Method, system and product to implement deterministic on-boarding and scheduling of virtualized workloads for edge computing

Patent metadata

Field	Value
Publication number	US-11159609-B2
Application number	US-202016833435-A
Country	US
Kind code	B2
Filing date	Mar 27, 2020
Priority date	Mar 27, 2020
Publication date	Oct 26, 2021
Grant date	Oct 26, 2021

Abstract

A non-transitory computer-readable storage medium, an apparatus, and a computer-implemented method. The computer-readable storage medium is of an edge computing system and is to identify a target edge node for deployment of a workload thereon. The computer-readable storage medium further comprises computer-readable instructions that, when executed, cause at least one processor to perform operations comprising: determining whether respective ones of candidate target edge nodes of a set of candidate target edge nodes of the edge computing system support workload determinism key performance indicators (KPIs) of the workload; in response to a determination that one or more candidate target edge nodes support the workload determinism KPIs, selecting a target edge node from the one or more candidate edge nodes; and causing the workload to be deployed at the target edge node.

First claim

What is claimed is:

1. A non-transitory computer-readable storage medium of an edge computing system to identify a target edge node for deployment of a workload thereon, the computer-readable storage medium comprising computer-readable instructions that, when executed, cause at least one processor to perform operations comprising:
determining whether respective ones of candidate target edge nodes of a set of candidate target edge nodes of the edge computing system support workload determinism key performance indicators (KPIs) of the workload;
in response to a determination that one or more candidate target edge nodes support the workload determinism KPIs, selecting a target edge node from the one or more candidate edge nodes;
causing the workload to be deployed at the target edge node; and
parsing one or more fields of a Virtualized Industrial Function Descriptor (VIFD) to determine the workload determinism KPIs.

2. The computer-readable storage medium of claim 1 , the operations further including comparing the workload determinism KPIs with respective overall edge node determinism metrics of the respective ones of the candidate target edge nodes to determine whether the respective ones of the candidate target edge nodes support the workload determinism KPIs.

3. The computer-readable storage medium of claim 1 , the operations further comprising calculating overall edge node determinism metrics for each of the respective ones of the candidate target edge nodes of the set of candidate target edge nodes, calculating the overall edge node determinism metrics being based on one or more edge node subsystem determinism metrics for said each of the respective ones of the candidate target edge nodes.

4. The computer-readable storage medium of claim 3 , wherein:
the one or more edge node subsystem determinism metrics for said each of the respective ones of the candidate target edge nodes include edge node subsystem determinism metrics d ij corresponding to:
a subsystem number i designating a subsystem type of said each of the respective ones of the candidate target edge nodes, where i is an integer equal to or greater than 1; and
a data point instance j of the one or more edge node subsystem determinism metrics for each subsystem number i, where j is an integer equal to or greater than 1 and;

the operations further include:
assigning a weight vector w i to subsystem determinism metrics of each subsystem number i;
for said each of the respective ones of the candidate target edge nodes, calculating a sum n j of the one or more edge node subsystem determinism metrics for each data point instance j and through all edge node subsystem determinism metrics i, wherein n j is given by:

n
j

=

Σ

i
=
1

⁢

w
i

⁢

d
ij

;

 and
performing regression on data points n j through data point instances j to determine the overall edge node determinism metrics as a function of time, d(t), for said each of the respective ones of the candidate target edge nodes.

5. The computer-readable storage medium of claim 3 , wherein the one or more edge node subsystem determinism metrics include at least one of: edge node compute subsystem determinism metrics, edge node network subsystem determinism metrics, edge node memory subsystem determinism metrics, or edge node network subsystem determinism metrics.

6. The computer-readable storage medium of claim 1 , the operations further comprising:
determining whether respective edge nodes of the edge computing system support workload computer resource requirements of the workload; and
in response to a determination that one or more of the respective edge nodes of the edge computing system support the workload compute resource requirements:
generating a node identification list for the one or more of the respective edge nodes of the edge computing system, the node identification list to identify the set of candidate target edge nodes; and
using the node identification list for the one or more of the respective edge nodes to determine whether respective ones of the candidate target edge nodes of the set of candidate target edge nodes support the workload determinism key performance indicators (KPIs).

7. The computer-readable storage medium of claim 6 , the operations further comprising:
determining compute resource requirements of the respective edge nodes from resource landscape data for the edge computing system; and
comparing the workload computer resource requirements with the compute resource requirements of the respective edge nodes to determine whether the respective edge nodes support the workload computer resource requirements.

8. The computer-readable storage medium of claim 4 , the operations further including:
in response to a determination that the one or more candidate target edge nodes include a single candidate target edge node, selecting the single candidate target edge node as the target edge node;
in response to a determination that the one or more candidate target edge nodes include a plurality of candidate target edge nodes:
applying a cost function to the plurality of candidate target edge nodes by determining a cost of deployment of the workload on each candidate target edge node of the plurality of candidate target edge nodes; and
selecting the target edge node based on determining the cost of deployment.

9. The computer-readable storage medium of claim 8 , the operations further including, in response to the determination that the one or more candidate target edge nodes include a plurality of candidate target edge nodes, selecting the target edge node as an edge node with a lowest cost of deployment.

10. The computer-readable storage medium of claim 8 , wherein selecting the target edge node includes:
determining that the workload is to be deployed in a redundant and synchronized configuration as a primary workload on a primary edge node and a secondary workload on a secondary edge node; and
based on a determination that the workload is to be deployed in a redundant and synchronized configuration, selecting, as the target edge node, the primary edge node and the secondary edge node from the plurality of candidate target edge nodes based on a rate of change of a last calculated datapoint instance of the overall edge node determinism metrics of each of the primary edge node and the secondary edge node.

11. The computer-readable storage medium of claim 10 , wherein the last calculated datapoint instance of the overall edge node determinism metrics of each of the primary edge node and the secondary edge node corresponds to a decision metric given by:
Decision Metric= d ′( t f )* n f

where d′(t f ) is a derivative of d(t f ), and d(t f ) is a value if d(t) at a last time instance t f at which d(t) has been determined.

12. The computer-readable storage medium of claim 10 , the operations further including implementing edge node state synchronization between the primary edge node and the secondary edge node by causing a storing of wor

Assignees

• Intel Corp

Inventors

• Mcgrath Michael J
• Healy Daire
• Lucero Christopher D
• Spoczynski Marcin

Classifications

• H04L67/1012

  based on compliance of requirements or conditions with available server resources · CPC title

• G06F9/5027 Primary

  the resource being a machine, e.g. CPUs, Servers, Terminals · CPC title

• Y02D10/00

  Energy efficient computing, e.g. low power processors, power management or thermal management · CPC title

• H04L67/1023

  based on a hash applied to IP addresses or costs · CPC title

• G16Y40/35

  Management of things, i.e. controlling in accordance with a policy or in order to achieve specified objectives · CPC title