Abstract: A system may receive a first directed acyclic graphic (DAG) for an application. The system may model performance of each function in the DAG to generate a performance model. The system may generate a plurality of variant DAGs. For each of the variant DAGs, the system may obtain a configuration vector and forecast, based on the performance model and the configuration vector, a plurality of end-to-end latency distributions for the variant DAGS. The system may select the variant DAG and configuration vector based on a selection criteria. The system may cause an application to be executed according to the variant DAG and configuration vector.

Abstract: A system may receive a directed acyclic graphic (DAG) for an application. The system may profile the DAG with a plurality of computer resource allocations to generate an end-to-end (E2E) latency model. The system may generate, based on the E2E latency model, an execution plan comprising optimized computer resource allocations and timing information. The system may cause a computing infrastructure to execute a function in a first stage of the DAG model on a first virtual machine right sized according to the execution plan. The system may cause the computing infrastructure to initialize, at a time determined specified by the execution plan, a second virtual machine for a function in a second stage in the DAG model. The system may cause the FaaS infrastructure to execute the function in the second stage on the second virtual machine after completion of the function in the first stage.

Abstract: A processing system is described which assigns jobs to heterogeneous processing modules. The processing system assigns jobs to the processing modules in a manner that attempts to accommodate the service demands of the jobs, but without advance knowledge of the service demands. In one case, the processing system implements the processing modules as computing units that have different physical characteristics. Alternatively, or in addition, the processing system may implement the processing modules as threads that are executed by computing units. Each thread which runs on a computing unit offers a level of performance that depends on a number of other threads that are simultaneously being executed by the same computing unit.

Abstract: Systems and methods related to harvesting of the unused resources in a distributed computing system are described. An example method, in a system including a host server, having a first instruction set architecture (ISA), and an interface card, having a second ISA is provided. The method includes designating at least one type of resource, associated with the host server for harvesting by compute entities configured for execution using the processor having the second ISA, where the host server is configured to execute compute entities requiring execution by the processor having the first ISA. The method further includes in response to a request for accessing the at least one type of resource by a compute entity, executing on the processor having the second ISA, automatically allowing the compute entity to access the at least one type of resource associated with the host server.

Abstract: A processing system is described which assigns jobs to heterogeneous processing modules. The processing system assigns jobs to the processing modules in a manner that attempts to accommodate the service demands of the jobs, but without advance knowledge of the service demands. In one case, the processing system implements the processing modules as computing units that have different physical characteristics. Alternatively, or in addition, the processing system may implement the processing modules as threads that are executed by computing units. Each thread which runs on a computing unit offers a level of performance that depends on a number of other threads that are simultaneously being executed by the same computing unit.

Abstract: Systems and methods related to harvesting of the unused resources in a distributed computing system are described. An example method, in a system including a host server, having a first instruction set architecture (ISA), and an interface card, having a second ISA is provided. The method includes designating at least one type of resource, associated with the host server for harvesting by compute entities configured for execution using the processor having the second ISA, where the host server is configured to execute compute entities requiring execution by the processor having the first ISA. The method further includes in response to a request for accessing the at least one type of resource by a compute entity, executing on the processor having the second ISA, automatically allowing the compute entity to access the at least one type of resource associated with the host server.

Abstract: A processing system is described which assigns jobs to heterogeneous processing modules. The processing system assigns jobs to the processing modules in a manner that attempts to accommodate the service demands of the jobs, but without advance knowledge of the service demands. In one case, the processing system implements the processing modules as computing units that have different physical characteristics. Alternatively, or in addition, the processing system may implement the processing modules as threads that are executed by computing units. Each thread which runs on a computing unit offers a level of performance that depends on a number of other threads that are simultaneously being executed by the same computing unit.

Abstract: A processing system is described which assigns jobs to heterogeneous processing modules. The processing system assigns jobs to the processing modules in a manner that attempts to accommodate the service demands of the jobs, but without advance knowledge of the service demands. In one case, the processing system implements the processing modules as computing units that have different physical characteristics. Alternatively, or in addition, the processing system may implement the processing modules as threads that are executed by computing units. Each thread which runs on a computing unit offers a level of performance that depends on a number of other threads that are simultaneously being executed by the same computing unit.

Abstract: A processing system is described which assigns jobs to heterogeneous processing modules. The processing system assigns jobs to the processing modules in a manner that attempts to accommodate the service demands of the jobs, but without advance knowledge of the service demands. In one case, the processing system implements the processing modules as computing units that have different physical characteristics. Alternatively, or in addition, the processing system may implement the processing modules as threads that are executed by computing units. Each thread which runs on a computing unit offers a level of performance that depends on a number of other threads that are simultaneously being executed by the same computing unit.

Abstract: The subject disclosure is directed towards scheduling requests using quality values that are defined for partial responses to the requests. For each request in a queue, an associated processing time is determined using a system load and/or the quality values. The associated processing time is less than or equal to a service demand, which represents an amount of time to produce a complete response.

Abstract: A processing system is described which assigns jobs to heterogeneous processing modules. The processing system assigns jobs to the processing modules in a manner that attempts to accommodate the service demands of the jobs, but without advance knowledge of the service demands. In one case, the processing system implements the processing modules as computing units that have different physical characteristics. Alternatively, or in addition, the processing system may implement the processing modules as threads that are executed by computing units. Each thread which runs on a computing unit offers a level of performance that depends on a number of other threads that are simultaneously being executed by the same computing unit.

Abstract: A processing system is described which assigns jobs to heterogeneous processing modules. The processing system assigns jobs to the processing modules in a manner that attempts to accommodate the service demands of the jobs, but without advance knowledge of the service demands. In one case, the processing system implements the processing modules as computing units that have different physical characteristics. Alternatively, or in addition, the processing system may implement the processing modules as threads that are executed by computing units. Each thread which runs on a computing unit offers a level of performance that depends on a number of other threads that are simultaneously being executed by the same computing unit.

Abstract: Graph queries are processed using a plurality of independent query execution engines. A graph query submitted to a graph database which is modeled by an attributed graph is received. The graph query is decomposed into a plurality of query components. For each of the query components, a one of the query execution engines that is available to process the query component is identified, a sub-query representing the query component is generated, the sub-query is sent to the identified query execution engine for processing, and results for the sub-query are received from the identified query execution engine. The results received are then combined to generate a response to the graph query.

Abstract: A processing system is described which assigns jobs to heterogeneous processing modules. The processing system assigns jobs to the processing modules in a manner that attempts to accommodate the service demands of the jobs, but without advance knowledge of the service demands. In one case, the processing system implements the processing modules as computing units that have different physical characteristics. Alternatively, or in addition, the processing system may implement the processing modules as threads that are executed by computing units. Each thread which runs on a computing unit offers a level of performance that depends on a number of other threads that are simultaneously being executed by the same computing unit.

Abstract: Graph queries are processed using a plurality of independent query execution engines. A graph query submitted to a graph database which is modeled by an attributed graph is received. The graph query is decomposed into a plurality of query components. For each of the query components, a one of the query execution engines that is available to process the query component is identified, a sub-query representing the query component is generated, the sub-query is sent to the identified query execution engine for processing, and results for the sub-query are received from the identified query execution engine. The results received are then combined to generate a response to the graph query.

Abstract: The subject disclosure is directed towards scheduling requests using quality values that are defined for partial responses to the requests. For each request in a queue, an associated processing time is determined using a system load and/or the quality values. The associated processing time is less than or equal to a service demand, which represents an amount of time to produce a complete response.

Abstract: Improved storage systems which use write off-loading are described. When a request to store some data in a particular storage location is received, if the particular storage location is unavailable, the data is stored in an alternative location. In an embodiment, the particular storage location may be unavailable because it is powered down or because it is overloaded. The data stored in the alternative location may be subsequently recovered and written to the particular storage location once it becomes available.

Abstract: Improved storage systems which use write off-loading are described. When a request to store some data in a particular storage location is received, if the particular storage location is unavailable, the data is stored in an alternative location. In an embodiment, the particular storage location may be unavailable because it is powered down or because it is overloaded. The data stored in the alternative location may be subsequently recovered and written to the particular storage location once it becomes available.