Abstract:
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.
Abstract:
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.
Abstract:
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.
Abstract:
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.
Abstract:
A method is provided in one example embodiment and may include determining a predicted average throughput for each of one or more cellular interfaces and adjusting bandwidth for each of the one or more of the cellular interfaces based, at least in part, on the predicted average throughput determined for each of the one or more cellular interfaces. Another method can be provided, which may include determining a variance in path metrics for multiple cellular interfaces and updating a routing table for the cellular interfaces using the determined variance if there is a difference between the determined variance and a previous variance determined for the cellular interfaces. Another method can be provided, which may include monitoring watermark thresholds for a MAC buffer; generating an interrupt when a particular watermark threshold for the MAC buffer is reached; and adjusting enqueueing of uplink packets into the MAC buffer based on the interrupt.
Abstract:
Techniques for interface bandwidth management. A wired interface bandwidth is configured for a wired interface of a router. A cellular interface bandwidth is configured for a cellular interface of cellular interfaces of the router. The cellular interface bandwidth includes an uplink bandwidth. One or more instantaneous uplink throughput values for the cellular interface are determined based on one or more uplink throughput per resource block values for the cellular interface. A predicted average uplink throughput for the cellular interface is determined based on the one or more instantaneous uplink throughput values. The uplink bandwidth is dynamically adjusted based on the predicted average uplink throughput determined for the cellular interface of the router.
Abstract:
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.
Abstract:
A method is provided in one example embodiment and may include determining a predicted average throughput for each of one or more cellular interfaces and adjusting bandwidth for each of the one or more of the cellular interfaces based, at least in part, on the predicted average throughput determined for each of the one or more cellular interfaces. Another method can be provided, which may include determining a variance in path metrics for multiple cellular interfaces and updating a routing table for the cellular interfaces using the determined variance if there is a difference between the determined variance and a previous variance determined for the cellular interfaces. Another method can be provided, which may include monitoring watermark thresholds for a MAC buffer; generating an interrupt when a particular watermark threshold for the MAC buffer is reached; and adjusting enqueueing of uplink packets into the MAC buffer based on the interrupt.
Abstract:
A method is provided in one example embodiment and may include determining a predicted average throughput for each of one or more cellular interfaces and adjusting bandwidth for each of the one or more of the cellular interfaces based, at least in part, on the predicted average throughput determined for each of the one or more cellular interfaces. Another method can be provided, which may include determining a variance in path metrics for multiple cellular interfaces and updating a routing table for the cellular interfaces using the determined variance if there is a difference between the determined variance and a previous variance determined for the cellular interfaces. Another method can be provided, which may include monitoring watermark thresholds for a MAC buffer; generating an interrupt when a particular watermark threshold for the MAC buffer is reached; and adjusting enqueueing of uplink packets into the MAC buffer based on the interrupt.
Abstract:
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.