International Business Machines Corporation

Christopher G Brooker

Steven M Partlow

Elpida Tzortzatos

Andrew C M Hicks

Harris M Morgenstern

Robert Miller, Jr

Charles E Mari

Thomas F Rankin

Alfred F Foster

Walter W Otto

Thomas William Conti

Michael E Gildein

Diane Marie Stamboni

Scott B Tuttle

Christine D Mikijanic

Duane C Hughes

Daniel John FitzGerald

Andrew Evans

Techniques for vector-based identification of software dependency relationships are described herein. An aspect includes determining a first dependency relationship value between a first code segment and a second code segment. Another aspect includes calculating a magnitude vector based on the first dependency relationship value and a second dependency relationship value corresponding to the first code segment and the second code segment. Another aspect includes determining a relationship score for the first code segment and the second code segment based on the magnitude vector and the first dependency relationship value.

Vector-based identification of software dependency relationships

Techniques for predictive water condition monitoring are described herein. An aspect includes a method that includes monitoring, by one or more processors, at least one water sensor to establish a baseline of a water condition model and monitoring one or more water conditions. A predicted water condition is determined based on the water condition model and the one or more water conditions. An alert is transmitted to one or more devices based on determining that the predicted water condition indicates a predicted contaminant level above a threshold.

Predictive water condition monitoring

Embodiments of the disclosure serializing wrapping of a circularly wrapping trace buffer via a compare-and-swap (CS) instruction by a method including executing a CS loop to advance to a location in the buffer indicated by a next free pointer. The method also includes incrementing a master wrap sequence number each time the next free pointer returns to a top of the buffer and executing another CS loop to increment a wrap number stored in a trace block corresponding to the location indicated by the next free pointer. Based upon determining that the wrap number stored in the trace block is one less than or equal to the master wrap sequence number, the method includes reserving space in a buffer associated with the trace block and storing the wrap number stored in the trace block as an old wrap number and incrementing a use-count of the trace block.

Serializing wrapping trace buffer via a compare-and-swap instruction

Embodiments relate to methods, systems and computer program products for defragmenting storage class memory by comparing a utilization rate of the storage class memory to a threshold value. If the utilization rate of the storage class memory is greater than the threshold value, the potentially wasted storage space is then compared to the combined storage capacity of the unclaimed extents of the storage class memory. If the potentially wasted storage space is greater than the combined storage capacity of the unclaimed extents of the storage class memory, a determination is made whether a defragmentation was recently performed. Based on determining that the defragmentation was not recently performed, or that it was recently performed and was productive, performing a defragmentation of the storage class memory.

Defragmentation of storage class memory managed in extents

Auxiliary storage is segmented into different types of extents (i.e., ranges of storage), including extents with 4K blocks of storage, extents with 1M blocks of storage, empty extents, and mixed extents that include blocks of storage of various sizes (e.g., 4K blocks and 1M blocks). The auxiliary storage, and in particular, the extents and blocks of storage therein, are managed to reduce storage fragmentation and optimize system performance.

Managing storage extents and the obtaining of storage blocks within the extents

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New Paltz, NY, United States of America

Christopher G Brooker