Intel Corporation

Trevor Cooper

Francesc Guim Bernat

Ned M Smith

Scott P Dubal

Stephen T Palermo

Jesse C Brandeburg

Kshitij Arun Doshi

Kapil Sood

Jr-Shian Tsai

Suraj Prabhakaran

Evan Custodio

Iosif Gasparakis

Karthik Kumar

Andrew J Herdrich

Timothy Verrall

Alexander Bachmutsky

Raghu R Kondapalli

Anjali Singhai Jain

Patrick G Kutch

Nilesh K Jain

Petar Torre

Pradeepsunder Ganesh

Mike Bursell

Rajesh Poornachandran

Thomas Willhalm

Parthasarathy Sarangam

Sanjay Bakshi

Michael Hingston McLaughlin Bursell

Mona Vij

Edwin Verplanke

Brian J Skerry

Brinda Ganesh

Ramanathan Sethuraman

Marcin Spoczynski

Adrian C Hoban

Alexander Vul

Ravi Iyer

Brian A Keating

Ignacio Astilleros Diez

Ben McCahill

Parthasarathy Sangam

Paul O'Neill

Kevin Bohan

Methods and apparatus for scale out hardware-assisted tracing schemes for distributed and scale-out applications. In connection with execution of one or more applications using a distributed processing environment including multiple compute nodes, telemetry and tracing data are obtained using hardware-based logic on the compute nodes. Processes associated with applications are identified, as well as the compute nodes on which instances of the processes are executed. Process instances are associated with process application space identifiers (PASIDs), while processes used for an application are associating with a global group identifier (GGID) that serves as an application ID. The PASIDs and GGIDs are used to store telemetry and/or tracing data on the compute nodes and/or forward such data to a tracing server in a manner that enables telemetry and/or tracing data to be aggregated on an application basis. Telemetry and/or tracing data may be obtained from processors on the compute nodes, and (optionally) additional elements such as network interface controllers (NICs). Tracing data may also be obtained from switches used for forwarding data between processes.

Hardware-assisted tracing schemes for distributed and scale-out applications

Technologies for collecting metrics associated with a processing resource (e.g., central processing unit (CPU) resources, accelerator device resources, and the like) over a time window are disclosed. According to an embodiment presented herein, a network device receives, in an edge network, a request to provide one or more metrics associated with a processing resource, the request specifying a window indicative of a time period to capture the one or more metrics. The network device obtains the one or more metrics from the processing resource for the specified window and provides the obtained one or more metrics in response to the request.

Technologies for capturing processing resource metrics as a function of time

Technologies for migrating data between edge accelerators hosted on different edge locations include a device hosted on a present edge location. The device includes one or more processors to: receive a workload from a requesting device, determine one or more accelerator devices hosted on the present edge location to perform the workload, and transmit the workload to the one or more accelerator devices to process the workload. The one or more processor is further to determine whether to perform data migration from the one or more accelerator devices to one or more different edge accelerator devices hosted on a different edge location, and send, in response to a determination to perform the data migration, a request to the one or more accelerator devices on the present edge location for transformed workload data to be processed by the one or more different edge accelerator devices.

Technologies for data migration between edge accelerators hosted on different edge locations

Methods, apparatus, systems and articles of manufacture to determine provenance for data supply chains are disclosed. Example instructions cause a machine to at least, in response to data being generated, generate a local data object and object metadata corresponding to the data; hash the local data object; generate a hash of a label of the local data object; generate a hierarchical data structure for the data including the hash of the local data object and the hash of the label of the local data object; generate a data supply chain object including the hierarchical data structure; and transmit the data and the data supply chain object to a device that requested access to the data.

Methods and apparatus to determine provenance for data supply chains

Technologies for managing telemetry and sensor data on an edge networking platform are disclosed. According to one embodiment disclosed herein, a device monitors telemetry data associated with multiple services provided in the edge networking platform. The device identifies, for each of the services and as a function of the associated telemetry data, one or more service telemetry patterns. The device generates a profile including the identified service telemetry patterns.

Technologies for managing sensor and telemetry data on an edge networking platform

Technologies for monitoring service level agreement (SLA) performance in an end-to-end SLA monitoring architecture include a network functions virtualization (NFV) SLA controller configured to manage SLA agents initialized in various network processing components of the end-to-end SLA monitoring architecture. To do so, the NFV SLA controller is configured to provide instruction to the SLA agents indicating which types of telemetry data to monitor and receive the requested telemetry data, as securely collected and securely packaged by the SLA agents. The NFV SLA controller is further configured to securely analyze the received telemetry data to determine one or more performance metrics and compare performance benchmarks against the performance metrics to generate an SLA report that includes the results of the comparison. Other embodiments are described and claimed.

Technologies for independent service level agreement monitoring

Methods, software, and apparatus for implementing local service chaining (LSC) with virtual machines (VMs) or virtualized containers in Software Defined Networking (SDN). In one aspect a method is implemented on a compute platform including a plurality of VMs or containers, each including a virtual network interface controller (vNIC) communicatively coupled to a virtual switch in an SDN. LSCs are implemented via a plurality of virtual network appliances hosted by the plurality of VMs or containers. Each LCS comprises a sequence (chain) of services performed by virtual network appliances defined for the LSC. In connection with performing the chain of services, packet data is forwarded between VMs or containers using a cut-through mechanisms under which packet data is directly written to receive (Rx) buffers on the vNICs in a manner that bypasses the virtual switch. LSC indicia (e.g., through LSC tags) and flow tables are used to inform each virtual network appliance and/or or its host VM or container of the next vNIC Rx buffer or Rx port to which packet data is to be written.

Local service chaining with virtual machines and virtualized containers in software defined networking

Methods and apparatus for accelerating VM-to-VM Network Traffic using CPU cache. A virtual queue manager (VQM) manages data that is to be kept in VM-VM shared data buffers in CPU cache. The VQM stores a list of VM-VM allow entries identifying data transfers between VMs that may use VM-VM cache 'fast-path' forwarding. Packets are sent from VMs to the VQM for forwarding to destination VMs. Indicia in the packets (e.g., in a tag or header) is inspected to determine whether a packet is to be forwarded via a VM-VM cache fast path or be forwarded via a virtual switch. The VQM determines the VM data already in the CPU cache domain while concurrently coordinating with the data to and from the external shared memory, and also ensures data coherency between data kept in cache and that which is kept in shared memory.

Method and apparatus for accelerating VM-to-VM network traffic using CPU cache

The present disclosure is directed to enhanced virtual function capabilities in a virtualized network environment. In general, devices may comprise physical and virtualized resources. The physical resources may comprise at least a network adaptor that may handle incoming data from a network and outgoing data to the network. The virtualized resources may comprise at least one virtual machine (VM) and a corresponding interface. The corresponding interface may be one of a physical interface, a virtual interface or a 'super' virtual interface. The physical interface may provide a first set of capabilities allowing the VM to access (e.g., control) at least the network adaptor. The virtual interface may provide a second set of capabilities that is a subset of the first set. The super virtual interface may provide a third set of capabilities including the second set of capabilities and at least one additional capability from the first set of capabilities.

Enhanced virtual function capabilities in a virtualized network environment

Growing community of inventors

Portland, OR, United States of America

Trevor Cooper