Abstract: System and methods for content- and contention-aware object detection are provided. A system may receive video information and perform object detection and object tracking based on an execution configuration. The system may approximate an optimized execution configuration. To approximate the optimized execution configuration, the system may identify, based on the video information, a plurality of content features. The system may further measure a contention level of a computer resource or multiple resources. The system may approximate, based on the content features and the utilization metric, latency metrics, for a plurality of execution configuration sets, respectively. The system may also approximate, based on the content features, accuracy metrics for the execution configuration sets, respectively. The system may select the optimized execution configuration set in response to satisfaction of a performance criterion.

Abstract: A system may forecast a workload for a cluster of nodes in a database management system. The system may generate a reconfiguration plan based on the forecasted workload. The system may obtain a heterogenous configuration set. The heterogenous configuration set may include respective configuration sets for the complete sets of nodes. The system may forecast, based on a first machine learning model, respective performance metrics for nodes in each of the complete sets. The system may forecast a cluster performance metric for the entire cluster of nodes based on a second machine learning model. The system may include, in response to satisfaction of an acceptance criterion, the heterogenous configuration set in the reconfiguration plan. The system may cause the cluster of nodes to be reconfigured based on the reconfiguration plan.

Abstract: A system may forecast a workload for a cluster of nodes in a database management system. The system may generate a reconfiguration plan based on the forecasted workload. The system may obtain a heterogenous configuration set. The heterogenous configuration set may include respective configuration sets for the complete sets of nodes. The system may forecast, based on a first machine learning model, respective performance metrics for nodes in each of the complete sets. The system may forecast a cluster performance metric for the entire cluster of nodes based on a second machine learning model. The system may include, in response to satisfaction of an acceptance criterion, the heterogenous configuration set in the reconfiguration plan. The system may cause the cluster of nodes to be reconfigured based on the reconfiguration plan.

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 fully tested autonomous system works predictably under ideal or assumed environment. However, behavior of the system is not fully defined when component(s) malfunction or fail (e.g., sensor failures) and the like which leads to inefficient task execution. Present disclosure provides system and method for imperfect sensing-based analysis of agents deployed in environments for traversal task execution, specifically, in unknown environments. The system estimates performance metric (e.g., task execution time) and safety metrics (e.g., number of collisions encountered while executing the task) related to task of traversal of the vehicle/agent from its current position to a target location. The system also incorporates sensitivity of each sensor(s), for given task, when they malfunction. The sensing malfunction can be both independent and/or co-related with malfunction of other sensors.

Abstract: Methods and systems for object detection are disclosed. The methods and systems include: receiving a video frame, determining an execution configuration among multiple configurations at an inference time based on the video frame and multiple metrics (e.g., a latency metric, an accuracy metric, and an energy metric), and performing object detection or object tracking at the inference time based on the video frame and the execution configuration. Other aspects, embodiments, and features are also claimed and described.

Abstract: A system may forecast a plurality of workload measurements for a database management system (DBMS) at respective times based on a workload model. The system may determine, based on the forecasted workload measurements, configuration parameter sets optimized for the DBMS at the respective times. The system may generate a reconfiguration plan. The system may determine performance that would result from reconfiguring nodes of the DBMS with the configurations parameter sets. The system may select a reconfiguration plan in response to the performance satisfying a fitness criterion. The system may cause, at the reconfiguration times, the nodes to begin reconfiguration with the configuration parameter sets included in the selected reconfiguration plan.

Abstract: A system may receive video information. The system may select a combination of light-weight features and heavy weight features. The light-weight features may be extracted from the video information and the heavy weight features not extracted. The system may forecast, based on the light-weight features and the heavy weight features, accuracy, and latency metrics for performing the object detection and tracking using a plurality of candidate branch configurations, respectively. The system may select a branch configuration from the plurality of candidate branch configurations in response to satisfaction of an optimization criterion. The system may perform object detection and tracking based on the selected branch configuration.

Abstract: A system may forecast a plurality of workload measurements for a database management system (DBMS) at respective times based on a workload model. The system may determine, based on the forecasted workload measurements, configuration parameter sets optimized for the DBMS at the respective times. The system may generate a reconfiguration plan. The system may determine performance that would result from reconfiguring nodes of the DBMS with the configurations parameter sets. The system may select a reconfiguration plan in response to the performance satisfying a fitness criterion. The system may cause, at the reconfiguration times, the nodes to begin reconfiguration with the configuration parameter sets included in the selected reconfiguration plan.

Abstract: System and methods for content- and contention-aware object detection are provided. A system may receive video information and perform object detection and object tracking based on an execution configuration. The system may approximate an optimized execution configuration. To approximate the optimized execution configuration, the system may identify, based on the video information, a plurality of content features. The system may further measure a contention level of a computer resource or multiple resources. The system may approximate, based on the content features and the utilization metric, latency metrics, for a plurality of execution configuration sets, respectively. The system may also approximate, based on the content features, accuracy metrics for the execution configuration sets, respectively. The system may select the optimized execution configuration set in response to satisfaction of a performance criterion.

Abstract: A system may forecast a plurality of workload measurements for a database management system (DBMS) at respective times based on a workload model. The system may determine, based on the forecasted workload measurements, configuration parameter sets optimized for the DBMS at the respective times. The system may generate a reconfiguration plan. The system may determine a performance gain that would result from reconfiguring nodes of the DBMS with the configurations parameter sets. In addition, the system may determine a performance loss that would result from the respective databases of the nodes being inaccessible during reconfiguration with the configuration parameter sets. The system may select a reconfiguration plan in response to the performance gain and the performance loss satisfying a fitness criterion. The system may cause, at the reconfiguration times, the nodes to begin reconfiguration with the configuration parameter sets included in the selected reconfiguration plan.

Abstract: A fully tested autonomous system works predictably under ideal or assumed environment. However, behavior of the system is not fully defined when component(s) malfunction or fail (e.g., sensor failures) and the like which leads to inefficient task execution. Present disclosure provides system and method for imperfect sensing-based analysis of agents deployed in environments for traversal task execution, specifically, in unknown environments. The system estimates performance metric (e.g., task execution time) and safety metrics (e.g., number of collisions encountered while executing the task) related to task of traversal of the vehicle/agent from its current position to a target location. The system also incorporates sensitivity of each sensor(s), for given task, when they malfunction. The sensing malfunction can be both independent and/or co-related with malfunction of other sensors.

Abstract: System and methods for predictive application streaming are provided. The system may execute an application and, concurrently, the system may while load data blocks not stored on the device using a construct of superblocks. The system may detect an access event caused by an application accessing a data block in a memory. The system may determine, in response to the access event, a superblock comprising a plurality of blocks that are historically accessed within the same execution time window as the block. The system may forecast, based on the superblock and a machine learning model, superblocks to be accessed by the application that are not stored in the memory. The system may download the superblocks from a remote endpoint accessible via a network.

Abstract: A system may forecast a workload for a cluster of nodes in a database management system. The system may generate a reconfiguration plan based on the forecasted workload. The system may obtain a heterogenous configuration set. The heterogenous configuration set may include respective configuration sets for the complete sets of nodes. The system may forecast, based on a first machine learning model, respective performance metrics for nodes in each of the complete sets. The system may forecast a cluster performance metric for the entire cluster of nodes based on a second machine learning model. The system may include, in response to satisfaction of an acceptance criterion, the heterogenous configuration set in the reconfiguration plan. The system may cause the cluster of nodes to be reconfigured based on the reconfiguration plan.

Abstract: A system may forecast a plurality of workload measurements for a database management system (DBMS) at respective times based on a workload model. The system may determine, based on the forecasted workload measurements, configuration parameter sets optimized for the DBMS at the respective times. The system may generate a reconfiguration plan. The system may determine a performance gain that would result from reconfiguring nodes of the DBMS with the configurations parameter sets. In addition, the system may determine a performance loss that would result from the respective databases of the nodes being inaccessible during reconfiguration with the configuration parameter sets. The system may select a reconfiguration plan in response to the performance gain and the performance loss satisfying a fitness criterion. The system may cause, at the reconfiguration times, the nodes to begin reconfiguration with the configuration parameter sets included in the selected reconfiguration plan.

Abstract: A reconstruction may be divided into a set of partial operations and scheduled in parallel using a distributed protocol which overlays a reduction tree to aggregate the results. In addition, a scheduling algorithm called multiple partial parallel repair is introduced for handling concurrent failures. Multiple reconstructions may be coordinated in parallel while working to minimize the conflict for shared resources.

Abstract: A cloud computing system which is configured to monitor operating status of a plurality of virtual computing machines running on a physical computing machine, wherein said monitoring includes monitoring a cycles per instruction (CPI) parameter and a cache miss rate (CMR) parameter of at least one of the plurality of virtual computing machines. The system detects interference in the operation of the at least one virtual machine, with the detection including determining when at least one of the CPI and CMR values exceed a predetermined threshold. When interference is detected, the system reconfigures a load balancing module associated with the virtual machine in question to send fewer requests to the virtual machine.

Abstract: A cloud computing system which is configured to monitor operating status of a plurality of virtual computing machines running on a physical computing machine, wherein said monitoring includes monitoring a cycles per instruction (CPI) parameter and a cache miss rate (CMR) parameter of at least one of the plurality of virtual computing machines. The system detects interference in the operation of the at least one virtual machine, with the detection including determining when at least one of the CPI and CMR values exceed a predetermined threshold. When interference is detected, the system reconfigures a load balancing module associated with the virtual machine in question to send fewer requests to the virtual machine.

Abstract: A reconstruction may be divided into a set of partial operations and scheduled in parallel using a distributed protocol which overlays a reduction tree to aggregate the results. In addition, a scheduling algorithm called multiple partial parallel repair is introduced for handling concurrent failures. Multiple reconstructions may be coordinated in parallel while working to minimize the conflict for shared resources.