Oracle International Corporation

Michael Zoll

Angelo Pruscino

Wilson Wai Shun Chan

Yaser I Suleiman

Wilson Wai Chan

Stefan Heinrich Roesch

Subhransu Basu

Wataru Miyoshi

Thomas Herter

Wolfgang Lohwasser

Thomas Breidt

Klaus Thielen

Sahil Kumar

Carol L Colrain

Tak Fung Wang

Tolga Yurek

Rajkumar Arockia Irudayaraj

Fulu Li

Eugene Reddy Ho

Logan O'Sullivan Bruns

Vladimir Begun

Amit Ganesh

Juan R Loaiza

Tirthankar Lahiri

Vineet Marwah

Dheeraj Pandey

Sameer Arun Joshi

Neil MacNaughton

Neil J S MacNaughton

Kiran Badrinarain Goyal

Chandrajith Ramachandran Unnithan

Mohsin Beg

Wai Shun Wilson Chan

WeiHsin Gu

Kotaro Ono

Herbert Van Den Bergh

Troy Anthony

Atsushi Morimura

Chern Yih Cheah

Disclosed is a method, a computer program product, and a computer system for implementing automatic connection load balancing of database connections between instances of a cluster. Load imbalances are detected globally in the cluster, and can be corrected autonomously by the local instances. The local controller/orchestrator detects the imbalance, computes a target for the instance, and makes any necessary adjustments on the instance, where a server marks the connections to redistribute, and then implements draining and/or planned failover to move sessions.

Automatic connection load balancing between instances of a cluster

Described is an approach that provides an adaptive solution to missing data for machine learning systems. A gradient solution is provided that is attentive to imputation needs at each of several missingness levels. This multilevel approach treats data missingness at any of multiple severity levels while utilizing, as much as possible, the actual observed data.

Method and system for adaptively imputing sparse and missing data for predictive models

Described is an improved approach to remove data outliers by filtering out data correlated to detrimental events within a system. One or more detrimental even conditions are defined to identify and handle abnormal transient states from collected data for a monitored system.

Method and system for adaptively removing outliers from data used in training of predictive models

Described is an approach for performing context-aware prognoses in machine learning systems. The approach harnesses streams of detailed data collected from a monitored target to create a context, in parallel to ongoing model operations, for the model outcomes. The context is then probed to identify the particular elements associated with the model findings.

Method and system for performing context-aware prognoses for health analysis of monitored systems

Described is an improved approach to implement selection of training data for machine learning, by presenting a designated set of specific data indicators where these data indicators correspond to metrics that end users are familiar with and are easily understood by ordinary users and DBAs within their knowledge domain. Selection of these indicators would correlate automatically to selection of a corresponding set of other metrics/signals that are less understandable to an ordinary user. Additional analysis of the selected data can then be performed to identify and correct any statistical problems with the selected training data.

Method and system for cleansing training data for predictive models

A method and apparatus is provided for dynamically generating data to dataset mappings and dataset to logical instance mappings and distributing these mappings to subscribers for the purpose of routing work. An advisory engine updates the mappings of data to data sets based at least in part on changing demands and patterns of access to data, and updates the mappings of datasets to logical instances based at least in part on anticipated capacity and change in conditions of the database servers. The subscribers receive and store the mapping information that maps the data to dataset identifiers, and dataset identifiers to logical database instances. The subscribers may also receive information that describes the current availability conditions of database instances, which are used to override the mapping of logical database instances to physical database instances. Subscribers may use the data to dataset mapping to map data identified from in an incoming database request to a dataset. Subscribers may use the dataset to logical instance mapping to map a dataset to a possible database server, and the logical instance to physical instance mapping to map a possible database server to an available database server. In this manner, the subscribers may route data efficiently even when access patterns and server conditions change.

Adaptively routing transactions to servers

A method, system, and computer program product for generating database cluster health alerts using machine learning. A first database cluster known to be operating normally is measured and modeled using machine learning techniques. A second database cluster is measured and compared to the learned model. More specifically, the method collects a first set of empirically-measured variables of a first database cluster, and using the first set of empirically-measured variables a mathematical behavior predictor model is generated. Then, after collecting a second set of empirically-measured variables of a second database cluster over a plurality of second time periods, the mathematical behavior predictor model classifies the observed behavior. The classified behavior might be deemed to be normal behavior, or some form of abnormal behavior. The method forms and report alerts when the classification deemed to be anomalous behavior, or fault behavior. A Bayesian belief network predicts the likelihood of continued anomalous behavior.

Generating database cluster health alerts using machine learning

An approach is disclosed for implementing failover and resume when using ordered sequences in a multi-instance database environment. The approach commences by instantiating a first database instance initially to serve as an active instance, then instantiating a second database instance to serve as an instance of one or more passive instances. The active database establishes mastership over a sequence and then processes requests for the 'next' symbol by accessing a shared sequence cache only after accessing a first instance semaphore. The active instance and the passive instance perform a protocol such that upon passive database detection of a failure of the active database, one of the passive database instances takes over mastership of the sequence cache, and then proceeds to satisfy sequence value requests. The particular order is observed in spite of the failure.

Failover and resume when using ordered sequences in a multi-instance database environment

A method, system, and computer program product for analyzing performance of a database cluster. Disclosed are techniques for analyzing performance of components of a database cluster by transforming many discrete event measurements into a time series to identify dominant signals. The method embodiment commences by sampling the database cluster to produce a set of timestamped events, then pre-processing the timestamped events by tagging at least some of the timestamped events with a semantic tag drawn from a semantic dictionary and formatting the set of timestamped events into a time series where a time series entry comprises a time indication and a plurality of values corresponding to signal state values. Further techniques are disclosed for identifying certain signals from the time series to which is applied various statistical measurement criteria in order to isolate a set of candidate signals which are then used to identify indicative causes of database cluster behavior.

Analyzing database cluster behavior by transforming discrete time series measurements

A method, system, and computer program product is disclosed for generating an ordered sequence from a predetermined sequence of symbols using protected interleaved caches, such as semaphore protected interleaved caches. The approach commences by dividing the predetermined sequence of symbols into two or more interleaved caches, then mapping each of the two or more interleaved caches to a particular semaphore of a group of semaphores. The group of semaphores is organized into bytes or machine words for storing the group of semaphores into a shared memory, the shared memory accessible by a plurality of session processes. Protected (serialized) access by the session processes is provided by granting access to one of the two or more interleaved caches only after one of the plurality of session processes performs a semaphore altering read-modify-write operation (e.g., a CAS) on the particular semaphore. The interleaved caches are assigned values successively from the predetermined sequence using a round-robin assignment technique.

Generating an ordered sequence in a database system using multiple interleaved caches

Systems, methods, and other embodiments associated with avoiding resource blockages and hang states are described. One example computer-implemented method for a computing system includes determining that a first process is waiting for a resource and is in a blocked state. The resource that the first process is waiting for is identified. A blocking process that is holding the resource is then identified. A priority of the blocking process is compared with a priority the first process. If the priority of the blocking process is lower than the priority of the first process, the priority of the blocking process is increased. In this manner the blocking process can be scheduled for execution sooner and thus release the resource.

Global avoidance of hang states via priority inheritance in multi-node computing system

Techniques for monitoring resources of a computer system are provided. A monitoring process collects and reports utilization data for one or more resources of a computer system, such as CPU, memory, disk I/O, and network I/O. Instead of reporting just an average of the collected data over a period of time (e.g., 10 seconds), the monitoring process at least reports individually collected resource utilization values. If one or more of the utilization values exceed specified thresholds for the respective resources, then an alert may be generated. In one approach, the monitoring process is made a real-time priority process in the computer system to ensure that the memory used by the monitoring process is not swapped out of memory. Also, being a real-time priority process ensures that the monitoring process obtains a CPU in order collect resource utilization data even when the computer system is in a starvation mode.

Real-time database exception monitoring tool using instance eviction data

Described herein are techniques for time limited lock ownership. In one embodiment, in response to receiving a request for a lock on a shared resource, the lock is granted and a lock lease period associated with the lock is established. Then, in response to determining that the lock lease period has expired, one or more lock lease expiration procedures are performed. In many cases, the time limited lock ownership may prevent system hanging, timely detect system deadlocks, and/or improve overall performance of the database.

Time limited lock ownership

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Foster City, CA, United States of America

Michael Zoll