Nefeli Networks, Inc.

Intel Corporation

Cloudflare, Inc.

Sylvia Ratnasamy

Daniele Di Proietto

Melvin Walls

Barath Raghavan

Kevin Roland Fall

David Naylor

Justine Sherry

Matthew Mussomele

Albert Christopher Torek

Nicholas Sun

Aurojit Panda

Salman Abdul Baset

Travis Duane Ewert

Allan Douglas Knies

Gianluca Iannaccone

Maziar H Manesh

Katerina Argyraki

Byung-Gon Chun

Norbert Egi

Mihai Dobrescu

Eugenia Corrales

Chris Torek

Kevin Fall

A method for anticipatory bidirectional packet steering involves receiving, by a first packet steering module of a network, a first encapsulated packet traveling in a forward traffic direction. The first encapsulated packet includes a first encapsulating data structure. The network includes two or more packet steering modules and two or more network nodes. Each of the packet steering modules includes a packet classifier module, a return path learning module, a flow policy table, and a replicated data structure (RDS). The return path learning module of the first packet steering module generates return traffic path information associated with the first encapsulated packet and based on the first encapsulating data structure. The first packet steering module updates the RDS using the return traffic path information and transmits the return traffic path information to one or more other packet steering modules.

Distributed anticipatory bidirectional packet steering for software network functions

A method for self-monitored universal scaling of software network functions involves receiving, at a switch of a network, one or more batches of data units. The network further includes one or more network function (NF) instances of an NF service, and a scaling controller. The switch transmits first data units to an NF instance of the NF service during a first time period. A first distribution associated with the NF instance is updated using the first transmitted data units. Upon determining that the updated distribution has changed such that a first measure of the first distribution is outside of a first confidence interval threshold, the first distribution is reinitialized. The switch transmits second data units to the NF instance during a second time period. The reinitialized first distribution is updated using the second transmitted data units to produce a second distribution associated with the NF instance.

Self-monitoring universal scaling controller for software network functions

A method for automating network function virtualization (NFV) using a modular NFV framework involves subscribing, by a control module of a network, to a key of a state store of the network. The state store includes stored data objects and unique keys. Each of the stored data objects is associated with one of the unique keys. The key is one of the unique keys. A notification is received at the control module from the state store. The notification is associated with the key. The control module reads a data object, associated with the key, from the stored data objects in the state store in response to the notification, and the control module modifies a network traffic flow of the network through two or more software network functions of the network based on the data object.

Modular system framework for software network function automation

A method for universal scaling of software network functions involves receiving, at a switch of a network, a batch of data units during a first period. The network further includes one or more network function (NF) instances of an NF service, and a scaling controller. The switch transmits one or more units of data during the first period to an NF instance of the NF service. An estimated maximum safe data unit rate is determined for the NF instance, and a representative safe data unit rate is determined for the NF service. A total number of data units designated to be received by the NF service during the first period is determined, and a total number of NF instances of the NF service to be provisioned in the network is determined at the scaling controller using the estimated total number of data units and the representative safe data unit rate.

Universal scaling controller for software network functions

A method for inline data plane monitor placement and operation for network function virtualization involves receiving, at one or more control modules of a network, a logical monitoring specification. The logical monitoring specification includes a monitoring regime abstracted from a physical topology of the network. The one or more control modules perform one or more of instantiating or identifying one or more monitor instances of the network based on the logical monitoring specification, the one or more monitor instances being or having been instantiated within one or more data plane components of the network. The one or more control modules configure the one or more monitor instances based on the logical monitoring specification.

Inline data plane monitor placement and operation for network function virtualization

Generally, this disclosure describes a scalable cluster router that includes a plurality of server-class computers interconnected together to form a router. Each server may be configured to independently schedule switching of packets to reduce the switch speed requirements on a per server basis. Each server may include a scheduler that independently load balances packet flows across servers of the cluster. Router capacity may be incrementally scaled by adding more servers, and router capacity may be increased by load balancing techniques within individual servers.

Scalable cluster router

Growing community of inventors

Berkeley, CA, United States of America

Sylvia Ratnasamy