Systems, methods, and machine-readable media for processing data transmissions from a plurality of client devices to create composites for transmission to destination addresses are provided. Communications via a network may be received from a set of devices. Each communication may include a digital identifier corresponding to a destination specification. The communication may be processed to identify the digital identifier and to identify the destination specification. A data portion may be selected from the communication and cached. A second format may be identified as corresponding to a second communication medium, where the second format is different from a first format of the communication. A composite may be created according to the second format. The composite may include the selected data portion and the destination specification. The composite formatted according to the second format may be transmitted toward an endpoint device in accordance with the destination specification.

Provided is a process of autonomous distributed workload and infrastructure scheduling based on physical telemetry data of a plurality of different data centers executing a plurality of different workload distributed applications on behalf of a plurality of different tenants.

Provided is a process of autonomous distributed workload and infrastructure scheduling based on physical telemetry data of a plurality of different data centers executing a plurality of different workload distributed applications on behalf of a plurality of different tenants.

A management device of a storage system, the management devices includes a memory and a processor. The processor coupled to the memory and configured to acquire workload load information and system load information when executing a container, and determine a workload arrangement destination and a replica position of a volume based on the workload load information and the system load information when activating a workload.

A management device of a storage system, the management devices includes a memory and a processor. The processor coupled to the memory and configured to acquire workload load information and system load information when executing a container, and determine a workload arrangement destination and a replica position of a volume based on the workload load information and the system load information when activating a workload.

Systems, methods and/or computer program products managing momentary increases in resource requirements of microservices within a service mesh using sliced replicas or pre-configured cloud functions. The service mesh tracks incoming API calls of applications requesting use of specific microservice capabilities. Over time capabilities being called are categorized by the service mesh based on call frequency. Service mesh predicts patterns of expected increases in requests for each capability of the microservices. As the increase in the number of API calls for the capability become more than a threshold level predicted by the service mesh, a sliced replica of the microservice is created or a pre-configured cloud function is enabled to handle requests specifically for the capability causing the momentary increase in resource requirements, while the service mesh continues routing API calls for other capabilities to the requested microservice. Upon resource requirements subsiding, the sliced replica or pre-configured cloud function is removed.

Systems, methods and/or computer program products managing momentary increases in resource requirements of microservices within a service mesh using sliced replicas or pre-configured cloud functions. The service mesh tracks incoming API calls of applications requesting use of specific microservice capabilities. Over time capabilities being called are categorized by the service mesh based on call frequency. Service mesh predicts patterns of expected increases in requests for each capability of the microservices. As the increase in the number of API calls for the capability become more than a threshold level predicted by the service mesh, a sliced replica of the microservice is created or a pre-configured cloud function is enabled to handle requests specifically for the capability causing the momentary increase in resource requirements, while the service mesh continues routing API calls for other capabilities to the requested microservice. Upon resource requirements subsiding, the sliced replica or pre-configured cloud function is removed.

Systems and methods for cloud-based hybrid service meshes in microservice architectures are disclosed. A method for selection of a microservice in a cloud-based hybrid mesh microservice architecture may include: (1) receiving, at a hybrid service mesh library in a first microservice instance, a request for a second microservice, wherein the hybrid service mesh library comprises microservice information for a plurality of microservice instances comprising an identification of each microservice instance, a location for each microservice instance, and a status for each microservice instance; (2) identifying, by the hybrid service mesh library, a number of microservice instances for the microservice and the location of each microservice instance; (3) selecting, by the hybrid service mesh library, one of the plurality of microservice instances; and (4) calling, by the hybrid service mesh library, the selected microservice instance.

Systems and methods for cloud-based hybrid service meshes in microservice architectures are disclosed. A method for selection of a microservice in a cloud-based hybrid mesh microservice architecture may include: (1) receiving, at a hybrid service mesh library in a first microservice instance, a request for a second microservice, wherein the hybrid service mesh library comprises microservice information for a plurality of microservice instances comprising an identification of each microservice instance, a location for each microservice instance, and a status for each microservice instance; (2) identifying, by the hybrid service mesh library, a number of microservice instances for the microservice and the location of each microservice instance; (3) selecting, by the hybrid service mesh library, one of the plurality of microservice instances; and (4) calling, by the hybrid service mesh library, the selected microservice instance.

Herein are resource-constrained techniques that plan ahead for resiliently moving pluggable databases between container databases after a failure in a high-availability database cluster. In an embodiment, a computer identifies many alternative placements that respectively assign each pluggable database to a respective container database. For each alternative placement, a respective resilience score is calculated for each pluggable database that is based on the container database of the pluggable database. Based on the resilience scores of the pluggable databases for the alternative placements, a particular placement is selected as an optimal placement that would maximize utilization of computer resources, minimize database latencies, maximize system throughput, and maximize the ability of the database cluster to avoid a service outage.