Mechanism to automatically prioritize I/O for NFV workloads at platform overload

    公开(公告)号:US12020068B2

    公开(公告)日:2024-06-25

    申请号:US17022332

    申请日:2020-09-16

    Abstract: Methods to automatically prioritize input/output (I/O) for Network Function Virtualization (NFV) workloads at platform overload and associated apparatus and mechanisms. During lab or runtime workload operations, various platform telemetry data are collected and analyzed to determine whether a current workload is uncore-sensitive—that is, sensitive to operations involving utilization of the uncore circuitry such as I/O-related operations, memory bandwidth utilization, LLC utilization, network traffic, core-to-core traffic etc. For uncore sensitive workloads, upon detection of a platform overload condition such as a thermal load approaching a TDP limit, the uncore circuitry is prioritized over the core circuitry such that the frequency of the core is reduced first. A closed-loop feedback mechanism is used to adjust the frequencies of the core and uncore under various workload conditions. The mechanism enables I/O throughput to be maintained for NFV workloads, while reducing the processor thermal load.

    Hierarchical reinforcement learning algorithm for NFV server power management

    公开(公告)号:US12001932B2

    公开(公告)日:2024-06-04

    申请号:US16939237

    申请日:2020-07-27

    CPC classification number: G06N3/006 G06F1/3287 G06N5/04 G06N20/00

    Abstract: Methods and apparatus for hierarchical reinforcement learning (RL) algorithm for network function virtualization (NFV) server power management. A first RL model at a first layer is trained by adjusting a frequency of the core of processor while performing a workload to obtain a first trained RL model. The trained RL model is operated in an inference mode while training a second RL model at a second level in the RL hierarchy by adjusting a frequency of the core and a frequency of processor circuitry external to the core to obtain a second trained RL model. Training may be performed online or offline. The first and second RL models are operated in inference modes during online operations to adjust the frequency of the core and the frequency of the circuitry external to the core while executing software on the plurality of cores of to perform a workload, such as an NFV workload.

    TECHNOLOGIES FOR REORDERING NETWORK PACKETS ON EGRESS

    公开(公告)号:US20190044879A1

    公开(公告)日:2019-02-07

    申请号:US16023743

    申请日:2018-06-29

    Abstract: Technologies for reordering network packets on egress include a network interface controller (NIC) configured to associate a received network packet with a descriptor, generate a sequence identifier for the received network packet, and insert the generated sequence identifier into the associated descriptor. The NIC is further configured to determine whether the received network packet is to be transmitted from a compute device associated with the NIC to another compute device and insert, in response to a determination that the received network packet is to be transmitted to the another compute device, the descriptor into a transmission queue of descriptors. Additionally, the NIC is configured to transmit the network packet based on position of the descriptor in the transmission queue of descriptors based on the generated sequence identifier. Other embodiments are described herein.

    TECHNOLOGIES FOR POWER-AWARE SCHEDULING FOR NETWORK PACKET PROCESSING

    公开(公告)号:US20190042310A1

    公开(公告)日:2019-02-07

    申请号:US15951650

    申请日:2018-04-12

    Abstract: Technologies for power-aware scheduling include a computing device that receives network packets. The computing device classifies the network packets by priority level and then assigns each network packet to a performance group bin. The packets are assigned based on priority level and other performance criteria. The computing device schedules the network packets assigned to each performance group for processing by a processing engine such as a processor core. Network packets assigned to performance groups having a high priority level are scheduled for processing by processing engines with a high performance level. The computing device may select performance levels for processing engines based on processing workload of the network packets. The computing device may control the performance level of the processing engines, for example by controlling the frequency of processor cores. The processing workload may include packet encryption. Other embodiments are described and claimed.

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