Operations, Administration, and Maintenance (OAM) scaling systems and methods are implemented by a network function performed by one of a physical network element and a virtual network element executed on one or more processors. The OAM scaling method includes providing N packet services, N is an integer; and, responsive to determined OAM session scaling limits, providing OAM sessions for the N packet services in an oversubscribed manner, wherein the determined OAM session scaling limits include M OAM sessions supported by the network function, M is an integer and less than N.

A method for monitoring message packets that are exchanged between at least two control units. The message packets are concatenated in a data stream and each have an identifier, a payload, and a length specification of the payload described by a data item of predefined word size. The at least two control units are connected by a distributor. The distributor is connected by a first distributor port to a first of the at least two control units, is connected by a second distributor port to a second of the at least two control units, and is connected by a third distributor port to a computer system. The data stream flows through the first and distributor port for communication between the first node and the second node. The computer system has a memory, and information on the respective identifiers of the message packets is stored in the memory.

Distributed data sampling, including: receiving a sampling target; generating, based on one or more sensors, sampled data; determining, based on the sampling target, a value for the sampled data; and determining, based on the value for the sampled data, whether to provide the sampled data to a remotely disposed computing device.

The packet capture manager uses a multi-tiered storage for storing captured network traffic. Captured packets are stored on a primary storage with a time-to-live according to a retention policy. The packet capture manager receives instructions from one or more network monitoring devices identifying one or more captured packets as packets of interest. The packet capture manager flags the identified packets as packets of interest, moves the flagged packets to a secondary storage, and changes the TTL of the moved packets. A machine learning model analyzes historical data of the instructions received from the one or more network monitoring devices. The packet capture manager uses the machine learning model to identify packets of interest and move identified packets to the secondary storage without specific instructions from a network monitoring device.

Computing devices, each of which monitors information in a monitoring environment, take on the role of a controller for some of them, separating the (real or virtual) elements of the environment into subsets. Computing devices provide their results to a unification device, which combines them into a monitoring parameter. Each computing device monitors its parameters based on a timestamp, so unification devices can determine whether results from those computing devices represent the same state of the environment. Unification devices divide the results from their computing devices into uniform durations. Even if results don't reflect the same environment state, unification devices can still approximate results for unification. Elements can be reassigned on time boundaries, or can be duplicated, with unification devices still able to unify results. Predicted queries can be pre-computed.

Novel tools and techniques are provided for implementing routing of network traffic across one or more network nodes or utilization of the one or more network nodes based on one or more demand classifications and/or based on detection of a trigger event associated with the one or more demand classifications. In some embodiment, a computing system might monitor network traffic across one or more network nodes or utilization of the one or more network nodes, the network traffic being routed based on a first demand classification. The computing system might determine whether at least one trigger event associated with a second demand classification has occurred. If so, the computing system might adjust the routing of the network traffic across the one or more network nodes or adjust the utilization of the one or more network nodes, based at least in part on the second demand classification.

A first distributed processing node transmits distributed data to a second distributed processing node as intermediate consolidated data. A third distributed processing node generates intermediate consolidated data after update from received intermediate consolidated data and distributed data, and transmits the intermediate consolidated data to a fourth distributed processing node. The first distributed processing node transmits the received intermediate consolidated data to fifth distributed processing node as consolidated data. The third distributed processing node transmits the received consolidated data to a sixth distributed processing node. When an aggregation communication time period required by each distributed processing node to consolidate the distributed data or an aggregation dispatch communication time period being a total time period of the aggregation communication time period and a time period required by each distributed processing node to dispatch the consolidated data exceeds a predetermined time period, the first distributed processing node issues a warning.

Novel tools and techniques are provided for implementing data packet processing, data packet capture, data packet storage, data packet retrieval, and data packet distribution. In various embodiments, a method might include detecting, with a computer, network traffic comprising one or more data packets within a network. Based on a detection of the network traffic comprising the one or more data packets within the network, the method might include capturing the one or more data packets to move the one or more data packets from the network to a storage of the computer. Next, the method might include determining one or more attributes associated with each captured data packet. Based on a determination of the one or more attributes, the method might additionally include storing each captured data packet according to the one or more first attributes in the storage of the computer.

Systems and methods for metrics capture and use in a computing network are provided. In examples, systems and methods are provided to permit network elements (such as network devices and workloads) to be instrumented for metrics collection as part of the process of provisioning the network element on the network. In examples, a collection template is provided to a customer that can be used to generate a collection component for collecting metrics associated with the network element. In examples, the collected metrics can be stored and used by an edge recommendation system to determine one or more recommended edge sites at which the network element should be placed according to optimization criteria.

A method useful for network and spectrum defense which operates to analyze cyber data and spectra while performing real time optimization which is based on the analyzed cyber data or spectrum. The method utilizes quantum computing and reconfigurable qubits with built-in memory to sample a target cyber data or spectrum, search through the sample and determine a desired or required network or spectrum reallocation, and determine optimal values for its order parameters and Hamiltonian and tune the qubits in accordance with the determination. An embodiment may provide for spectrum optimization that minimizes frequency bandwidth, power, and bit error rate. The desired or required network or spectrum reallocation and optimal values order parameters and Hamiltonian may be stored in the built-in memory to facilitate machine learning.