Min Jie Li

US Patent:

20200174843, Jun 4, 2020

Filed:

Nov 29, 2018

Appl. No.:

16/204550

Inventors:

- Armonk NY, US
John A. BIVENS - Ossining NY, US
Min LI - San Jose CA, US
Ruchi MAHINDRU - Elmsford NY, US
HariGovind V. RAMASAMY - Ossining NY, US
Valentina SALAPURA - Chappaqua NY, US
Eugen SCHENFELD - South Brunswick NJ, US

Assignee:

INTERNATIONAL BUSINESS MACHINES CORPORATION - Armonk NY

International Classification:

G06F 9/50
G06F 12/0802
G06F 15/167
G06F 15/173

Abstract:

Direct inter-processor communication is enabled with respect to data in a memory location without having to switch specific circuits through a switching element (e.g., an optical switch). Rather, in this approach a memory pool is augmented to include a dedicated portion that serves as a disaggregated memory common space for communicating processors. The approach obviates the requirement of switching of physical memory modules through the optical switch to enable the processor-to-processor communication. Rather, processors (communicating with another) have an overlapping ability to access the same memory module in the pool; thus, there is no longer a need to change physical optical switch circuits to facilitate the inter-processor communication. The disaggregated memory common space is shared among the processors, which can access the common space for reads and writes, although particular locations in the memory common space for reads and writes are different.

US Patent:

20200175183, Jun 4, 2020

Filed:

Nov 29, 2018

Appl. No.:

16/204463

Inventors:

- Armonk NY, US
John A. BIVENS - Ossining NY, US
Ruchi MAHINDRU - Elmsford NY, US
Valentina SALAPURA - Chappaqua NY, US
Min LI - San Jose CA, US
Yaoping RUAN - White Plains NY, US
Eugen SCHENFELD - South Brunswick NJ, US

Assignee:

INTERNATIONAL BUSINESS MACHINES CORPORATION - Armonk NY

International Classification:

G06F 21/60
H04L 9/00
H04L 9/08
G06F 21/30
H04L 9/30

Abstract:

A group of processors in a processor pool comprise a secure “enclave” in which user code is executable and user data is readable solely with the enclave. This is facilitated through the key management scheme described that includes two sets of key-pairs, namely: a processor group key-pair, and a separate user key-pair (typically one per-user, although a user may have multiple such key-pairs). The processor group key-pair is associated with all (or some define subset of) the processors in the group. This key-pair is used to securely communicate a user private key among the processors. The user private key, however, is not transmitted to non-members of the group. Further, preferably the user private key is refreshed periodically or upon any membership change (in the group) to ensure that non-members or ex-members cannot decipher the encrypted user key.

US Patent:

20200097347, Mar 26, 2020

Filed:

Sep 26, 2018

Appl. No.:

16/143418

Inventors:

- Armonk NY, US
John A. BIVENS - Ossining NY, US
Min LI - San Jose CA, US
Valentina SALAPURA - Chappaqua NY, US
Eugen SCHENFELD - South Brunswick NJ, US

Assignee:

INTERNATIONAL BUSINESS MACHINES CORPORATION - Armonk NY

International Classification:

G06F 11/00
G06F 11/07
G06N 5/02

Abstract:

Embodiments for preemptive deep diagnostics of resources in a disaggregated computing environment. Responsive to detecting a threshold breach of a recurrent event associated with a first resource of a first resource type executing a workload, an alert is generated; and responsive to receiving the alert, the execution of the workload on the first resource is ceased. Health check diagnostics are identified and invoked on the first resource based on the alert and a server telemetry. Results of the health check diagnostics are mapped to a set of learned failure patterns; and a potential failure of the first resource is predicted based on the mapping.

US Patent:

20200097348, Mar 26, 2020

Filed:

Sep 26, 2018

Appl. No.:

16/143430

Inventors:

- Armonk NY, US
John A. BIVENS - Ossining NY, US
Min LI - San Jose CA, US
Valentina SALAPURA - Chappaqua NY, US
Eugen SCHENFELD - South Brunswick NJ, US

Assignee:

INTERNATIONAL BUSINESS MACHINES CORPORATION - Armonk NY

International Classification:

G06F 11/00
G06F 9/50
G06F 11/07

Abstract:

Embodiments for preemptive deep diagnostics of resources in a disaggregated computing environment. Respective resources from respective pools of resources of different types are assigned to compose a disaggregated server. A workload is executed by the respective resources within the disaggregated server while the respective resources of the disaggregated server are monitored by a monitoring task. Responsive to a first resource of the respective resources generating an alert from the monitoring task, the workload is instantiated to be concurrently performed by the first resource and a second resource of the respective resources while initiating a health check diagnostic operation on the first resource.

US Patent:

20200097349, Mar 26, 2020

Filed:

Sep 26, 2018

Appl. No.:

16/143421

Inventors:

- Armonk NY, US
John A. BIVENS - Ossining NY, US
Min LI - San Jose CA, US
Valentina SALAPURA - Chappaqua NY, US
Eugen SCHENFELD - South Brunswick NJ, US

Assignee:

INTERNATIONAL BUSINESS MACHINES CORPORATION - Armonk NY

International Classification:

G06F 11/07

Abstract:

Embodiments for efficient resource placement in a disaggregated computing environment. Responsive to receiving an alert of a threshold breach of one or more events associated with a suspicious resource of a first resource type while executing a workload, a known good resource is selected from an available resource pool of the first resource type, where the selecting is performed to optimize a usage of the first resource type. The known good resource from the available resource pool of the first resource type is assigned to the workload such that execution of the workload is transferred from being performed by the suspicious resource to the known good resource.

US Patent:

20200097352, Mar 26, 2020

Filed:

Sep 26, 2018

Appl. No.:

16/143433

Inventors:

- Armonk NY, US
John A. BIVENS - Ossining NY, US
Min LI - San Jose CA, US
Valentina SALAPURA - Chappaqua NY, US
Eugen SCHENFELD - South Brunswick NJ, US

Assignee:

INTERNATIONAL BUSINESS MACHINES CORPORATION - Armonk NY

International Classification:

G06F 11/07

Abstract:

Embodiments for preemptive substitution of resources in a disaggregated computing environment. Failure patterns and mitigation actions are analyzed for specific failures of respective resources within the disaggregated computing environment. Responsive to determining a failure threshold has been reached for a first resource of a first type of the respective resources, a mitigation action is performed according to the analyzed failure patterns. A result of the mitigation action is determined and the result is used to improve the failure pattern analyzation.

US Patent:

20200097358, Mar 26, 2020

Filed:

Sep 26, 2018

Appl. No.:

16/143424

Inventors:

- Armonk NY, US
John A. BIVENS - Ossining NY, US
Min LI - San Jose CA, US
Valentina SALAPURA - Chappaqua NY, US
Eugen SCHENFELD - South Brunswick NJ, US

Assignee:

INTERNATIONAL BUSINESS MACHINES CORPORATION - Armonk NY

International Classification:

G06F 11/07
G06F 9/50

Abstract:

Embodiments for preemptive deep diagnostics of resources in a disaggregated computing environment. A set of new resources of a first type is provided to an available resource pool within the disaggregated computing environment. An estimate for an expected time to failure (ETTF) for each one of the set of new resources is computed, and respective ones of the new resources from the available resource pool are provisioned to respective workloads based on the ETTF.

US Patent:

20200099592, Mar 26, 2020

Filed:

Sep 26, 2018

Appl. No.:

16/143427

Inventors:

- Armonk NY, US
John A. BIVENS - Ossining NY, US
Min LI - San Jose CA, US
Valentina SALAPURA - Chappaqua NY, US
Eugen SCHENFELD - South Brunswick NJ, US

Assignee:

INTERNATIONAL BUSINESS MACHINES CORPORATION - Armonk NY

International Classification:

H04L 12/24
G06F 11/30
G06F 11/20
H04L 29/06
G06K 9/62
G06F 15/18
G06F 17/30

Abstract:

Embodiments for component lifecycle optimization in a disaggregated computing environment. A monitoring and machine learning process is performed to learn a respective lifecycle of different resource types as the different resource types are assigned to respective workloads. The monitoring and machine learning process is used to develop a set of learned failure patterns for determining a mitigation action to perform as new faults are encountered within each of the different resource types while executing the respective workloads. The mitigation action is performed to optimize a remaining lifecycle of respective ones of the different resource types according to the set of learned failure patterns.