Abstract: Systems and methods are provided for prioritizing system memory for separate isolated computing workspaces (e.g., cloud-based software containers) that are executing together on an information handling system by reallocating higher performance system memory to higher-ranked computing workspace/s. In one example, the disclosed systems and methods may be implemented to optimize system memory usage for memory-heavy heterogeneous workloads of workspaces that are simultaneously executing together as modern clients on an information handling system.

Abstract: Various embodiments of information handling systems and related methods provide a secure platform control point, which enables cloud-native applications to access the local hardware resources and platform capabilities of the information handling system. The embodiments disclosed herein provide the same level of security, manageability and compute/intelligence for cloud-native applications, as endpoint-native applications run locally on the information handling system.

Abstract: Systems and methods are provided for prioritizing system memory for separate isolated computing workspaces (e.g., cloud-based software containers) that are executing together on an information handling system by reallocating higher performance system memory to higher-ranked computing workspace/s. In one example, the disclosed systems and methods may be implemented to optimize system memory usage for memory-heavy heterogeneous workloads of workspaces that are simultaneously executing together as modern clients on an information handling system.

Abstract: The present disclosure provides various embodiments of information handling systems and related methods to provide workload remediation on client devices running multiple concurrent workloads. More specifically, the present disclosure provides software services and computer-implemented methods that utilize workload performance metrics and contextual information to provide workload remediation for each workload/application included within a user's workspace. The disclosed embodiments provide an automated iterative remediation framework, which identifies degradation of workload performance metrics of each workload, takes one or more corrective actions to remediate the performance degradation based on a set of observed states obtained for each workload, measures the efficacy of each corrective action using a weighted scoring function, and improves the workload performance for each workload by selecting the corrective action that optimizes the weighted scoring function for the set of observed states.

Abstract: The present disclosure provides various embodiments of information handling systems and related methods to provide a secure platform control point, which enables cloud-native applications to access the local hardware resources and platform capabilities of the information handling system. In doing so, the embodiments disclosed herein provide the same level of security, manageability and compute/intelligence for cloud-native applications, as is currently provided for endpoint-native applications run locally on the information handling system.

Abstract: Embodiments of systems and methods are provided to trigger migration of a workload from cloud-based resources to local resources, or vice versa. In the disclosed embodiments, an orchestration service receives telemetry data from a client system associated with a user and cloud resource usage data corresponding to the user from a plurality of cloud service providers. Before the end of each cloud computing service billing cycle, the orchestration service: uses the cloud resource usage data and/or the telemetry data to determine a cloud resource usage, which is expected for the user at the end of the cloud computing service billing cycle; generates a trigger to migrate the user's workload from cloud-based resources to local resources, or vice versa, based on the expected cloud resource usage; and initiates migration of the user's workload if a trigger is generated. As such, the orchestration service can be used to effectively manage per-user cloud resource costs.