Nicira, Inc.

Vmware, Inc.

Rick Lund

Jayant Jain

Anirban Sengupta

Mohan Parthasarathy

Serge Maskalik

Allwyn M Sequeira

Alok S Tiagi

Xinhua Hong

Raju Koganty

Russell Lu

Srinivas Nimmagadda

Mike Parsa

Ly Loi

Sushruth Gopal

Brenden Blanco

Kausum Kumar

Jingmin Zhou

Subrahmanyam Manuguri

Arnold Poon

Nishant Jain

A method of collecting health check metrics for a network is provided. The method, at a deep packet inspector on a physical host in a datacenter, receives a copy of a network packet from a load balancer. The packet includes a plurality of layers. Each layer corresponds to a communication protocol in a plurality of communication protocols. The method identifies an application referenced in the packet. The method analyzes the information in one or more layers of the packet to determine metrics for the source application. The method sends the determined metrics to the load balancer.

Automatic health check and performance monitoring for applications and protocols using deep packet inspection in a datacenter

Some embodiments provide a novel method for load balancing data messages that are sent by a source compute node (SCN) to one or more different groups of destination compute nodes (DCNs). In some embodiments, the method deploys a load balancer in the source compute node's egress datapath. This load balancer receives each data message sent from the source compute node, and determines whether the data message is addressed to one of the DCN groups for which the load balancer spreads the data traffic to balance the load across (e.g., data traffic directed to) the DCNs in the group. When the received data message is not addressed to one of the load balanced DCN groups, the load balancer forwards the received data message to its addressed destination. On the other hand, when the received data message is addressed to one of load balancer's DCN groups, the load balancer identifies a DCN in the addressed DCN group that should receive the data message, and directs the data message to the identified DCN. To direct the data message to the identified DCN, the load balancer in some embodiments changes the destination address (e.g., the destination IP address, destination port, destination MAC address, etc.) in the data message from the address of the identified DCN group to the address (e.g., the destination IP address) of the identified DCN.

Inline load balancing

In some embodiments, a method instantiates a proxy that stores first state information for first workloads running on a first computing device. The first computing device receives a migrated workload from a second computing device and second state information for a session associated with the migrated workload. The second state information is generated by a proxy on the second computing device that processed one or more packets for the migrated workload on the second computing device. The method stories the second state information for the proxy on the first computing device and resumes the session associated with the migrated workload using the proxy on the first computing device.

Distributed inline proxy

Some embodiments provide a method for performing services on a host computer that executes several machines in a datacenter. The method configures a first set of one or more service containers for a first machine executing on the host computer, and a second set of one or more service containers for a second machine executing on the host computer. Each configured service container performs a service operation (e.g., a middlebox service operation, such as firewall, load balancing, encryption, etc.) on data messages associated with a particular machine (e.g., on ingress and/or egress data messages to and/or from the particular machine). For each particular machine, the method also configures a module along the particular machine's datapath to identify a subset of service operations to perform on a set of data messages associated with the particular machine, and to direct the set of data messages to a set of service containers configured for the particular machine to perform the identified set of service operations on the set of data messages. In some embodiments, the first and second machines are part of one logical network or one virtual private cloud that is deployed over a common physical network in the datacenter.

Providing stateful services in a scalable manner for machines executing on host computers

Some embodiments provide a method for performing services on a host computer that executes several machines in a datacenter. The method configures a first set of one or more service containers for a first machine executing on the host computer, and a second set of one or more service containers for a second machine executing on the host computer. Each configured service container performs a service operation on data messages associated with a particular machine. For each particular machine, the method also configures a module along the particular machine's datapath to identify a subset of service operations to perform on a set of data messages associated with the particular machine, and to direct the set of data messages to a set of service containers configured for the particular machine to perform the identified set of service operations on the set of data messages.

The current document is directed to methods and systems that generate microsegmentation quotients for computational entities and components of a distributed-computer-system. In the described implementation, microsegmentation quotients are generated for each component, subsystem, or computational entity, collectively referred to as 'system entities,' of a set of specified system-entity types within the distributed computer system. Microsegmentation quotients are generated for system entities at any of the various hierarchical levels within a distributed computer system, including for the entire distributed computer system. Microsegmentation quotients are generated by an iterative process that refines initial estimates of the microsegmentation quotients for system entities within the distributed computer system. Microsegmentation quotients are displayed, through system-management interfaces, to administration and management personnel and provided to automated administration-and-management-system tools and facilities in order to facilitate analysis and monitoring of distributed-computer-system security as well as to facilitate rapid and accurate detection and amelioration of security-related deficiencies and problems.

Methods and systems that generate and use microsegmentation quotients for security monitoring of distributed-computer-system components

Some embodiments provide novel inline switches that distribute data messages from source compute nodes (SCNs) to different groups of destination service compute nodes (DSCNs). In some embodiments, the inline switches are deployed in the source compute nodes datapaths (e.g., egress datapath). The inline switches in some embodiments are service switches that (1) receive data messages from the SCNs, (2) identify service nodes in a service-node cluster for processing the data messages based on service policies that the switches implement, and (3) use tunnels to send the received data messages to their identified service nodes. Alternatively, or conjunctively, the inline service switches of some embodiments (1) identify service-nodes cluster for processing the data messages based on service policies that the switches implement, and (2) use tunnels to send the received data messages to the identified service-node clusters. The service-node clusters can perform the same service or can perform different services in some embodiments. This tunnel-based approach for distributing data messages to service nodes/clusters is advantageous for seamlessly implementing in a datacenter a cloud-based XaaS model (where XaaS stands for X as a service, and X stands for anything), in which any number of services are provided by service providers in the cloud.

Sticky service sessions in a datacenter

Some embodiments provide a novel method for collecting and analyzing attributes of data flows associated with machines executing on a plurality of host computers to detect anomalous behavior. In some embodiments, an anomalous behavior is detected for at least one particular flow associated with at least one machine executing on the host computer. In some embodiments, anomaly detection is based on the context data from the guest introspection agent and deep packet inspection. An identifier of the detected anomalous behavior is stored, in some embodiments. The stored attributes are provided, in some embodiments, to a server for further analysis.

Offloading anomaly detection from server to host

Some embodiments provide a novel method for collecting and reporting attributes of data flows associated with machines executing on a plurality of host computers to an analysis appliance. Each host computer, in some embodiments, is responsible for collecting and reporting attributes of data flows associated with machines executing on a host computer. In some embodiments, the host computer includes a flow exporter that processes and publishes flow data to the analysis appliance, a set of agents for collecting context data relating to the flows from machines executing on the host, a set of additional modules that provide additional context data, an anomaly detection engine that analyzes flow data and context data and provides additional context data, and a context exporter for processing and publishing context data to the analysis appliance.

Host-based flow aggregation

Tunnel-enabled elastic service model

Some embodiments provide a novel method for collecting and reporting attributes of data flows associated with machines executing on a plurality of host computers to an analysis appliance. The analysis appliance, in some embodiments, receives definitions of keys and provides them to the host computers. In some embodiments, existing keys are modified based on the analysis. Additionally, or alternatively, new keys are provided based on the analysis. In some embodiments, the analysis appliance receives the flow group records (e.g., sets of attributes) based on the keys and the configuration data from each host computer.

Dynamically providing keys to host for flow aggregation

Some embodiments provide a novel method for collecting and reporting attributes of data flows associated with machines executing on a plurality of host computers to an analysis appliance. Each host computer, in some embodiments, is responsible for collecting and reporting attributes of data flows associated with machines executing on a host computer. The host computer, in some embodiments, first eliminates duplicative flow group records and then aggregates the flow data according to a set of received keys that specify attributes that define the aggregation. For example, a simple key that specifies a set of machine identifiers (e.g., a VM ID) as attribute values will, for each machine identifier, aggregate all flows with that machine identifier into a single aggregated flow group record. In some embodiments, the host computer includes a flow exporter that processes and publishes flow data to the analysis appliance.

Growing community of inventors

Livermore, CA, United States of America

Rick Lund