network where the data packet originates and a service plane of the computer network associated with an application hosted on the computer network; and
matching the stored identifying information in the shared memory with the data plane identifying information to identify the data packet.
14. The system of claim 13 , wherein the memory stores additional instructions that, when executed by the system, cause the system to perform operations comprising:
storing the data plane identifying information in a ring buffer associated with the shared memory.
15. The system of claim 13 , wherein the memory stores further instructions that, when executed by the system, cause the system to perform operations comprising:
calculating a confidence level using machine learning logic, wherein the confidence level is calculated based on the data packet; and
assigning a source of the data packet with the confidence level, wherein the confidence level identifies a level of trustworthiness of the source and its corresponding data packets.
16. The system of claim 15 , wherein the confidence level defines a frequency of performing subsequent evaluations on other data packets associated with the source, wherein the frequency identifies a period of time until a next evaluation.
17. The system of claim 16 , wherein the frequency also identifies a number of data packets that are transmitted without evaluations until the next evaluation.
18. The system of claim 15 , wherein the confidence level defines an extent of evaluating other data packets associated with the source, wherein the extent identifies a number of data packets that are evaluated during a next evaluation.
19. The system of claim 15 , wherein the memory stores additional instructions that, when executed by the system, cause the system to perform operations comprising:
evaluating the data packet at the application by determining whether the data packet includes malicious content.
20. The system of claim 15 , wherein the memory stores further instructions that, when executed by the system, cause the system to perform operations comprising:
generating a verdict based on an evaluation of the data packet, wherein the verdict instructs the computer network whether the data packet is to be forwarded or dropped.