Abstract: Embodiments described herein provide for the pseudo-cooperative load balancing and/or distribution of traffic and/or other types of data by independent nodes. Techniques described herein may be considered “pseudo-cooperative” in that the resulting distribution of traffic and/or other data may resemble load balancing techniques in which various nodes, that serve as an ingress for data for distribution and/or other types of processing, communicate with each other (e.g., share context information) in order to perform load balancing in a coordinated or cooperative manner. In contrast, nodes in accordance with some embodiments described herein may perform load balancing with the same or similar results (e.g., a relatively even distribution of traffic and/or other data), without communicating with each other, thus saving time, processing resources, and/or other resources.

Abstract: A device may receive, from a source device, original time series data to be stored in a data structure associated with the device, and may sort the original time series data to generate sorted time series data. The device may identify an index for the original time series data based on the sorted time series data. The device may process the sorted time series data, with a regression model, to generate compressed time series data and parameters associated with the compressed time series data. The device may encode the index to generate an encoded index, and may store the encoded index, the compressed time series data, and the parameters in the data structure.

Abstract: Embodiments described herein provide for the pseudo-cooperative load balancing and/or distribution of traffic and/or other types of data by independent nodes. Techniques described herein may be considered “pseudo-cooperative” in that the resulting distribution of traffic and/or other data may resemble load balancing techniques in which various nodes, that serve as an ingress for data for distribution and/or other types of processing, communicate with each other (e.g., share context information) in order to perform load balancing in a coordinated or cooperative manner. In contrast, nodes in accordance with some embodiments described herein may perform load balancing with the same or similar results (e.g., a relatively even distribution of traffic and/or other data), without communicating with each other, thus saving time, processing resources, and/or other resources.

Abstract: Embodiments described herein provide for the pseudo-cooperative load balancing and/or distribution of traffic and/or other types of data by independent nodes. Techniques described herein may be considered “pseudo-cooperative” in that the resulting distribution of traffic and/or other data may resemble load balancing techniques in which various nodes, that serve as an ingress for data for distribution and/or other types of processing, communicate with each other (e.g., share context information) in order to perform load balancing in a coordinated or cooperative manner. In contrast, nodes in accordance with some embodiments described herein may perform load balancing with the same or similar results (e.g., a relatively even distribution of traffic and/or other data), without communicating with each other, thus saving time, processing resources, and/or other resources.

Abstract: Embodiments described herein provide for the pseudo-cooperative load balancing and/or distribution of traffic and/or other types of data by independent nodes. Techniques described herein may be considered “pseudo-cooperative” in that the resulting distribution of traffic and/or other data may resemble load balancing techniques in which various nodes, that serve as an ingress for data for distribution and/or other types of processing, communicate with each other (e.g., share context information) in order to perform load balancing in a coordinated or cooperative manner. In contrast, nodes in accordance with some embodiments described herein may perform load balancing with the same or similar results (e.g., a relatively even distribution of traffic and/or other data), without communicating with each other, thus saving time, processing resources, and/or other resources.

Abstract: A device receives network data and artificial packets associated with a network, and processes the network data to generate sequential sets of the network data. The device processes the sequential sets of the network data, with a time series model, to generate time series network data, and determines whether the time series network data satisfies a quality threshold. The device transforms, when the time series network data satisfies the quality threshold, residual data in the time series network data to identify data outliers, and removes the data outliers to generate modified time series network data. The device processes the modified time series network data, with a model, to generate a forecast data point, and compares the forecast data point and an actual data point to determine whether an anomaly exists in the network. The device performs one or more actions based on determining whether the anomaly exists in the network.

Abstract: A device may receive, from a source device, original time series data to be stored in a data structure associated with the device, and may sort the original time series data to generate sorted time series data. The device may identify an index for the original time series data based on the sorted time series data. The device may process the sorted time series data, with a regression model, to generate compressed time series data and parameters associated with the compressed time series data. The device may encode the index to generate an encoded index, and may store the encoded index, the compressed time series data, and the parameters in the data structure.

Abstract: A device receives network data and artificial packets associated with a network, and processes the network data to generate sequential sets of the network data. The device processes the sequential sets of the network data, with a time series model, to generate time series network data, and determines whether the time series network data satisfies a quality threshold. The device transforms, when the time series network data satisfies the quality threshold, residual data in the time series network data to identify data outliers, and removes the data outliers to generate modified time series network data. The device processes the modified time series network data, with a model, to generate a forecast data point, and compares the forecast data point and an actual data point to determine whether an anomaly exists in the network. The device performs one or more actions based on determining whether the anomaly exists in the network.