International Business Machines Corporation

Lenovo (singapore) Pte. Ltd.

Karthick Rajamani

Charles Robert Lefurgy

Malcolm Scott Allen-Ware

Freeman Leigh Rawson, Iii

Malcolm Scott Ware

Juan C Rubio

Guillermo Jesus Silva

Gregory Scott Still

Todd Jon Rosedahl

Michael Stephen Floyd

John Bruce Carter

Heather Lynn Hanson

Wesley Michael Felter

Alan J Drake

Wael R El-Essawy

Thomas W Keller, Jr

Eric Eugene Retter

Jeffrey A Stuecheli

Tilman Gloekler

Alexandre Peixoto Ferreira

Timothy Glen Hallett

Cong Xu

John Steven Dodson

Lixin Zhang

William Evan Speight

Victor V Zyuban

Bishop Chapman Brock

Thomas Walter Keller

Naresh Nayar

Eric Rozner

Elmootazbellah Nabil Elnozahy

Jordan Ross Keuseman

Soraya Ghiasi

Shawn P Authement

Junaid Khalid

Amos A Omokpo

John Charles Elliott

Wei T Huang

Srinivasan Ramani

David Blair Whitworth

Guy L Guthrie

Bruce Gerard Mealey

Randal Craig Swanberg

Gary A Van Huben

Kenneth Lee Wright

Richard Loise Arndt

Venkat Rajeev Indukuru

Michael Alan Schappert

Scot H Rider

Francis Patrick O'Connell

Irving G Baysah

Christopher Francois

Madhusudan K Iyengar

Kirk David Peterson

Brian William Thompto

Kanak Behari Agarwal

Ramakrishnan Rajamony

Jente Benedict Kuang

Saravanan Sethuraman

Gary Dale Carpenter

Hillery C Hunter

Barry P Linder

Ahmed Gheith

Jian Li

Patrick Kevin Egan

Ronald Jay Bolam

Janani Mukundan

Steven W Mittl

Joshua D Friedrich

Daniel Dean Heimsoth

Kshitij Sudan

Anupama Jagannathan

Alper Buyuktosunoglu

Warren Edward Maule

Pradip Bose

Andrew Dale Walls

Jon Alfred Casey

Richard Louis Arndt

Hans Mikael Jacobson

Sani Richard Nassif

Balakrishna Raghavendra Iyer

Robert Brett Tremaine

Harigovind Venkatraj Ramasamy

Chitra K Subramanian

Thomas Anthony Wassick

Michael B Healy

Thomas M Brey

Tobias Webel

Sungjun Chun

Anne Elizabeth Gattiker

Augusto Javier Vega

Mark Andrew Brittain

Andreas Bieswanger

Sriram Govindan

Richard Frank Rizzolo

Steven Paul Hartman

Jeonghee Shin

Stefanie Ruth Chiras

Hye-Young McCreary

Venkata K Tavva

Systems and techniques that facilitate automated validation of power topology are provided. In various embodiments, a control component can transmit a transition command to a power-distribution node of a data center, wherein the transition command can cause an outlet of the power-distribution node to transition between power states. In various aspects, a verification component can verify that a power-consumption node of the data center is connected to the outlet by comparing a pre-transition power characteristic of the power-consumption node with a post-transition power characteristic of the power-consumption node.

Automated validation of power topology via power state transitioning

An embodiment includes extracting statistical data associated with invocation of an application programming interface (API) from a log and using the statistical data to calculate a performance value and generate an aggregate dataset that combines the performance value with performance values associated with other invocations of the API. The embodiment includes calculating metric values for performance values for respective time intervals of a time period and calculating mean and standard deviation values of the metric values for the time period. The embodiment includes selecting the API as a candidate API and detecting a Customer Impacting Event (CIE) by applying a machine learning algorithm using monitored values associated with the candidate API during a time frame defined by a rolling window. The embodiment also includes automatically initiating a selected alert from among a plurality of alert options based at least in part on the monitored values associated with the CIE.

Automated selection of performance monitors

A method for determining an intervention response category for provisioning workflows. The method determines provisioning features of a provisioning step. The method performs outlier detection to identify and remove outliers from non-intervention data to produce a non-intervention normal data set. The method performs iterative grouping on the non-intervention normal data set to determine significant variables in the provisioning features. The method performs response mapping of provisions using results of the iterative grouping including a significance of errors and a presence of errors in the non-intervention normal data and partial intervention data to categorize the provisions into a response category.

Method for proactive trouble-shooting of provisioning workflows for efficient cloud operations

A system is configured to determine a dominant error causing a provisioning step to become stuck during provisioning of a machine in a cloud environment. The system includes memory for storing instructions, and a processor configured to execute said instructions to determine an inverse error frequency (IEF) value for pre-intervention errors in a set of intervention provisioning data; determine a dominant error for a provision during said provisioning step in said set of intervention provisioning data based on a pre-intervention error that has a maximum IEF value; determine a duration frequency (DuF) value for the provision at said provisioning step for provisions in a set of non-intervention provisioning data; and determine said dominant error for each provision during said provisioning step in said set of non-intervention provisioning data based on an error that resulted in DuF value.

Approach to automated detection of dominant errors in cloud provisions

A distributed power management system is configured determine a node power consumption of a node during a first time interval. The system can determine a node power cap. The system can determine a proportional component power budget for a component of the node based, at least in part, on the node power consumption and a component power consumption. The system can determine a power budget for the component for a second time interval based, at least in part on the proportional component power budget.

Distributed power budgeting

A system and method for storing data that includes at least one memory device having a plurality of memory cells for storing data; and a memory control circuit that manages the read current and read pulse width applied to the memory cells, wherein the at least one memory device has a read current circuit configured to utilize adjustments to at least one of the read current or the read pulse width applied to the memory cells. In response to a request to read a group of the memory cells, the memory control circuit in an example, in response to determining that a comparative temperature value exceeds a first threshold, is configured to perform at least one of reducing the read current and/or increasing the read pulse width to be applied to the group of memory devices to be read.

Method and system to improve read reliability in memory devices

A computer controls power distribution. The computing system determines a power budget for a portion of a topography for a power delivery system. The computing system generates a pool of worker programs for the portion of the topography. The computing system generates a first number of power management tasks to manage power consumption in the portion of the topography based on the power budget. The computing system sends the first number of power management tasks to at least one worker program included in the pool of worker programs.

Continuously available power control system

Technology for computing number of active servers needed over time in a cloud/compute cluster includes the following operations (not necessarily in the following order): (i) determining the capacity of each VCE provisioned on the cloud against the resource guaranteed to that VCE; (ii) forecasting the resource needs over time using historical requests for each VCE flavor; and (iii) using the forecasted resource needs to determine the required number of future servers at some future time. Some embodiments of the present invention use a formula that accounts for the interplay among various parameter values of the VCE flavors and also the mapping of the needs of VCEs of various flavors to the capabilities of physical resources.

Cloud data center with reduced energy consumption

Memory bandwidth allocations for a plurality of tenants on a processor socket may be managed by determining a total memory bandwidth for the processor socket, determining a respective share of the total memory bandwidth for each tenant, and determining a respective dynamic memory bandwidth budget for the each tenant. The memory bandwidth allocations may be controlled based on the determined respective dynamic memory bandwidth budgets, wherein the each respective dynamic memory bandwidth budget supports utilizing the each respective share and supports sharing unused memory bandwidth of the each respective tenant with a different tenant included in the plurality of tenants.

Memory bandwidth management for performance-sensitive IaaS

Dynamically allocating cache in a multi-tenant infrastructure includes monitoring cache usage for multiple workloads in a multi-tenant processing infrastructure to determine a workload phase. A baseline performance level per workload is determined. The baseline performance level is dependent upon the workload phase. The workloads for each tenant are categorized based on cache utilization and the cache is allocated to each workload based on the baseline performance level, cache utilization, and system wide cache capacity.

Dynamically allocating cache in a multi-tenant processing infrastructure

Utilizing a computing device to determine and enforce limits on cloud computing containers receiving data over a network. A determination is made of total container time remaining available for a first container to execute in a computing environment. Processor packet receipt time is determined for receiving and processing of a packet or a batch of packets via a network stack associated with the computing device. An updated total container time remaining is calculated for the first container accounting for the processor packet receipt time. The updated total container time remaining is enforced by dropping a subsequent packet or batch of packets received at the network stack if the updated total container time remaining is insufficient.

Accounting and enforcing non-process execution by container-based software receiving data over a network

A system with a local data collector that collects power management data for a subsystem. The local data collector can determine whether a first formatting associated with a first channel between the local data collector and a system power management data collector is equivalent to a second formatting associated with a second channel between the local data collector and the system power management data collector, and in response to a determination that the first formatting and second formatting are not equivalent format the power management data according to the first formatting; store the power management data formatted according to the first formatting in a first location in a memory; format the power management data according to the second formatting; and store the power management data formatted according to the second formatting in a second location the memory.

Scalable data collection for system management

A method and apparatus for adjusting a frequency of a processor is disclosed herein. In one embodiment, the method includes inhibiting one or more processor cores from exiting an idle state. The method further includes determining a number of processor cores requesting exit from the idle state and a number of non-idle processor cores. The method also includes selecting a maximum frequency for the inhibited processor cores based on the number of inhibited processor cores requesting exit from the idle state and the number of non-idle processor cores. The method includes setting the maximum frequency for the inhibited processor cores, and then uninhibiting the processor cores requesting exit from the idle state.

Management of core power state transition in a microprocessor

A computer controls power distribution. The computing system determines a topography for a power delivery system that powers a group of computing devices. The computing system determines a number of worker programs for a pool of worker programs based on the topography. The computing system generates the pool of worker programs. The pool of worker programs includes both the number of worker programs and a number of back-up worker programs. The computing system generates a number of power management tasks to manage power consumption through one or more power elements included in the topography of the power delivery system. The computing system sends one or more power management tasks to a worker program included in the pool of worker programs.

Growing community of inventors

Austin, TX, United States of America

Karthick Rajamani