Abstract: An automated tuning service is used to automatically tune, or modify, the operational parameters of a large-scale cloud infrastructure. The tuning service performs automated and fully data/model-driven configuration based from learning various real-time performance of the cloud infrastructure. Such performance is identified through monitoring various telemetric data of the cloud infrastructure. The tuning service leverages a mix of domain knowledge and principled data-science to capture the essence of our cluster dynamic behavior in a collection of descriptive machine learning (ML) models. The ML models power automated optimization procedures for parameter tuning, and inform administrators in most tactical and strategical engineering/capacity decisions (such as hardware and datacenter design, software investments, etc.).

Abstract: An automated tuning service is used to automatically tune, or modify, the operational parameters of a large-scale cloud infrastructure. The tuning service performs automated and fully data/model-driven configuration based from learning various real-time performance of the cloud infrastructure. Such performance is identified through monitoring various telemetric data of the cloud infrastructure. The tuning service leverages a mix of domain knowledge and principled data-science to capture the essence of our cluster dynamic behavior in a collection of descriptive machine learning (ML) models. The ML models power automated optimization procedures for parameter tuning, and inform administrators in most tactical and strategical engineering/capacity decisions (such as hardware and datacenter design, software investments, etc.).

Abstract: Example aspects include techniques for query processing over deep neural network runtimes. These techniques may include receiving a query including one or more query operators and determining a query representation based on the one or more query operators. In addition, the techniques may include determining a neural network program based on the query representation, the neural network program including one or more neural network operators for performing the query in a neural network runtime, generating a neural network data structure based on a dataset associated with the query, and executing the neural network program in the neural network runtime over the neural network data structure to generate a query result.

Abstract: An automated tuning service is used to automatically tune, or modify, the operational parameters of a large-scale cloud infrastructure. The tuning service performs automated and fully data/model-driven configuration based from learning various real-time performance of the cloud infrastructure. Such performance is identified through monitoring various telemetric data of the cloud infrastructure. The tuning service leverages a mix of domain knowledge and principled data-science to capture the essence of our cluster dynamic behavior in a collection of descriptive machine learning (ML) models. The ML models power automated optimization procedures for parameter tuning, and inform administrators in most tactical and strategical engineering/capacity decisions (such as hardware and datacenter design, software investments, etc.).

Abstract: Methods, systems, and computer program products are provided for generating and utilizing materialized graph views. A system according to one embodiment includes a graph database including a graph and schema, a workload analyzer, a view enumerator, a query rewriter and an execution engine. The workload analyzer is configured to receive and analyze queries in a query workload. The view enumerator is configured to use an inference engine to operate on facts derived from the graph and a query, and view templates comprising inference rules to enumerate candidate views. The workload analyzer is further configured to selects a candidate view to materialize, provide the selected view to the execution engine that is configured to generate the materialized view. The workload analyzer may select the at least one candidate view based on factors such as query evaluation cost estimates, candidate view performance improvement estimates, view size estimates and view creation cost estimates.

Abstract: Methods, systems, and computer program products are provided for generating a neural network model. A ML pipeline parser is configured to identify a set of ML operators for a previously trained ML pipeline, and map the set of ML operators to a set of neural network operators. The ML pipeline parser generates a first neural network representation using the set of neural network operators. A neural network optimizer is configured to perform an optimization on the first neural network representation to generate a second neural network representation. A tensor set provider outputs a set of tensor operations based on the second neural network representation for execution on a neural network framework. In this manner, a traditional ML pipeline can be converted into a neural network pipeline that may be executed on an appropriate framework, such as one that utilizes specialized hardware accelerators.

Abstract: Embodiments for efficient queue management for cluster scheduling and managing task queues for tasks which are to be executed in a distributed computing environment. Both centralized and distributed scheduling is provided. Task queues may be bound by length-based bounding or delay-based bounding. Tasks may be prioritized and task queues may be dynamically reordered based on task priorities. Job completion times and cluster resource utilization may both be improved.

Abstract: The description relates to executing an inference query relative to a database management system, such as a relational database management system. In one example a trained machine learning model can be stored within the database management system. An inference query can be received that applies the trained machine learning model on data local to the database management system. Analysis can be performed on the inference query and the trained machine learning model to generate a unified intermediate representation of the inference query and the trained model. Cross optimization can be performed on the unified intermediate representation. Based upon the cross-optimization, a first portion of the unified intermediate representation to be executed by a database engine of the database management system can be determined, and, a second portion of the unified intermediate representation to be executed by a machine learning runtime can be determined.

Abstract: Described herein is a system and method for ranking and/or taking an action regarding execution of jobs of a shared computing cluster based upon predicted user impact. Information regarding previous executions of a plurality of jobs is obtained, for example, from job execution log(s). Data dependencies of the plurality of jobs are determined. Job impact of each of the plurality of jobs as a function of the determined data dependencies is calculated. User impact of each of the plurality of jobs as a function of the determined data dependencies, the calculated job impact, and time is calculated. The plurality of jobs are ranked in accordance with the calculated user impact. An action is taken in accordance with the ranking of the plurality of jobs. The action can include automatic scheduling of the jobs and/or providing information regarding the rankings to a user.

Abstract: Methods, systems, and computer program products are provided for generating and utilizing materialized graph views. A system according to one embodiment includes a graph database including a graph and schema, a workload analyzer, a view enumerator, a query rewriter and an execution engine. The workload analyzer is configured to receive and analyze queries in a query workload. The view enumerator is configured to use an inference engine to operate on facts derived from the graph and a query, and view templates comprising inference rules to enumerate candidate views. The workload analyzer is further configured to selects a candidate view to materialize, provide the selected view to the execution engine that is configured to generate the materialized view. The workload analyzer may select the at least one candidate view based on factors such as query evaluation cost estimates, candidate view performance improvement estimates, view size estimates and view creation cost estimates.

Abstract: Described herein is a system and method for selecting subexpressions to be materialized. For a predefined storage budget, subexpressions of a set of candidate subexpressions to be materialized to minimize query evaluation cost are selected based upon a calculated utility of the set of candidate subexpressions, interactions of the candidate subexpressions, and, a cost of evaluating the candidate subexpressions. Based upon the subexpressions selected to be materialized, subexpression(s) of the set of candidate subexpressions to use when evaluating particular queries of the set of queries to minimize query evaluation cost are determined.

Abstract: Described herein is a system and method for ranking and/or taking an action regarding execution of jobs of a shared computing cluster based upon predicted user impact. Information regarding previous executions of a plurality of jobs is obtained, for example, from job execution log(s). Data dependencies of the plurality of jobs are determined. Job impact of each of the plurality of jobs as a function of the determined data dependencies is calculated. User impact of each of the plurality of jobs as a function of the determined data dependencies, the calculated job impact, and time is calculated. The plurality of jobs are ranked in accordance with the calculated user impact. An action is taken in accordance with the ranking of the plurality of jobs. The action can include automatic scheduling of the jobs and/or providing information regarding the rankings to a user.

Abstract: Described herein is a system and method for selecting subexpressions to be materialized. For a predefined storage budget, subexpressions of a set of candidate subexpressions to be materialized to minimize query evaluation cost are selected based upon a calculated utility of the set of candidate subexpressions, interactions of the candidate subexpressions, and, a cost of evaluating the candidate subexpressions. Based upon the subexpressions selected to be materialized, subexpression(s) of the set of candidate subexpressions to use when evaluating particular queries of the set of queries to minimize query evaluation cost are determined.

Abstract: Embodiments for efficient queue management for cluster scheduling and managing task queues for tasks which are to be executed in a distributed computing environment. Both centralized and distributed scheduling is provided. Task queues may be bound by length-based bounding or delay-based bounding. Tasks may be prioritized and task queues may be dynamically reordered based on task priorities. Job completion times and cluster resource utilization may both be improved.

Abstract: In one embodiment, a computer-implemented method includes selecting one or more sub-expressions of a query during compile time. One or more pilot runs are performed by one or more computer processors. The one or more pilot runs include a pilot run associated with each of one or more of the selected sub-expressions, and each pilot run includes at least partial execution of the associated selected sub-expression. The pilot runs are performed during execution time. Statistics are collected on the one or more pilot runs during performance of the one or more pilot runs. The query is optimized based at least in part on the statistics collected during the one or more pilot runs, where the optimization includes basing cardinality and cost estimates on the statistics collected during the pilot runs.

Abstract: In one embodiment, a computer-implemented method includes selecting one or more sub-expressions of a query during compile time. One or more pilot runs are performed by one or more computer processors. The one or more pilot runs include a pilot run associated with each of one or more of the selected sub-expressions, and each pilot run includes at least partial execution of the associated selected sub-expression. The pilot runs are performed during execution time. Statistics are collected on the one or more pilot runs during performance of the one or more pilot runs. The query is optimized based at least in part on the statistics collected during the one or more pilot runs, where the optimization includes basing cardinality and cost estimates on the statistics collected during the pilot runs.