Abstract: Embodiments described herein are generally directed to the use of sidecars to perform dynamic API contract generation and conversion. In an example, a first sidecar of a source microservice intercepts a first call to a first API exposed by a destination microservice. The first call makes use of a first API technology specified by a first contract and is originated by the source microservice. An API technology is selected from multiple API technologies. The selected API technology is determined to be different than the first API technology. Based on the first contract, a second contract is dynamically generated that specifies an intermediate API that makes use of the selected API technology. A second sidecar of the destination microservice is caused to generate the intermediate API and connect the intermediate API to the first API.

Abstract: Embodiments described herein are generally directed to the use of sidecars to perform dynamic Application Programming Interface (API) contract generation and conversion. In an example, a first call by a first microservice to a first API of a second microservice is intercepted by a first sidecar of the first microservice. The first API is of a first API type of multiple API types and is specified by a first contract. An API type of the multiple API types is selected by the first sidecar. Responsive to determining the selected API type differs from the first API type, based on the first contract, a second contract is generated by the first sidecar specifying a second API of the selected API type; and a second sidecar of the second microservice is caused to generate the second API and internally connect the second API to the first API based on the second contract.

Abstract: Embodiments described herein are generally directed to intelligent management of microservices failover. In an example, responsive to an uncorrectable hardware error associated with a processing resource of a platform on which a task of a service is being performed by a primary microservice, a failover trigger is received by a failover service. A secondary microservice is identified by the failover service that is operating in lockstep mode with the primary microservice. The secondary microservice is caused by the failover service to takeover performance of the task in non-lockstep mode based on failover metadata persisted by the primary microservice. The primary microservice is caused by the failover service to be taken offline.

Abstract: Various examples relate to apparatuses, devices, methods and computer programs for a group leader and a group member of a group of nodes of a blockchain network. The apparatus for the group leader comprises interface circuitry, machine-readable instructions, and processor circuitry to execute the machine-readable instructions to manage a membership of nodes of the blockchain network in the group of nodes, perform or delegate blockchain-related computational activity on behalf of the group of nodes according to an energy criterion.

Abstract: An apparatus to facilitate at-scale telemetry using interactive matrix for deterministic microservices performance is disclosed. The apparatus includes one or more processors to: receive user input comprising an objective or task corresponding to scheduling a microservice for a service, wherein the objective or task may include QoS, SLO, ML feedback; identify interaction matrix components in an interaction matrix that match the objective or tasks for the microservice; identify knowledgebase components in knowledgebase that match the objective or tasks for the microservice; and determine a scheduling operation for the microservice, the scheduling operation to deploy the microservice in a configuration that is in accordance with the objective or task, wherein the configuration comprises a set of hardware devices and microservice interaction points determined based on the interaction matrix components and the knowledgebase components.

Abstract: Examples relate to a concept for software application container hardware resource allocation, and in particular to sidecar apparatuses, sidecar devices, methods for a software application container sidecars, a resource management controller apparatus, a resource management controller device, and corresponding computer programs and computer systems. A sidecar apparatus comprises interface circuitry, machine-readable instructions and processing circuitry to execute the machine-readable instructions to obtain information on hardware resources desired by a software application container from the software application container, and to provide a request for changing the hardware resources allocated to the software application container to another entity capable of influencing an allocation of hardware resources to the software application container.

Abstract: Examples relate to a computer system, a telemetry hub apparatus, a telemetry hub device, a telemetry hub method, a microservice apparatus, a microservice device, a microservice method and to corresponding computer programs. The telemetry apparatus is configured to obtain telemetry information from a plurality of microservices, and to provide access to the telemetry information for the plurality of microservices according to an access scheme.

Abstract: An apparatus to facilitate provenance audit trails for microservices architectures is disclosed. The apparatus includes one or more processors to obtain provenance metadata for a microservice from a local blockchain of provenance metadata maintained for the hardware resource executing a task performed by the microservice, the provenance metadata comprising identification of the microservice, operating state of at least one of a hardware resource or a software resource used to execute the microservice and the task, and an operating state of a sidecar of the microservice during the task; access one or more policies established for the microservice; analyze the provenance metadata with respect to the one or more policies to identify if there is a violation of the one or more policies; and generate one or more evaluation metrics based on whether the violation of the one or more policies is identified.

Abstract: Various examples relate to an apparatus, device, method, and a non-transitory machine-readable storage medium for a node of a blockchain network. The apparatus comprises interface circuitry, machine-readable instructions and processor circuitry to execute the machine-readable instructions to compare a traffic pattern of requests associated with one or more smart contracts hosted by the node of the blockchain network with a reference traffic pattern, determine an estimated denial of service of at least one of the one or more smart contracts based on the comparison between the traffic pattern and the reference traffic pattern, determine one or more potential mitigations for the estimated denial of service, and apply at least one of the one or more potential mitigations.

Abstract: An apparatus to facilitate provenance audit trails for microservices architectures is disclosed. The apparatus includes one or more processors to: obtain, by a microservice of a service hosted in a datacenter, provisioned credentials for the microservice based on an attestation protocol; generate, for a task performed by the microservice, provenance metadata for the task, the provenance metadata including identification of the microservice, operating state of at least one of a hardware resource or a software resource used to execute the microservice and the task, and operating state of a sidecar of the microservice during the task; encrypt the provenance metadata with the provisioned credentials for the microservice; and record the encrypted provenance metadata in a local blockchain of provenance metadata maintained for the hardware resource executing the task and the microservice.

Abstract: An apparatus to facilitate telemetry targeted query injection for enhanced debugging in microservices architectures is disclosed. The apparatus includes one or more processors to: identify contextual trace of a previous query recorded in collected data of a service, where microservices of the service responded to the previous query; access an interdependency flow graph representing an architecture and interaction of microservices deployed for a service; retrieve, based on the interdependency flow graph, telemetry data of the microservices corresponding to the contextual trace; identify, based on the telemetry data, an activation profile corresponding to the previous query, the activation profile detailing a response of the microservices to the previous query; compare the activation profile to a correlation profile for the previous query to detect whether an anomaly occurred in the service in response to the previous query; and recommend a modified query based on detection of the anomaly.

Abstract: Telemetry systems for monitoring cooling of compute components and related apparatus and methods are disclosed. An example apparatus includes interface circuitry, machine-readable instructions, and programmable circuitry to at least one of instantiate or execute the machine-readable instructions to generate a heatmap based on outputs of one or more sensors in an environment, the environment including a first compute device, the sensor outputs including a metric associated with a property of a coolant and a location of the sensor in the environment, identify a compute performance metric of the first compute device, determine a cooling parameter for the first compute device based on the heatmap and the compute performance metric, and cause a cooling distribution unit to control flow of the coolant in the environment based on the cooling parameter.

Abstract: Methods and apparatus to manage noise in computing systems are disclosed. An example server includes a housing to at least partially contain components of the server, a transducer to output an indication of noise detected outside of the housing, machine readable instructions, and programmable circuitry to at least one of instantiate or execute the machine readable instructions to adjust the operation of the server based on the output of the transducer to reduce noise.

Abstract: Embodiments described herein are generally directed to intelligent management of microservices failover. In an example, responsive to an uncorrectable hardware error associated with a processing resource of a platform on which a task of a service is being performed by a primary microservice, a failover trigger is received by a failover service. A secondary microservice is identified by the failover service that is operating in lockstep mode with the primary microservice. The secondary microservice is caused by the failover service to takeover performance of the task in non-lockstep mode based on failover metadata persisted by the primary microservice. The primary microservice is caused by the failover service to be taken offline.

Abstract: Embodiments described herein are generally directed to a transparent and adaptable mechanism for performing secure application communications through sidecars. In an example, a set of security features is discovered by a first sidecar of a first microservice of multiple microservices of an application. The set of security features are associated with a device of multiple devices of a set of one or more host systems on which the first microservice is running. Information regarding the set of discovered security features is made available to the other microservices by the first sidecar by sharing the information with a discovery service accessible to all of the microservices. A configuration of a communication channel through which a message is to be transmitted from a second microservice to the first microservice is determined by a second sidecar of the second microservice by issuing a request to the discovery service regarding the first microservice.

Abstract: An apparatus to facilitate telemetry targeted query injection for enhanced debugging in microservices architectures is disclosed. The apparatus includes one or more processors to: identify contextual trace of a previous query recorded in collected data of a service, where microservices of the service responded to the previous query; access an interdependency flow graph representing an architecture and interaction of microservices deployed for a service; retrieve, based on the interdependency flow graph, telemetry data of the microservices corresponding to the contextual trace; identify, based on the telemetry data, an activation profile corresponding to the previous query, the activation profile detailing a response of the microservices to the previous query; compare the activation profile to a correlation profile for the previous query to detect whether an anomaly occurred in the service in response to the previous query; and recommend a modified query based on detection of the anomaly.

Abstract: An apparatus to facilitate at-scale telemetry using interactive matrix for deterministic microservices performance is disclosed. The apparatus includes one or more processors to: receive user input comprising an objective or task corresponding to scheduling a microservice for a service, wherein the objective or task may include QoS, SLO, ML feedback; identify interaction matrix components in an interaction matrix that match the objective or tasks for the microservice; identify knowledgebase components in knowledgebase that match the objective or tasks for the microservice; and determine a scheduling operation for the microservice, the scheduling operation to deploy the microservice in a configuration that is in accordance with the objective or task, wherein the configuration comprises a set of hardware devices and microservice interaction points determined based on the interaction matrix components and the knowledgebase components.

Abstract: Embodiments described herein are generally directed to the use of sidecars to perform dynamic API contract generation and conversion. In an example, a first call by a first microservice to a first API of a second microservice is intercepted by a first sidecar of the first microservice. The first API is of a first API type of multiple API types and is specified by a first contract. An API type of the multiple API types is selected by the first sidecar. Responsive to determining the selected API type differs from the first API type, based on the first contract, a second contract is generated by the first sidecar specifying a second API of the selected API type; and a second sidecar of the second microservice is caused to generate the second API and internally connect the second API to the first API based on the second contract.

Abstract: An apparatus to facilitate provenance audit trails for microservices architectures is disclosed. The apparatus includes one or more processors to: obtain, by a microservice of a service hosted in a datacenter, provisioned credentials for the microservice based on an attestation protocol; generate, for a task performed by the microservice, provenance metadata for the task, the provenance metadata including identification of the microservice, operating state of at least one of a hardware resource or a software resource used to execute the microservice and the task, and operating state of a sidecar of the microservice during the task; encrypt the provenance metadata with the provisioned credentials for the microservice; and record the encrypted provenance metadata in a local blockchain of provenance metadata maintained for the hardware resource executing the task and the microservice.

Abstract: Embodiments described herein are generally directed to assigning virtual machine (VM) workloads to groupings/partitions of accelerator resources. In an example, a processing resource of a host system maintains: (i) a resource data structure containing resource utilization information for each of one or more accelerators associated with the host system; and (ii) a group data structure containing information regarding each group of multiple groups of one or more virtual functions (VFs) of the one or more accelerators that has been assigned for use by a respective VM of multiple VMs running on a virtual machine monitor (VMM) of the processing resource. A request to deploy a workload associated with a first VM is received. Responsive to the request, the workload is assigned to a VF of a group of the multiple groups determined to have resource capacity available to satisfy expected resource utilization of the workload.