Vmware, Inc.

Rachil Chandran

Karthik Seshadri

Siddartha Laxman Karibhimanvar

Shrisha Chandrashekar

Akash Srivastava

Sivaraj M

Leah Nutman

Keshav Mathur

Tyler J Curtis

Aayush Asawa

Radhakrishnan Devarajan

Lalit Jain

James Ang

Chandrashekhar Jha

Ritesh Ranjan Jha

Yash Bhatnagar

Rajat Garg

Gurudutt Maiya Belur

Samuel P McBride

Harutyun Beybutyan

Kien Chiew Wong

Methods, apparatus, systems and articles of manufacture to establish a data pipeline between cloud computing platforms. An apparatus includes at least one memory, machine readable instructions in the apparatus, and processor circuitry to execute the machine readable instructions to at least extract a data producer name from data, the data to be provided from a data producer to a data consumer, identify a buffer identifier based on a mapping of the data producer name to the buffer identifier, cause transmission of the data to a buffer associated with the buffer identifier, and cause transmission of the data from the buffer to the data consumer based on an association between the buffer identifier and a data consumer name, the data consumer name corresponding to the data consumer.

Methods and apparatus for data pipelines between cloud computing platforms

Methods, apparatus, systems and articles of manufacture to provide a custom installable open virtualization application file for on-premise installation via the cloud are disclosed. Example instructions cause one or more processors to determine a sizing factor based on a response from a user device; determine customized resource information for an agent in a private cloud network using an inventory count algorithm based on the sizing factor; modify an open virtualization appliance (OVA) file to include the customized resource information, the OVA file being deployed in a public cloud network; and transmit an indication to a location of the modified OVA file to the user device, the location of the modified OVA file being the same location as the OVA file.

Methods and apparatus to provide a custom installable open virtualization application file for on-premise installation via the cloud

An example apparatus comprises at least one memory, instructions in the apparatus, and at least one processor to execute the instructions to identify an agent identifier from a plurality of network command responses, the plurality of network command responses including a plurality of network parameters, apply weighted values to the plurality of network parameters, and determine a total weighted sum of the plurality of network parameters, the total weighted sum corresponding to a connectivity factor of an agent associated with the agent identifier.

Methods and apparatus for determining low latency network connectivity in computing systems

Disclosed are various embodiments for automating the prediction of workload tenures in datacenter environments. In some embodiments, parameters are identified for a plurality of workloads of a software defined data center. A machine learning model is trained to determine a predicted tenure based on parameters of the workloads. A workload for the software defined data center is configured to include at least one workload parameter. The workload is processed using the trained machine learning model to determine the predicted tenure. An input to the machine learning model includes the at least one workload parameter.

Workload tenure prediction for capacity planning

System and method for virtual agent management in a cloud architecture deploys multiple virtual appliances with proxy services in the cloud architecture, instantiates virtual agents in the virtual appliances, communicates with a cloud framework using the proxy services, and controls the virtual agents based on communications with the cloud framework.

Method and apparatus for virtual agent management in cloud computing

Methods, apparatus, systems and articles of manufacture are disclosed to establish a data pipeline between cloud computing platforms. An apparatus includes a producer registration controller to register a data producer with a data pipeline service in a public cloud network, the data producer associated with a private cloud network, a consumer registration controller to register a data consumer with the data pipeline service, and a communication controller to, in response to the registration of the data consumer, transmit data generated by the public cloud network from the data consumer to the data buffer via a first data plane gateway, and, in response to a validation of the data consumer, transmit the data from the data buffer to the data consumer via a second data plane gateway, the first data plane gateway different from the second data plane gateway.

An example apparatus comprises a variable determiner to: parse a plurality of network command responses from a first data collector agent and from a second data collector agent; initialize a value for network connectivity parameters corresponding to the network command responses, the value corresponding to the parsed plurality of network command responses; and assign weighted values to the network connectivity parameters; a connectivity analyzer to determine a first network connectivity factor for the first data collector agent and a second network connectivity factor for the second data collector agent; and a recommender system to: determine whether the first network connectivity factor is a smaller value than the second network connectivity factor; and when the first network connectivity factor is the smaller value, initiate the first data collector agent corresponding to the first network connectivity factor to begin collecting data.

Computational methods and systems that estimate time remaining and right size for usable capacities of resources used to run virtual objects of a distributed computing system are described. For each stream of metric data that represents usage of a resource of a distributed computing system, a model for forecasting metric data is determined and used to compute forecasted metric data in a forecast interval. A resource utilization metric is computed from the forecasted metric data and may be used to estimate a time remaining before the usable capacity of the resource is expected to be insufficient and the resource usable capacity is adjusted. The resource utilization metric may be used to determine the capacity remaining is insufficient. A right-size usable capacity for the resource is computed based on the resource utilization metric and the usable capacity of the resource is adjusted to at least the right-size usable capacity.

Methods and systems for estimating time remaining and right sizing usable capacities of resources of a distributed computing system

Methods, apparatus, systems and articles of manufacture to provide a custom installable open virtualization application file for on-premise installation via the cloud are disclosed. An example apparatus includes a resource processor to determine a resource capacity for an agent in a private cloud network; a file manipulator to modify an open virtualization appliance (OVA) file by modifying a descriptor file of the OVA file to configure the resource capacity for the agent in the private cloud network, the OVA file being deployed in a public cloud network; and a first interface to transmit an indication to a location of the modified OVA file to a user device, the location of the modified OVA file being the same location as the OVA file.

Computational methods and systems to reclaim capacity of a virtual infrastructure of distributed computing system are described. Methods and systems are directed to forecasting usage of resources that form a virtual infrastructure of a distributed computing system. Streams of metric data that represent usage of resources of the virtual infrastructure assigned to a virtual object are collected. A binary sequence of active status metric data is computed for the virtual object based on the streams of metric data. Forecasted active status metric data are computed in a forecast interval based on the sequence of active status metric data. Expected active or inactive status of virtual object over the forecast interval is determined from the forecasted active status metric data. If the virtual object is expected to inactive status over the forecast interval, resources assigned to the virtual object are reclaimed for use by active virtual objects.

Methods and systems to reclaim capacity of unused resources of a distributed computing system

A capacity-analysis tool (CAT) provides a model framework for creating a model of a capacity-planning-target (CPT) system, e.g., a data center. The tool includes a model framework that, in turn, includes a closed-system template for creating CSMs, i.e., models of capacity-limited systems. A user uses the CAT to create CPT models using the CSMs as building blocks. A machine-learning engine is used to train the CPT model, converting parameter time-series data to functions of time. The trained CPT models are then used to make capacity-planning estimates, e.g., time remaining on a system before usage matches capacity. The CAT makes it easy to extend a model, e.g., by adding new dimensions (new factors of interest) in the form of new CSMs to which the new dimensions have been assigned.

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Bengaluru, India

Rachil Chandran