H04L41/142

Providing storage resources from a storage pool

Locally providing cloud storage array services for a plurality of storage systems within a data center by: receiving a request for storage resources from an operating system level virtualization service; determining, among the plurality of storage systems; an implementation of the request from the operating system level virtualization service; and providing storage resources to the operating system level virtualization service in accordance with the implementation of the request from the operating system level virtualization service.

Autonomous generation of attack signatures to detect malicious network activity
11711383 · 2023-07-25 · ·

Methods and systems for detecting malicious activity on a network. The methods described herein involve gathering data regarding a first state of a computing environment, executing an attack tool to simulate malicious activity in the computing environment, and then gathering data regarding a second state of the computing environment. The methods described herein may then involve generating a signature based on changes between the first and second states, and then using the generated signature to detect malicious activity in a target network.

LOW-COMPLEXITY DETECTION OF POTENTIAL NETWORK ANOMALIES USING INTERMEDIATE-STAGE PROCESSING
20230239316 · 2023-07-27 · ·

In an embodiment, a computer implemented method receives flow data for a network flows. The method extracts a tuple from the flow data and calculates long-term and short-term trends based at least in part on the tuple. The long-term and short-term trends are compared to determine whether a potential network anomaly exists. If a potential network anomaly does exist, the method initiates a heavy hitter detection algorithm. The method forms a low-complexity intermediate stage of processing that enables a high-complexity heavy hitter detection algorithm to execute when heavy hitters are likely to be detected.

LOW-COMPLEXITY DETECTION OF POTENTIAL NETWORK ANOMALIES USING INTERMEDIATE-STAGE PROCESSING
20230239316 · 2023-07-27 · ·

In an embodiment, a computer implemented method receives flow data for a network flows. The method extracts a tuple from the flow data and calculates long-term and short-term trends based at least in part on the tuple. The long-term and short-term trends are compared to determine whether a potential network anomaly exists. If a potential network anomaly does exist, the method initiates a heavy hitter detection algorithm. The method forms a low-complexity intermediate stage of processing that enables a high-complexity heavy hitter detection algorithm to execute when heavy hitters are likely to be detected.

TRAFFIC REPLICATION IN OVERLAY NETWORKS SPANNING MULTIPLE SITES

Some embodiments provide a method of replicating messages for a logical network. At a particular tunnel endpoint in a particular datacenter, the method receives a message to be replicated to members of a replication group. The method replicates the message to a set of tunnel endpoints of the replication group located in a same segment of the particular datacenter as the particular tunnel endpoint. The method replicates the message to a first set of proxy endpoints of the replication group, each of which is located in a different segment of the particular datacenter and for replicating the message to tunnel endpoints located in its respective segment of the particular datacenter. The method replicates the message to a second set of proxy endpoints of the replication group, each of which is located in a different datacenter and for replicating the message to tunnel endpoints located in its respective datacenter.

COMMUNICATION-PERFORMANCE CHARACTERIZATION VIA AUGMENTED REALITY

An electronic device that assesses communication performance is described. During operation, the electronic device receives information specifying a location in an environment. For example, the information may correspond to user-interface activity associated with a user interface. Notably, the user interface may include an augmented reality and the user-interface activity may include defining the location, such as by dropping a pin in the augmented reality. Then, the electronic device provides the information to an access point and/or a controller of the access point, where the location is within communication range of the access point. Next, the electronic device receives, from the access point and/or the controller, measurements of one or more communication performance metrics at or proximate to the location during a time interval. Moreover, the electronic device provides a graphical representation of the communication performance at or proximate to the location based at least in part on the measurements.

COMMUNICATION-PERFORMANCE CHARACTERIZATION VIA AUGMENTED REALITY

An electronic device that assesses communication performance is described. During operation, the electronic device receives information specifying a location in an environment. For example, the information may correspond to user-interface activity associated with a user interface. Notably, the user interface may include an augmented reality and the user-interface activity may include defining the location, such as by dropping a pin in the augmented reality. Then, the electronic device provides the information to an access point and/or a controller of the access point, where the location is within communication range of the access point. Next, the electronic device receives, from the access point and/or the controller, measurements of one or more communication performance metrics at or proximate to the location during a time interval. Moreover, the electronic device provides a graphical representation of the communication performance at or proximate to the location based at least in part on the measurements.

DEEP LEARNING BASED SYSTEM AND METHOD FOR INLINE NETWORK ANALYSIS
20230239310 · 2023-07-27 ·

Described herein are a device and a method for performing a network analysis. In one aspect, the device includes a reconfigurable neural network circuit to determine an indication of a predicted network characteristic. In one aspect, the reconfigurable neural network circuit includes a control circuit to select a packet attribute or a flow attribute of a raw packet stream from a pipeline, and determine a configuration setting corresponding to the packet attribute or the flow attribute. The configuration setting may indicate a configuration of the reconfigurable neural network circuit to implement a neural network. In one aspect, the reconfigurable neural network circuit includes a storage to provide neural network parameters of the neural network, according to the configuration setting. In one aspect, the reconfigurable neural network circuit includes computational circuits to perform computations based on the neural network parameters from the storage to determine the indication of the predicted network characteristic.

NETWORK DEVICE CAPACITY EXPANSION METHOD AND RELATED APPARATUS
20230007508 · 2023-01-05 ·

This application provides network device capacity expansion methods and devices. One method includes: obtaining network performance data of a target network device, determining a state of the target network device based on the network performance data of the target network device, and determining, based on the state of the target network device, whether to perform capacity expansion for the target network device. In the foregoing technical solution, whether a capacity expansion operation needs to be performed on a network device can be determined in a timely manner based on a state of the network device.

Anomaly detection and reporting in a network assurance appliance

Systems, methods, and computer-readable media for detecting and reporting anomalies in a network environment for providing network assurance. In some embodiments, a system can determine confidence scores for at least one value of parameters of a network environment defining network events occurring in the network environment. The confidences scores can indicate a frequency that the defined network events have a specific event state. The confidence scores can be monitored to detect an anomaly in the network environment. In response to detecting the anomaly in the network environment, the system can determine a relevant network state of the network environment. The relevant network state of the network environment and the anomaly in the network environment can be presented to a user.