Abstract: Systems and methods provide for Selective Tracking of Acknowledgments (STACKing) to improve buffer utilization and traffic shaping for one or more network devices. A network device can identify a first flow that corresponds to a predetermined traffic class and a predetermined congestion state. The device can determine a current window size and congestion threshold of the first flow. In response to a determination to selectively track a portion of acknowledgments of the first flow, the device can track, in main memory, information of a first portion of acknowledgments of the first flow. The device can exclude, from one or more buffers, a second portion of acknowledgments of the first flow. The device can re-generate and transmit segments corresponding to the second portion of acknowledgments at a target transmission rate based on traffic shaping policies for the predetermined traffic class and congestion state.

Abstract: Systems and methods provide for generating traffic class-specific congestion signatures and other machine learning models for improving network performance. In some embodiments, a network controller can receive historical traffic data captured by a plurality of network devices within a first period of time that the network devices apply one or more traffic shaping policies for a predetermined traffic class and a predetermined congestion state. The controller can generate training data sets including flows of the historical traffic data labeled as corresponding to the predetermined traffic class and predetermined congestion state. The controller can generate, based on the training data sets, traffic class-specific congestion signatures that receive input traffic data determined to correspond to the predetermined traffic class and output an indication whether the input traffic data corresponds to the predetermined congestion state.

Abstract: Systems, methods, and computer-readable media for dynamically assigning devices to specific channels in the CBRS band. One or more dedicated channels within the CBRS band can be selected and a boost channel device group can be formed by exclusively allocating the one or more dedicated channel to the boost channel device group. Further, a normal channel device group can be formed by allocating, to the normal channel device group, one or more channels in the CBRS band that are separate from the one or more dedicated channels allocated to the boost channel device group. One or more devices communicating in the CBRS band can be selectively assigned to either the boost channel device group or the normal channel device group to selectively control communications in the CBRS band over the one or more dedicated channels utilized by the boost channel device group.

Abstract: Systems and methods provide for Selective Tracking of Acknowledgments (STACKing) to improve buffer utilization and traffic shaping for one or more network devices. A network device can identify a first flow that corresponds to a predetermined traffic class and a predetermined congestion state. The device can determine a current window size and congestion threshold of the first flow. In response to a determination to selectively track a portion of acknowledgments of the first flow, the device can track, in main memory, information of a first portion of acknowledgments of the first flow. The device can exclude, from one or more buffers, a second portion of acknowledgments of the first flow. The device can re-generate and transmit segments corresponding to the second portion of acknowledgments at a target transmission rate based on traffic shaping policies for the predetermined traffic class and congestion state.

Abstract: Systems and methods provide for generating traffic class-specific congestion signatures and other machine learning models for improving network performance. In some embodiments, a network controller can receive historical traffic data captured by a plurality of network devices within a first period of time that the network devices apply one or more traffic shaping policies for a predetermined traffic class and a predetermined congestion state. The controller can generate training data sets including flows of the historical traffic data labeled as corresponding to the predetermined traffic class and predetermined congestion state. The controller can generate, based on the training data sets, traffic class-specific congestion signatures that receive input traffic data determined to correspond to the predetermined traffic class and output an indication whether the input traffic data corresponds to the predetermined congestion state.

Abstract: Systems, methods, and computer-readable media for dynamically assigning devices to specific channels in the CBRS band. One or more dedicated channels within the CBRS band can be selected and a boost channel device group can be formed by exclusively allocating the one or more dedicated channel to the boost channel device group. Further, a normal channel device group can be formed by allocating, to the normal channel device group, one or more channels in the CBRS band that are separate from the one or more dedicated channels allocated to the boost channel device group. One or more devices communicating in the CBRS band can be selectively assigned to either the boost channel device group or the normal channel device group to selectively control communications in the CBRS band over the one or more dedicated channels utilized by the boost channel device group.

Abstract: Systems and methods provide for Selective Tracking of Acknowledgments (STACKing) to improve buffer utilization and traffic shaping for one or more network devices. A network device can identify a first flow that corresponds to a predetermined traffic class and a predetermined congestion state. The device can determine a current window size and congestion threshold of the first flow. In response to a determination to selectively track a portion of acknowledgments of the first flow, the device can track, in main memory, information of a first portion of acknowledgments of the first flow. The device can exclude, from one or more buffers, a second portion of acknowledgments of the first flow. The device can re-generate and transmit segments corresponding to the second portion of acknowledgments at a target transmission rate based on traffic shaping policies for the predetermined traffic class and congestion state.

Abstract: Systems and methods provide for Selective Tracking of Acknowledgments (STACKing) to improve buffer utilization and traffic shaping for one or more network devices. A network device can identify a first flow that corresponds to a predetermined traffic class and a predetermined congestion state. The device can determine a current window size and congestion threshold of the first flow. In response to a determination to selectively track a portion of acknowledgments of the first flow, the device can track, in main memory, information of a first portion of acknowledgments of the first flow. The device can exclude, from one or more buffers, a second portion of acknowledgments of the first flow. The device can re-generate and transmit segments corresponding to the second portion of acknowledgments at a target transmission rate based on traffic shaping policies for the predetermined traffic class and congestion state.

Abstract: Systems and methods provide for generating traffic class-specific congestion signatures and other machine learning models for improving network performance. In some embodiments, a network controller can receive historical traffic data captured by a plurality of network devices within a first period of time that the network devices apply one or more traffic shaping policies for a predetermined traffic class and a predetermined congestion state. The controller can generate training data sets including flows of the historical traffic data labeled as corresponding to the predetermined traffic class and predetermined congestion state. The controller can generate, based on the training data sets, traffic class-specific congestion signatures that receive input traffic data determined to correspond to the predetermined traffic class and output an indication whether the input traffic data corresponds to the predetermined congestion state.