An interface may be provided between i) a selective forwarding unit (SFU) configured to, in real-time, receive a data stream from a sender via a first network link of a communication network and selectively forward the data stream to one or more receivers via respective second network links, and ii) one or more core network functions (PCF, PCRF, NSMF, CSMF) for establishing service guarantees for data flows in the communication network. In a specific example, the interface may be established as a network function (SMGF) which translates streaming requirements for one-to-many flows coming from WebRTC SFUs into appropriate QoS/network slice configurations, such that the quality of RTC flows may be increased. Accordingly, negative side-effects of conservative congestion control algorithms in WebRTC clients and static/overprovisioned QoS at network operators may be overcome.

In one embodiment, a method includes monitoring, by a control loop including a processor and a memory, a first environment. The control loop includes one or more predetermined control loop parameters. The method also includes receiving, by the control loop and in response to monitoring the first environment, first data from the first environment and receiving, by the control loop, information from an adaptation control loop. The method also includes determining, by the control loop, to automatically adjust at least one of the one or more predetermined control loop parameters based at least in part on the information received from the adaptation control loop and automatically adjusting, by the control loop, the one or more predetermined control loop parameters. The method further includes determining, by the control loop, to initiate an action based on the first data collected from the first environment and the one or more adjusted control loop parameters.

Methods to strengthen the cyber-security and privacy in a proposed deterministic Internet of Things (IoT) network are described. The proposed deterministic IoT consists of a network of simple deterministic packet switches under the control of a low-complexity ‘Software Defined Networking’ (SDN) control-plane. The network can transport ‘Deterministic Traffic Flows’ (DTFs), where each DTF has a source node, a destination node, a fixed path through the network, and a deterministic or guaranteed rate of transmission. The SDN control-plane can configure millions of distinct interference-free ‘Deterministic Virtual Networks’ (DVNs) into the IoT, where each DVN is a collection of interference-free DTFs. The SDN control-plane can configure each deterministic packet switch to store several deterministic periodic schedules, defined for a scheduling-frame which comprises F time-slots. The schedules of a network determine which DTFs are authorized to transmit data over each fiber-optic link of the network. These schedules also ensure that each DTF will receive a deterministic rate of transmission through every switch it traverses, with full immunity to congestion, interference and Denial-of-Service (DoS) attacks. Any unauthorized transmissions by a cyber-attacker can also be detected quickly, since the schedules also identify unauthorized transmissions. Each source node and destination node of a DTF, and optionally each switch in the network, can have a low-complexity private-key encryption/decryption unit. The SDN control-plane can configure the source and destination nodes of a DTF, and optionally the switches in the network, to encrypt and decrypt the packets of a DTF using these low-complexity encryption/decryption units. To strengthen security and privacy and to lower the energy use, the private keys can be very large, for example several thousands of bits. The SDN control-plane can configure each DTF to achieve a desired level of security well beyond what is possible with existing schemes such as AES, by using very long keys. The encryption/decryption units also use a new serial permutation unit the very low hardware cost, which allows for exceptional security and very-high throughputs in FPGA hardware.

Various example embodiments for supporting scalable deterministic services in packet networks are presented. Various example embodiments for supporting scalable deterministic services in packet networks may be configured to support delay guarantees (e.g., finite end-to-end delay bounds) for a class of traffic flows referred to as guaranteed-delay (GD) traffic flows. Various example embodiments for supporting scalable deterministic services in packet networks may be configured to support delay guarantees for GD traffic flows of a network based on a queuing arrangement that is based on network outputs of the network, a packet scheduling method that is configured to support scheduling of packets of the GD traffic flows, and a service rate allocation rule configured to support delay guarantees for the GD traffic flows.

Methods and systems for managing data transmissions are disclosed. An example method can comprise determining a plurality of time allocations for a time cycle. The plurality of time allocations can comprise a first time allocation which can be determined based on an information rate, a committed information rate, an excess information rate, an effective bandwidth rate, other factors, or a combination thereof. Data can be received from multiple sources into a buffer, for example, and can be processed within a time cycle if processing the data will not exceed the time allocation.

Technologies for managing burst bandwidth requirements are disclosed. In the illustrative embodiment, a software-defined network (SDN) controller monitors storage devices in a data center. If a storage device fails, the SDN controller manages the bandwidth used to replicate the data that was stored on the failed storage device. The SDN controller may allocate an initial amount of bandwidth based on one or more parameters of the storage device, and the SDN controller may increase the bandwidth in a series of discrete steps. In another embodiment, the SDN controller may predict a bandwidth burst based on sequential writes at a storage sled from several compute devices, and allocate bandwidth accordingly in a tiered manner.

Some embodiments provide a method for a hardware forwarding element that includes multiple queues. The method receives a packet at a multi-stage processing pipeline of the hardware forwarding element. The method determines, at one of the stages of the processing pipeline, to modify a setting of a particular one of the queues. The method stores an identifier for the particular queue and instructions to modify the queue setting with data passed through the processing pipeline for the packet. The stored information is subsequently used by the hardware forwarding element to modify the queue setting.

Systems and methods are disclosed for enhancing network performance by using modified traffic control (e.g., rate limiting and/or scheduling) techniques to control a rate of packet (e.g., data packet) traffic to a queue scheduled by a Quality of Service (QoS) engine for reading and transmission. In particular, the QoS engine schedules packets using estimated packet sizes before an actual packet size is known by a direct memory access (DMA) engine coupled to the QoS engine. The QoS engine subsequently compensates for discrepancies between the estimated packet sizes and actual packet sizes (e.g., when the DMA engine has received an actual packet size of the scheduled packet). Using these modified traffic control techniques that leverage estimating packet sizes may reduce and/or eliminate latency introduced due to determining actual packet sizes.

Some embodiments provide a method for a hardware forwarding element that includes multiple queues. The method receives a packet at a multi-stage processing pipeline of the hardware forwarding element. The method determines, at one of the stages of the processing pipeline, to modify a setting of a particular one of the queues. The method stores an identifier for the particular queue and instructions to modify the queue setting with data passed through the processing pipeline for the packet. The stored information is subsequently used by the hardware forwarding element to modify the queue setting.

Systems and methods are disclosed for enhancing network performance by using modified traffic control (e.g., rate limiting and/or scheduling) techniques to control a rate of packet (e.g., data packet) traffic to a queue scheduled by a Quality of Service (QoS) engine for reading and transmission. In particular, the QoS engine schedules packets using estimated packet sizes before an actual packet size is known by a direct memory access (DMA) engine coupled to the QoS engine. The QoS engine subsequently compensates for discrepancies between the estimated packet sizes and actual packet sizes (e.g., when the DMA engine has received an actual packet size of the scheduled packet). Using these modified traffic control techniques that leverage estimating packet sizes may reduce and/or eliminate latency introduced due to determining actual packet sizes.