Vmware, Inc.

Cisco Technology, Inc.

Avi Networks

Gaurav Rastogi

Kalyan Ghosh

Ashutosh Gupta

Samar Sharma

Ramsundar Janakiraman

Badri Ramaswamy

Joy J Chatterjee

Thomas James Edsall

Dinesh Ganapathy Dutt

Raymond Kloth

Jeevan Madhu Kamisetty

Sanjaya Kumar

Sreeram Iyer

Raju Kumar

Srinivas Avasarala

Matthew Cressa

Fabio Rodolfo Maino

Murali Basavaiah

Praveen Yalagandula

Ranganathan Rajagopalan

Abhijeet Joglekar

Rajesh Bhandari

Jonathan Zhanjun Yue

Sandeep Yadav

Prajval Bavi

Malhar Singh

Raymond J Kloth

Kalyan K Ghosh

Some embodiments of the invention provide a method for collecting metric values relating to operations of a set of one or more resources executing on host computers in a datacenter. In some embodiments, the method hierarchically collects and analyzes samples, with a first set of samples collected and analyzed in the data plane, and a second set of samples collected and analyzed in the control plane by aggregating the samples collected in the data plane. In some embodiments, the data plane includes host computers on which sample collecting engines (e.g., service engines) execute, while the control plane includes a set of one or more servers that obtains sample data collected by the host computers, and aggregates and analyzes this data.

Collecting samples hierarchically in a datacenter

Described are systems and methods for automatically generating, by a computing device, a regular expression that matches a list of input strings. A method includes identifying a set of baseline regular expression classes that match a portion of an input string of the list of input strings. The method further generates a current regular expression as a sequence of baseline regular expression classes from the set of baseline regular expression classes based on matching baseline regular expression classes to characters of a first input string of the list of input strings. The method further determines whether the current regular expression matches all input strings of the list of input strings, and if it does not, the method regenerates a portion of the current regular expression that occurs after an earliest character, in order, of one of the one or more input strings, that does not match the current regular expression.

Building a dynamic regular expression from sampled data

Autoscaling comprises: accessing operational data associated with a pool of servers; determining an expected load associated with a service provided by the pool of servers based at least in part on the operational data; determining an estimated capacity associated with the service provided by the pool of servers; determining that an autoscale operation is to be performed based at least in part on the expected load and the estimated capacity, wherein the autoscale operation automatically scales out one or more additional servers or automatically scales in one or more servers in the pool of servers; and performing the autoscale operation.

In intelligent autoscale of services

Managing performance metrics includes: obtaining a plurality of performance metrics associated with a plurality of sources on a network; aggregating, at a first rate, the plurality of performance metrics associated with the plurality of sources to generate a plurality of first aggregated results; maintaining at least some of the plurality of first aggregated results in one or more memories; aggregating, at a second rate, the plurality of first aggregated results to generate a plurality of second aggregated results, the second rate being a lower rate than the first rate; and maintaining at least some of the plurality of second aggregated results in the one or more memories.

Scalable real time metrics management

A custom metrics technique includes: accessing a packet; processing the packet using a packet processing pipeline of a service engine in a distributed network service platform, including: reaching a pre-specified point in the packet processing pipeline; inserting, in the packet processing pipeline, script code that corresponds to the pre-specified point; executing the script code to collect at least metric-related data associated with a user-specified metric object; and executing remaining packet processing pipeline.

User defined custom metrics

Determining and presenting traffic patterns includes: obtaining a first set of traffic metrics pertaining to network traffic associated with a plurality of containers of a container-based cloud computing platform, wherein: the plurality of containers supports a plurality of microservices; and a microservice has one or more corresponding microservice instances that execute on one or more of the plurality of containers; transforming the first set of traffic metrics into a second set of traffic metrics, the second set of traffic metrics comprising network traffic information pertaining to at least some of the plurality of microservices, the transformation being based at least in part on information pertaining to the plurality of containers and the plurality of microservices supported by the plurality of containers; constructing a microservice map based on the second set of traffic metrics; and outputting the microservice map.

Traffic pattern detection and presentation in container-based cloud computing architecture

Intelligent autoscale of services

A method for evaluating health of a distributed network service environment (DNSE) includes determining an application performance measurement (APM) based at least in part on performance metrics (PM) associated with sources, where the sources are associated with the DNSE. The sources include service engine(s) and servers. At least some of the servers are configured to provide a distributed application, and the service engine(s) are configured to provide the servers with a network service and collect at least a part of the PM. The method includes determining a health risk of the DNSE based at least in part on risk parameters obtained by the service engine(s). The method includes combining the APM and the health risk of the DNSE to determine a health indicator. If the indicator indicates a change of the DNSE, a notification of the change is generated. The method includes outputting the notification of the change.

Distributed network service risk monitoring and scoring

A technique is provided for facilitating fabric membership login for an N_Port of a storage area network. A communication from a network node is received. The communication may include a portion of criteria associated with the N_Port. Using at least a portion of the portion of criteria, a virtual fabric identifier corresponding to a virtual fabric which is associated with the N_Port may be automatically identified. Fabric configuration information, which includes the virtual fabric identifier, may be automatically provided to the network node. A fabric login request from the N_Port to login to the virtual fabric may then be received. According to a specific embodiment, the communication may be transmitted from a network node to an F_Port on a Fibre Channel switch. When the N_Port received the fabric configuration information from the F_Port, the generating, using at least a portion of the received fabric configuration information, a fabric login request message may be generated by the N_Port which includes a fabric login request for the N_Port to login to the virtual fabric corresponding to the virtual fabric identifier. In at least one implementation, the N_Port and/or F_Port may be configured or designed to support trunking functionality.

Technique for implementing virtual fabric membership assignments for devices in a storage area network

A technique is provided for facilitating fabric membership login for an N_Port of a storage area network. A communication from a network node is received. The communication may include a portion of criteria associated with the N_Port. Using at least a portion of the portion of criteria, a virtual fabric identifier corresponding to a virtual fabric which is associated with the N_Port may be automatically identified. Fabric configuration information, which includes the virtual fabric identifier, may be automatically provided to the network node. A fabric login request from the N_Port to login to the virtual fabric may then be received. According to a specific embodiment, the communication may be transmitted from a network node to an F_Port on a Fiber Channel switch. When the N_Port received the fabric configuration information from the F_Port, the generating, using at least a portion of the received fabric configuration information, a fabric login request message may be generated by the N_Port which includes a fabric login request for the N_Port to login to the virtual fabric corresponding to the virtual fabric identifier. In at least one implementation, the N_Port and/or F_Port may be configured or designed to support trunking functionality.

Methods and apparatus for performing SPAN for a virtual port are disclosed. Specifically, a frame is received from a first port. The frame or a copy thereof is transmitted to a second port, where the second port is identified in a header of the frame. At least one of the first port and the second port is a virtual port. The frame or a copy thereof is also transmitted to a third port, thereby enabling an analyzer coupled to the third port to analyze traffic received by the third port.

Virtual port based span

Methods and devices are provided for non-disruptive monitoring of network traffic through one or more ports of a Fibre Channel network device. Preferred embodiments of the invention are used in conjunction with the switched port analyzer ('SPAN') and/or remote SPAN ('RSPAN') features. SPAN mode operation allows traffic through any Fibre Channel interface of a network device to be replicated and delivered to a single port on the same network device. Ingress SPAN allows the monitoring of some or all packets that ingress a specified port or ports. Egress SPAN allows the monitoring of some or all packets that egress a specified port or ports. RSPAN allows the delivery of the replicated traffic to a port on a remote network device. Filtering may be applied, for example, to SPAN packets having selected virtual storage area network numbers.

Growing community of inventors

San Francisco, CA, United States of America

Gaurav Rastogi