Abstract: Methods and systems for managing the performance of workloads in a distributed system are disclosed. The distributed system may include any number of clients and deployments where workloads may be performed. The workloads may be performed by deploying container instances to the deployments. The container instances may perform the workload when deployed. It may be challenging to ascertain whether a container instance, container image on which the instance is based, and/or corresponding build file used to obtain the container image should be trusted. Metadata block chains may be used to ascertain whether container instances should be deployed to service the workload requests.

Abstract: Methods and systems for managing distributed systems are disclosed. The distributed system may include any number of data processing systems that may contribute to the functionality of the distributed system. To contribute to the functionality of the distributed system, each of the data processing systems may need to be configured to facilitate cooperative operation. To manage configuration of data processing system, a control plane may be utilized. The control plane may utilize a consensus based process for managing leadership among members of the control plane.

Abstract: Methods and systems for managing the performance of workloads in a distributed system are disclosed. The distributed system may include any number of clients and deployments where workloads may be performed. The deployments may include different hardware resources, may have different levels of performance, and/or may have other different characteristics that may impact performance of a given workload using any of the deployments. To service the workloads, container instances may be deployed to various deployments. When deciding where to deploy the container instances, the hardware resources of the deployments and/or resource expectations associated with the container instances may be taken into account. By doing so, container instances may be more likely to be deployed to deployments that meet their resource expectations.

Abstract: Methods and systems for managing workload performance in distributed systems is disclosed. The distributed system may include any number of data processing systems that may perform workloads. To manage workload performance, the distributed system may include a distributed control plane. The distributed control plane may include any number of data processing systems that both receive and service workload requests. When a workload request is received by one of the data processing systems of the control plane, a consensus based processing for a selecting one of the data processing systems to perform the workload may be performed. Consequently, the data processing system that received the workload request may or may not perform the workload to service the workload request depending on the outcome of the consensus based process.

Abstract: Methods and systems for managing the performance of workloads in a distributed system are disclosed. The distributed system may include any number of clients, deployments, and data sources operably to one another. To service the workloads, container instances may be deployed to various deployments. When deciding where to deploy the container instances, the hardware resources of the deployments and/or resource expectations associated with the container instances may be taken into account. By doing so, container instances may be more likely to be deployed to deployments that meet their resource expectations. The resource expectations may be embedded as metadata in resources specific build files.

Abstract: Methods and systems for managing the performance of workloads in a distributed system are disclosed. The distributed system may include any number of clients and deployments where workloads may be performed. The workloads may be performed by deploying container instances to the deployments. The container instances may perform the workload when deployed. It may be challenging to ascertain whether a container instance, container image on which the instance is based, and/or corresponding build file used to obtain the container image should be trusted. Metadata block chains may be used to ascertain whether container instances should be deployed to service the workload requests.

Abstract: Methods and systems for managing distributed systems are disclosed. The distributed system may include any number of data processing systems that may contribute to the functionality of the distributed system. To contribute to the functionality of the distributed system, each of the data processing systems may need to be configured to facilitate cooperative operation. To manage configuration of data processing system, a control plane may be utilized. The control plane may utilize a consensus based process for managing leadership among members of the control plane.