Abstract: In some implementations, the method may include providing one or more collectors which periodically request memory utilization from a device. In addition, the method may include receiving, by a stream processor, memory utilization from the one or more collectors. The method may include monitoring, by the stream processor, if an average memory utilization evaluated over a predetermined time crosses a predetermined threshold (for example 5 minutes). Moreover, the method may include sending data downstream to a data sink for persistence. Also, the method may include sending a new sampling strategy to the one or more collectors, if the average memory utilization evaluated over the predetermined time crosses the predetermined threshold.

Abstract: In some implementations, the device may include initiating a stream producer that sends formatted data to a topic. In addition, the device may include ingesting, by a sink connector, the formatted data into an object storage service. The device may include implementing an event-driven serverless compute, where the event-driven serverless compute is triggered automatically when any new data is ingested to the object storage service, and where the event-driven serverless compute reads the JSON data, converts it to transformed data, and writes the transformed data to a distributed data store. Moreover, the device may include creating an ETL job, where the ETL job reads the data, further transforms the data, and writes it back into the distributed data store as ETL transformed data. Also, the device may include sending the ETL transformed data to an LLM API in batches to create inference results, where the batches are queued to manage the rate limits.

Abstract: In some implementations, the method may include receiving, by one or more agent applications, a query from a user. In addition, the method may include providing a dataframe to the one or more agent applications. The method may include appending a predetermined number of initial entries from the dataframe to a suffix of the query. Moreover, the method may include constructing a standardized prompt template, where the query is embedded within the standardized prompt template. Also, the method may include Channeling the prompt template to one or more Large Language Models (LLMs). Further, the method may include Utilizing a GPT API to generate a generated code snippet. In addition, the method may include Executing the generated code snippet to create a resulting output. The method may include Relaying the resulting output back to the user.

Abstract: In some aspects, the system may include collecting, by a target device, event data regarding a new event on occurrence of the new event, where event data includes an old value configured to correlate the event data. Also, the system may include placing, by the target device, the event data into a service specification by using a plurality of intrinsic bindings. Furthermore, the system may include encoding both the old value and a new value into a message packet, where the new value further may include a newly updated value that is being reported as the new event. In addition, the system may include generating a comparison of the old value and the new value. Moreover, the system may include transmitting the message packet to plurality of receiving monitor devices to take action based on the comparison.

Abstract: In one general aspect, the method may include providing a user interface on a web server for users to upload audit criteria, where audit criteria further may include a mode of collection to by utilized by a collector module. Said method may also include installing a plurality of agents for automated data collection, where the agents are software modules controlled by an LLM network module. Said method may furthermore include receiving audit criteria from users in predefined template documents into the LLM network module. Said method may in addition include processing the audit criteria by the LLM network module and triggering automatic data collection by a collector to create collected data.

Abstract: In some aspects, a method may include installing, by a control plane application, a plurality of access control lists (ACLs) to an ASIC using hardware programming APIs. Also, the method may include identifying traffic flowing through the ASIC with the plurality of ACLs both on ingress and egress to create matched packets. Furthermore, the method may include executing an accounting of the matched packets using one or more hardware get APIs of the ASIC to create collected data. In addition, the method may include correlating the collected data from the one or more hardware get APIs to calculate a packet loss for a particular application and to create application correlated data. Moreover, the method may include exporting the application correlated data to an external monitoring platform over a secure channel.

Abstract: In some aspects, the system may include collecting, by a target device, event data regarding a new event on occurrence of the new event, where event data includes an old value configured to correlate the event data. Also, the system may include placing, by the target device, the event data into a service specification by using a plurality of intrinsic bindings. Furthermore, the system may include encoding both the old value and a new value into a message packet, where the new value further may include a newly updated value that is being reported as the new event. In addition, the system may include generating a comparison of the old value and the new value. Moreover, the system may include transmitting the message packet to plurality of receiving monitor devices to take action based on the comparison.

Abstract: A system and method for measuring latency is disclosed. In some implementations, the processor may include providing a switch API to process a plurality of microservices involved in a workflow and connected to a distributed in-memory keyvalue database, where the distributed in-memory keyvalue database further may include a plurality of key spaces. In addition, the processor may include managing configuration tools to track the latency of each microservice in the plurality of microservices. The processor may include programing data plane information of the microservices to create programmed data, where programming further may include collecting and analyzing data on the latency of each microservice of the plurality of microservices. Moreover, the processor may include feeding the programmed data into an ASIC.

Abstract: The present disclosure provides a system and a method of creating a data lake configured to ingest data from heterogeneous network equipment and provide access to a plurality of applications using any kind of public cloud. In one general aspect, a method to create a data lake may include installing a plurality of agents by an end-user may include a multivendor environment. The method may also include adding a plurality of data sources to be monitored to a collector module, where the plurality of data sources further may include of one or more devices and endpoints. The method may furthermore include establishing a secure connection between the collector module and the plurality of data sources. The method may in addition include collecting, by the collector module, input data from one or more organic agents and one or more vendor specific agents to create collected data.

Abstract: In some implementations, a method of flow monitoring and identification may include identifying one or more ingress interfaces, where customer flow and traffic enters a switch. In addition, the method may include identifying one or more egress interfaces where the customer flow and traffic exits the switch. The method may include determining accurate information of the customer flow passing through the one or more ingress interfaces and the one or more egress interfaces, which includes metadata from a Layer 3 IP Header.