Vmware, Inc.

Leah Nutman

Paul Pedersen

Keshav Mathur

Darren Brown

Junyuan Lin

Peng Gao

Xing Wang

Wei Ey Li

Rachil Chandran

Jinyi Lu

Lalit Jain

James Ang

Yu P Sun

Harutyun Beybutyan

Sandy Lau

Sergio Nakai

Kien Chiew Wong

Parikshit Santhana Gopalan Gopalan

Various examples are disclosed for transitioning usage forecasting in a computing environment. Usage of computing resources of a computing environment are forecasted using a first forecasting data model and usage measurements obtained from the computing resources. A use of the first forecasting data model in forecasting the usage is transitioned to a second forecasting data model without incurring downtime in the computing environment. After the transition, the usage of the computing resources of the computing environment is forecasted using the second forecasting data model and the usage measurements obtained from the computing resources. The second forecasting data model exponentially decays the usage measurements based on a respective time period at which the usage measurements were obtained.

Exponential decay real-time capacity planning

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

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

Various examples are disclosed for forecasting resource usage and computing capacity utilizing an exponential decay. In some examples, a computing environment can obtain usage measurements from a data stream over a time interval, where the usage measurements describe utilization of computing resource. The computing environment can generate a weight function for individual ones of the usage measurements, where the weight function exponentially decays the usage measurements based on a respective time period at which the usage measurements were obtained. The computing environment can forecast a future capacity of the computing resources based on the usage measurements and the weight function assigned to the individual ones of the usage measurements. The computing environment can further upgrade a forecast engine to use the exponential decay without resetting the forecast engine or its memory.

Various examples are disclosed for workload placement using forecast data. Forecast data for workloads and providers during a predefined period of time in the future is considered when identifying stressed providers and the feasibility of a workload move. Workloads with demand spikes at different future times can be matched by stacking current demand and forecast demand by timestamps. The possibility of stress can be reduced by making moves preemptively and considering forecast demand when evaluating the feasibility of a workload move.

Workload placement with forecast

Computational methods and systems that proactively manage usage of computational resources of a distributed computing system are described. A sequence of metric data representing usage of a resource is detrended to obtain a sequence of non-trendy metric data. Stochastic process models, a pulse wave model and a seasonal model of the sequence of non-trendy metric data are computed. When a forecast request is received, a sequence of forecasted metric data is computed over a forecast interval based on the estimated trend and one of the pulse wave or seasonal model that matches the periodicity of the sequence of non-trendy metric data. Alternatively, the sequence of forecasted metric data is computed based on the estimated trend and the stochastic process model with a smallest accumulated residual error. Usage of the resource by virtual objects of the distributed computing system may be adjusted based on the sequence of forecasted metric data.

Methods and systems to proactively manage usage of computational resources of a distributed computing system

The current document is directed to methods and systems that collect metric data within computing facilities, including large data centers and cloud-computing facilities. In a described implementation, input metric data is compressed by replacing each metric data point with a one-bit, two-bit, four-bit, or eight-bit compressed data value. During a first time window following reception of a metric data point, the metric data point remains available in uncompressed form to facilitate data analysis and monitoring functionalities that use uncompressed metric data. During a second time window, the metric data point is compressed and stored in memory, where the compressed data point remains available for data analysis and monitoring functionalities that use compressed metric data for detection of peaks, periodic patterns, and other characteristics. Finally, the compressed data point is archived in mass storage, where it remains available to data-analysis and management functionalities for a lengthy time period.

Growing community of inventors

Palo Alto, CA, United States of America

Leah Nutman