International Business Machines Corporation

Sandhya Kapoor

Valentina Salapura

Richard Edwin Harper

Long Wang

Fred Allison Bower, Iii

Jing Min Xu

Yuan Wang

Lin Y Yang

Harigovind Venkatraj Ramasamy

Xiao Xi Liu

Hiroki Nakano

Fan Jing Meng

William V Da Palma

Nikhil Hegde

Gregory Joseph McKnight

Yi-hsiu Wei

Takayuki Kushida

Peter Morjan

Harigovind Ramasamy

Jason M Heim

Tony W Offer

Kumar S Varadan

Deepak W Elias

A method of organizing computer resources includes receiving a specification defining a plurality of quiescence groups of independent component instances for each of at least two services, and performing a first load balancing of the quiescence groups across a plurality of physical servers to define a plurality of supergroups while assigning each of the physical servers across the supergroups.

Hardware placement and maintenance scheduling in high availability systems

A computer-implemented method is presented for automatically generating alerting rules. The method includes identifying, via offline analytics, abnormal patterns and normal patterns from history logs based on machine learning, statistical analysis and deep learning, the history logs stored in a history log database, automatically generating the alerting rules based on the identified abnormal and normal patterns, and transmitting the alerting rules to an alerting engine for evaluation. The method further includes receiving a plurality of online log messages from a plurality of computing devices connected to a network, augmenting the plurality of online log messages, and extracting information from the plurality of augmented online log messages to be provided to the alerting engine, the alerting engine configured to approve and enforce the alerting rules automatically generated by the offline analytics processing.

Tool-specific alerting rules based on abnormal and normal patterns obtained from history logs

Data processing workload control in a data center is provided, where the data center includes computers whose operations consume power and a workload controller composed of automated computing machinery that controls the overall data processing workload in the data center. The data processing workload is composed of a plurality of specific data processing jobs, including scheduling, by the workload controller in dependence upon power performance information, the data processing jobs for execution upon the computers in the data center, the power performance information including power consumption at a plurality of power-conserving states for each computer in the data center that executes data processing jobs and dispatching by the workload controller the data processing jobs as scheduled for execution on computers in the data center.

Data processing workload control

A method for managing communication between a client process and a server process in a distributed computing environment, the client process residing on a host computer that is connected to a physical network having a transport layer and a network layer. The method begins when the client process makes a remote procedure call by detecting whether a server process identified by the remote procedure call is located on the host computer. If so, a binding handle vector is returned to the client process. The protocol sequence in the binding handle is mapped to a second protocol sequence that establishes an interprocess communication path between the client and server processes instead of a path through the transport and network layers of the physical network. The remote procedure call is then executed, preferably by using a send and receive messaging facility of the host computer operating system.

Transparent local RPC optimization

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Austin, TX, United States of America

Sandhya Kapoor