Classifying network traffic to use cellular network slices based on application parameters
Systems and methods for classifying network traffic to use cellular network slices based on application parameters are described. In some embodiments, an Information Handling System (IHS) may include: a processor and a memory coupled to the processor, the memory having program instructions stored thereon that, upon execution, cause the IHS to: receive a plurality of packets originated by a single application; classify each of the plurality of packets into one of a plurality of network slices based upon network parameters of the application, where each of the network slices is associated with a weight, and for each given packet among the plurality of packets, add a weight to a header portion of the given packet, where the weight corresponds to the given packet's classification.
1. An Information Handling System (IHS), comprising:
a processor; and
a memory coupled to the processor, the memory having program instructions stored thereon that, upon execution, cause the IHS to:
receive a plurality of packets originated by a single application, wherein the single application includes a plurality of application types;
classify each of the plurality of packets into one of a plurality of network slices based upon application type of the application and network parameters of the application, wherein each packet classification comprises at least one of: video, voice, background, or best effort, and wherein each of the network slices is associated with a weight; and
for each given packet among the plurality of packets, add a weight to a header portion of the given packet, wherein the weight corresponds to the given packet's classification.
2. The IHS of claim 1 , wherein each of the plurality of network slices has a different quality-of-service (QoS) capability.
3. The IHS of claim 1 , wherein the application has a QoS requirement and wherein the weight modifies the QoS requirement for the given packet.
4. The IHS of claim 1 , wherein the network parameters comprise at least one of: maximum throughput, average throughput, low time-between packets, average time-between packets, packet size, type of protocol, or header parameters.
5. The IHS of claim 1 , wherein the classification is based upon a machine learning (ML) algorithm that receives a packet and associates the packet with a corresponding network slice based upon previously collected network parameters.
6. The IHS of claim 1 , wherein program instructions stored thereon that, upon execution, cause the IHS to:
transmit the given packet over a selected link of a multi-link Virtual Private Network (VPN), wherein the link is selected based upon the weight.
7. The IHS of claim 6 , wherein the multi-link Virtual Private Network (VPN) is established, at least in part, over a fifth generation (5G) technology cellular network.
8. The IHS of claim 6 , wherein the program instructions, upon execution, cause the IHS to, for each given packet among the plurality of packets, add a reliability attribute to another header portion of the given packet, wherein the reliability attribute adds flexibility to the selection of the link based upon the weight.
9. A memory device having program instructions stored thereon that, upon execution by a processor of an Information Handling System (IHS), cause the IHS to:
receive context information from sensors coupled to the IHS, wherein the context information includes a posture of the IHS;
receive a plurality of packets originated by a single application;
classify each of the plurality of packets into one of a plurality of network slices based upon network parameters of the application and the context information, wherein each of the network slices is associated with a weight;
for each given packet among the plurality of packets, add a weight to a header portion of the given packet, wherein the weight corresponds to the given packet's classification, wherein the weight is modified based upon the posture of the IHS; and
transmit the given packet over a selected link of a multi-link Virtual Private Network (VPN), wherein the link is selected based upon the weight.
10. The memory device of claim 9 , wherein each of the plurality of network slices has a different quality-of-service (QoS) capability, and wherein the application has a QoS requirement and wherein the weight modifies the QoS requirement for the given packet.
11. The memory device of claim 9 , wherein each packet classification comprises at least one of: video, voice, background, or best effort, and wherein the network parameters comprise at least one of: maximum throughput, average throughput, low time-between packets, average time-between packets, packet size, type of protocol, or header parameters.
12. The memory device of claim 11 , wherein the classification is based upon a machine learning (ML) algorithm that receives a packet and associates the packet with a corresponding network slice based upon previously collected network parameters.
13. The memory device of claim 9 , wherein the multi-link Virtual Private Network (VPN) is established, at least in part, over a fifth generation (5G) technology cellular network.
14. The memory device of claim 9 , wherein the program instructions, upon execution, cause the IHS to, for each given packet among the plurality of packets, add a reliability attribute to another header portion of the given packet, wherein the reliability attribute adds flexibility to the selection of the link based upon the weight.
15. A method, comprising:
receiving context information from sensors coupled to an Information Handling System (IHS), wherein the context information includes, at least in part, a distance or proximity between a user and the IHS;
receiving a plurality of packets originated by a single application;
classifying each of the plurality of packets into one of a plurality of network slices based upon network parameters of the application and the context information, wherein each of the network slices is associated with a weight;
for each given packet among the plurality of packets, adding a weight to a header portion of the given packet, wherein the weight corresponds to the given packet's classification, wherein the weight is modified based upon the distance or proximity between the user and the IHS; and
transmitting the given packet over a fifth generation (5G) technology cellular network.
16. The method of claim 15 , wherein each of the plurality of network slices has a different quality-of-service (QoS) capability, wherein the application has a QoS requirement and wherein the weight modifies the QoS requirement for the given packet, wherein each packet classification comprises at least one of: video, voice, background, or best effort, and wherein the network parameters comprise at least one of: maximum throughput, average throughput, low time-between packets, average time-between packets, packet size, type of protocol, or header parameters.
17. The method of claim 15 , wherein the classification is based upon a machine learning (ML) algorithm that receives a packet and associates the packet with a corresponding network slice based upon previously collected network parameters.
18. The method of claim 15 , wherein transmitting the given packet over the 5G technology cellular network further comprises transmitting the given packet over a selected link of a multi-link Virtual Private Network (VPN), wherein the link is selected based upon the weight.
19. The method of claim 15 , further comprising, for each given packet among the plurality of packets, adding a reliability attribute to another header portion of the given packet, wherein the reliability attribute adds flexibility to the selection of the link based upon the weight.
20. The method of claim 15 , wherein the weight is modified based on the distance between the user and the IHS relative to a threshold distance.