Intel Corporation

Giovani Estrada

Joseph Butler

Victor Bayon-Molino

Alexander J Leckey

Thijs Metsch

Michael J McGrath

John Kennedy

Katalin Klara Bartfai-Walcott

Slawomir Putyrski

Vincenzo Mario Riccobene

Michael J Nolan

Keith A Ellis

Annie Ibrahim Rana

David Coates

Chris Woods

Niall Cahill

Connor Upton

Michael Christopher Woods

Michael J Mcgrath

Methods, apparatus, systems and articles of manufacture to improve accuracy of a fog/edge-based classifier system are disclosed. An example apparatus includes a transducer to mounted on a tracked object, the transducer to generate data samples corresponding to the tracked object; a discriminator to: generate a first classification using a first model based on a first calculated feature of the first data samples from the transducer, the first model corresponding to calculated features determined from second data samples, the second data samples obtained prior to the first data samples; generate an offset based on a difference between a first model feature the first model and a second model feature of a second model, the second model being different than the first model; and adjust the first calculated feature using the offset to generate an adjusted feature; a pattern matching engine to generate a second classification using vectors corresponding to the second model based on the adjusted feature; and a counter to, when the first classification matches the second classification, increment a count.

Methods and apparatus to improve accuracy of edge and/or a fog-based classification

Methods, systems, and storage media for telemetry adaptation are disclosed herein. In an embodiment, a networking device may include a data collector agent module to receive measurement data from measurement sources according to an initial telemetry policy and to provide the measurement data to the one or more servers of the monitoring system. The networking device may include an anomaly detection module to receive measurement data from the data collector agent module, to detect an anomaly in the measurement data, and to provide an indication of the anomaly to the data collector agent module for the data collector agent module to provide a first modified telemetry policy for the measurement sources.

Telemetry adaptation

Various systems and methods for implementing automatic model generation for performance monitoring are described herein. A performance monitoring system includes a model manager to: identify a performance model that predicts performance of an operational node, the performance model based on telemetry data from the operational node; and implement an automatic verification operation to analyze the performance model and revise the performance model when the performance model is no longer valid; and an event processor to: initiate a remedial action at the operational node when the performance model indicates an alert state.

Automatic model-based computing environment performance monitoring

Methods, apparatus, systems, and articles of manufacture for conditional classifier chaining in a constrained machine learning environment are disclosed. An example apparatus includes a classification controller to select a first model to be utilized to classify a first feature identified from sensor data. A memory controller is to copy the first model to a memory. A machine learning processor is to apply the first model to the first feature to create a first classification output, the first classification output indicating an identified class. The classification controller is to, in response to a determination that the first classification output identifies a second model to be used for classification, instruct the memory controller to load the second model into the memory. The machine learning processor is to apply the second model to the second feature to create a second classification output.

Methods and apparatus for conditional classifier chaining in a constrained machine learning environment

Technologies for provisioning jobs to servers at a datacenter include a compute device to identify a class of each of a plurality of available servers in the datacenter, wherein the class is indicative of a set of hardware included on the corresponding server. The compute device selects a workload that includes a plurality of jobs to provision to a subset of the available servers and determines a provisioning configuration for the workload from a set of prospective configurations as a function of a rack utilization of each prospective configuration, the classes utilized by each prospective configuration, and a number of racks utilized by each prospective configuration. The provisioning configuration identifies a subset of the available servers to which to provision the jobs of the workload.

Technologies for execution acceleration in heterogeneous environments

Technologies for datacenter management include one or more computing racks each including a rack controller. The rack controller may receive system, performance, or health metrics for the components of the computing rack. The rack controller generates regression models to predict component lifespan and may predict logical machine lifespans based on the lifespan of the included hardware components. The rack controller may generate notifications or schedule maintenance sessions based on remaining component or logical machine lifespans. The rack controller may compose logical machines using components having similar remaining lifespans. In some embodiments the rack controller may validate a service level agreement prior to executing an application based on the probability of component failure. A management interface may generate an interactive visualization of the system state and optimize the datacenter schedule based on optimization rules derived from human input in response to the visualization. Other embodiments are described and claimed.

Workload optimization, scheduling, and placement for rack-scale architecture computing systems

One embodiment provides an apparatus. The apparatus includes ranker logic. The ranker logic is to rank each of a plurality of compute nodes in a data center based, at least in part, on a respective node score. Each node score is determined based, at least in part, on a utilization (U), a saturation parameter (S) and a capacity factor (C). The capacity factor is determined based, at least in part, on a sold capacity (C) related to the compute node. The ranker logic is further to select one compute node with a highest node score for placement of a received workload.

Ranking system

A system comprises a scoring engine comprising at least one processor and memory. The scoring engine is to generate, based on telemetry information obtained from a plurality of nodes of a computing infrastructure, a first availability score for a first node of the plurality of computing infrastructure nodes and a second availability score for a second node of the plurality of computing infrastructure nodes. The scoring engine is further to generate, based on the first availability score of the first computing infrastructure node and the second availability score of the second computing infrastructure node, an edge tension score for a link between the first node and the second node.

Computing infrastructure optimizations based on tension levels between computing infrastructure nodes

Examples may include techniques for allocating configurable computing resources from a pool of configurable computing resources to a logical server or virtual machine. The logical server or virtual machine may use allocated configurable computing resources to implement, execute or run a workload.

Techniques to allocate configurable computing resources

Examples are described for computing resource discovery and management for a system of configurable computing resources that may include disaggregate physical elements such as central processing units, storage devices, memory devices, network input/output devices or network switches. In some examples, these disaggregate physical elements may be located within one or more racks of a data center.

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Dublin, Ireland

Giovani Estrada