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Determine a load balancing mechanism for allocation of shared resources in a storage system using a machine learning module based on number of I/O operations

專利號
US11175958B2
公開日期
2021-11-16
申請人
INTERNATIONAL BUSINESS MACHINES CORPORATION（US NY Armonk）
發(fā)明人
Lokesh M. Gupta; Matthew R. Craig; Beth Ann Peterson; Kevin John Ash
IPC分類
G06F9/50; G06N3/08; G06N20/00
技術(shù)領(lǐng)域
tcbs,learning,storage,in,machine,host,module,adapter,controller,resources
地域： NY NY Armonk

摘要

A plurality of interfaces that share a plurality of resources in a storage controller are maintained. In response to an occurrence of a predetermined number of operations associated with an interface of the plurality of interfaces, an input is provided on a plurality of attributes of the storage controller to a machine learning module. In response to receiving the input, the machine learning module generates an output value corresponding to a number of resources of the plurality of resources to allocate to the interface in the storage controller.

說明書

In further embodiments, the plurality of attributes includes: a measure of a peak number of I/O operations in the host bus adapter; a measure of an average number of I/O operations in the host bus adapter; and a measure of a median number of I/O operations in the host bus adapter.

In certain embodiments, the plurality of attributes includes: a measure of a number of high priority I/O requests rejected from the host bus adapter, wherein high priority I/O requests are expected to be processed faster than low priority I/O requests; a measure of a number of high priority requests active in the host bus adapter; and a measure of a number of connections from the host computational device to the host bus adapter.

In additional embodiments, the storage controller transmits the output value to a central computing device that generates weights and biases to be applied to machine learning modules of a plurality of storage controllers.

BRIEF DESCRIPTION OF THE DRAWINGS

Referring now to the drawings in which like reference numbers represent corresponding parts throughout:

FIG. 1 illustrates a block diagram of a computing environment for training and using a machine learning module for load balancing of shared resources in a storage controller, in accordance with certain embodiments;

FIG. 2 illustrates a block diagram that shows elements in a dual-server based storage controller in which load balancing of shared resources are determined by a machine learning module, in accordance with certain embodiments.

權(quán)利要求

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