Abstract: A system, method, and computer-readable medium for processing in-order messages. A message broker is provided with unique message keys to performing in-order ensuring that a batch of messages are sent on the same message broker partition or data flow. A reactive generic data streaming processor provides a parallel data stream uniquely grouped to each message broker partition or data flow. In-order processing is maintained on each parallel data stream by using downstream publishers that preserve strict order in each data stream. A terminal sync message with a unique message key to identifies end of in-order streaming a message group.

Abstract: A system, method, and computer-readable medium for encoded byte message serialization and deserialization by microservices. A comprising receiving a multi-level message is received that includes generic data types for serialization and deserialization the encoded byte message. The multi-level message includes a top level message as to type that provides general information about content and applicability, and sub-messages as to types that include content related to purpose which includes an undefined type that contains properties related to schema for serialization and deserialization of the encoded byte message. The top level message and sub-messages content are processed to determine the undefined type that contains properties related to schema for serialization and deserialization of the encoded byte message. Dynamic loading is performed as to schema properties of the undefined type for serialization and deserialization of the encoded byte message for consumption by the microservices.

Abstract: An architecture to perform resource management among multiple network nodes and associated resources is disclosed. Example resource management techniques include those relating to: proactive reservation of edge computing resources; deadline-driven resource allocation; speculative edge QoS pre-allocation; and automatic QoS migration across edge computing nodes.

Abstract: Example methods, apparatus, and systems to manage quality of service with respect to service level agreements in a computing device are disclosed. An example apparatus includes a first mesh proxy assigned to a first platform-agnostic application, the first mesh proxy to generate a first resource request signal based on a first service level agreement requirement from the first platform-agnostic application; a second mesh proxy assigned to a second platform-agnostic application, the second mesh proxy to generate a second resource request signal based on a second service level agreement requirement from second platform-agnostic application; and a load balancer to allocate hardware resources for the first platform-agnostic application and the second platform-agnostic application based on the first resource request signal and the second resource request signal.

Abstract: Methods, apparatus, and processor-readable storage media for efficient storage of key-value data with schema integration are provided herein. An example computer-implemented method includes obtaining a metrics data message associated with a product, wherein the metrics data message has a first format and comprises a schema version and a type of the product; identifying one of a plurality of schema definitions for the metrics data message based at least in part on the schema version and the type of the product; converting the metrics data message into a second format based on the identified schema definition, wherein the second format removes at least some redundant data from the metrics data message; and storing the converted metrics data message in a metrics database.

Abstract: Methods, apparatus, and processor-readable storage media for automated stateful counter aggregation of device data are provided herein. An example computer-implemented method includes obtaining historical aggregate counter data and historical individual member counter data associated with a variable set of device members and a given temporal period; computing one or more stateful aggregate counter data values attributed to at least a portion of the variable set of device members for a given temporal value by applying at least one stateful counter aggregation algorithm to the obtained data; and performing one or more automated actions based at least in part on the one or more computed stateful aggregate counter data values.

Abstract: Methods, apparatus, and processor-readable storage media for automated stateful counter aggregation of device data are provided herein. An example computer-implemented method includes obtaining historical aggregate counter data and historical individual member counter data associated with a variable set of device members and a given temporal period; computing one or more stateful aggregate counter data values attributed to at least a portion of the variable set of device members for a given temporal value by applying at least one stateful counter aggregation algorithm to the obtained data; and performing one or more automated actions based at least in part on the one or more computed stateful aggregate counter data values.

Abstract: Techniques for performing application scaling may include: receiving a plurality of observed queue depth values for a queue, wherein the queue includes data packets to be processed by a first set of one or more applications of a first application type; determining, in accordance with the observed queue depth values and scaling configuration parameters for the first application type, whether to modify the number of applications currently in the first set, wherein the scaling configuration parameters includes a scaling threshold; and responsive to determining to modify the number of application currently in the first set, performing first processing that modifies the number of applications currently in the first set. Modifying the number of applications in the first set may include scaling up or scaling down the number of instances in the first set.