Dell Products Inc.

Yonit Lopatinski

Roman Bober

Lior Gdaliahu

Ofir Ezrielev

Eliyahu Rosenes

Eli Rosenes

Techniques are provided for message content reduction using message templates. One method comprises obtaining a message of a given type, of multiple types, to be transmitted over a network; identifying one or more templates, of multiple templates, for the given type, wherein the templates comprise a respective message identifier; selecting a given template of the given message type based on a similarity of the message with the given template; updating the message by (i) incorporating the message identifier of the given template into the message and (ii) removing one or more fields of the message that match respective fields of the given template; and transmitting the updated message to a recipient device. The recipient device may determine whether the updated message comprises the message identifier, and may retrieve a message template using the message identifier. The recipient device may store the updated message as a template in a message cache.

Message content reduction using message templates

Methods and systems for managing workloads are disclosed. The workloads may be supported by operation of workload components that are hosted by infrastructure. The hosted locations of the workload components by the infrastructure may impact the performance of the workloads. To manage performance of the workloads, an optimization process may be performed to identify a migration plan for migrating some of the workload components to other infrastructure such as shared edge infrastructure. Migration of the workload components may reduce the computing resource cost for performing various workloads.

System and method for management of workload distribution in shared environment

Methods and systems for managing workloads are disclosed. The workloads may be supported by operation of workload components that are hosted by infrastructure. The hosted locations of the workload components by the infrastructure may impact the performance of the workloads. To manage performance of the workloads, an optimization process may be performed to identify a migration plan for migrating some of the workload components to other infrastructure such as shared edge infrastructure and cloud infrastructure temporarily or permanently. Migration of the workload components may reduce the computing resource cost for performing various workloads and/or reduce workload performance disruptions.

System and method for management of workload distribution for transitory disruption

A system can identify microservices calls. The system can, in response to identifying a first call, where the first call originates externally, storing, to logged calls, a first association between the first call, a response to the first call, a first entity that originated the first call, a second entity to which the first call is destined, a unique identifier, and a counter associated with the unique identifier, or where the first call originates internally, storing, to the logged calls, a second association between the first call, the response, the first entity, the second entity, the unique identifier, and the counter after incrementing the counter, wherein the unique identifier is identified by the first call. The system can order logged calls that share unique identifiers according to values of respective counters. The system can store a topology of the microservices that is generated according to the ordered logged calls.

Microservices anti-pattern discovery

Methods and systems for managing workloads are disclosed. The workloads may be supported by operation of workload components that are hosted by infrastructure. The hosted locations of the workload components by the infrastructure may impact the performance of the workloads. To manage performance of the workloads, an optimization process may be performed to identify a migration plan for migrating some of the workload components to different infrastructure locations. Some of the different infrastructure locations may reduce computing resource cost for performance of the workloads.

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Shomria, Israel

Yonit Lopatinski