International Business Machines Corporation

Lynn M Koch

Christopher J Dawson

Clifford V Harris

Brendan F Coffey

Gene L Brown

Gregory J Boss

Tedrick Neal Northway

Randy Scott Johnson

Jason L Anderson

Daniel Everett Jemiolo

Timothy R Echtenkamp

Edward H Anderson, Iii

David A Romero

Embodiments of the present invention provide an approach for forecasting a capacity available for processing a workload in a networked computing environment (e.g., a cloud computing environment). Specifically, aspects of the present invention provide service availability for cloud subscribers by forecasting the capacity available for running or scheduled applications in a networked computing environment. In one embodiment, capacity data may be collected and analyzed in real-time from a set of cloud service providers and/or peer cloud-based systems. In order to further increase forecast accuracy, historical data and forecast output may be post-processed. Data may be post-processed in a substantially continuous manner so as to assess the accuracy of previous forecasts. By factoring in actual capacity data collected after a forecast, and taking into account applications requirements as well as other factors, substantially continuous calibration of the algorithm can occur so as to improve the accuracy of future forecasts and enable functioning in a self-learning (e.g., heuristic) mode.

Forecasting capacity available for processing workloads in a networked computing environment

Embodiments of the present invention provide an approach for identifying optimal virtual machine (VM) images in a networked computing environment (e.g., a cloud computing environment). Specifically, in a typical embodiment, a set of system requirements, a profile, and a performance state of the networked computing environment are received as input and analyzed against a library of VM images. Based on the analysis, a set of VM images having software programs (e.g., also referred to herein as a software stack) capable of accommodating requirements defined by the input is identified. A requester can select one or more of the identified VM images, which can then be provisioned/deployed accordingly.

Identifying optimal virtual machine images in a networked computing environment

A transition costing program (TCP) uses standardized activity tasks (SAT), standard activity estimates (SAE) for each SAT, a transition costing estimator (TCE) to develop full time equivalent (FTE) values for transitioning a customer. The TCP functions in an engagement phase, a boarding phase, and end cost variance (ECV) analysis phase so that feedback is provided to validate or modify the SAE. In the engagement phase, the TCP selects the activities necessary to transition the customer from an SAT library and enters the SAT selections into the TCE. As the SAT selections are entered, the TCE populates a display with corresponding SAE value for each task. During transition, costs are monitored by SAE, the transition is completed and the actual costs for each task are compared with the SAE value for each SAT. When the comparison shows a variance, the TCP analyzes the variance and determines whether to modify the SAE value for the SAT under review.

System and method for estimating transition costs

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Tucson, AZ, United States of America

Lynn M Koch