Future Wei Technologies, Inc.

Oracle International Corporation

Chi Young Ku

Masood Mortazavi

Stephen P Morgan

Guangyu Shi

Jonathan D Klein

Sreenivas Gollapudi

Ari Wolfe Mozes

Arvind Nithrakashyap

Ron-Chung Hu

Mengmeng Chen

DaQi Ren

A device comprises a memory storage comprising instructions and one or more processors in communication with the memory. The one or more processors execute the instructions to access data for a plurality of previous time periods for a client of a plurality of clients, the data for each previous time period of the plurality of previous time periods including resource usage data for a plurality of jobs of the client, apply a time series analysis algorithm to the accessed data to predict a distribution of characteristics of resource usage for a future time period for the client, based on the predicted distribution of characteristics of resource usage, simulate a number of future predicted workloads for the future time period for the client, and based on the simulated number of future predicted workloads, automatically reserve a set of resources for the future time period for the client.

Method of dynamic resource allocation for public clouds

A method comprising receiving a plurality of data records, storing the data records as data segments in a storage element, receiving a plurality of descriptors for each data segment, wherein each descriptor describes an aspect of data contained in the data segments, employing a first user-defined function to resolve a first minimum descriptor for each data segment and a first maximum descriptor for each data segment, composing a lightweight index for the data segments, wherein the lightweight index comprises the first minimum descriptor for each data segment and the first maximum descriptor for each data segment, and appending the lightweight index to the data segments in the storage element.

Serialization scheme for storing data and lightweight indices on devices with append-only bands

A method of dynamically computing an optimal materialization schedule for each column in a column oriented RDBMS. Dynamic column-specific materialization scheduling in a distributed column oriented RDBMS is optimized by choosing a materialization strategy based on execution cost including central processing unit (CPU), disk, and network costs for each individual exchange operator. The dynamic programming approach is computationally feasible because the optimal schedule for a sub-plan is path independent.

System and method for column-specific materialization scheduling

A method implemented by a network element (NE) in a network, comprising composing a first network storage entity by mapping a plurality of logical storage units to a plurality of physical storage units in a physical storage system according to a first storage metric associated with the plurality of physical storage units, arranging the plurality of logical storage units sequentially to form a logical circular buffer, and designating a current logical storage unit for writing data and an upcoming logical storage unit for writing data after the current storage unit is fully written, and rebalancing the physical storage system while the physical storage system is actively performing network storage operations by relocating at least one of the logical storage units to a different physical storage unit according to a second storage metric associated with the plurality of physical storage units.

Rebalancing and elastic storage scheme with elastic named distributed circular buffers

A method comprising receiving one or more data storage specifications from a tenant, determining that a plurality of physical storage units in a physical storage system comprises a sufficient system capacity to provision data storage for the tenant, sending a storage request message to request creation of a network storage entity for the tenant according to the data storage specifications, and receiving a storage response message indicating a first of a plurality of logical storage units allocated to the network storage entity according to the data storage specifications, wherein the plurality of logical storage units are distributed across the plurality of physical storage units, and wherein the plurality of logical storage units are arranged in a sequential order to form a logical circular buffer.

Large-scale storage and retrieval of data with well-bounded life

A shingled magnetic recording (SMR) append-only file system includes a disk comprising a plurality of concentric append-only shingled data bands having partially overlapping data tracks, wherein the data bands are associated with a circular linked list having a head data band and a tail data band, the head data band and the tail data band each comprising a plurality of data blocks. The system also includes a processor configured to write data blocks to the disk, and create a new file wherein an empty data band of the plurality of data bands is removed from the circular linked list and added to a single linked list of the new file.

SMR-aware append-only file system

A number, of the blocks of data to be prefetched into a buffer cache, is determined dynamically at run time (e.g. during execution of a query), based at least in part on the load placed on the buffer cache. An application program (such as a database) is responsive to the number (also called 'prefetch size'), to determine the amount of prefetching. A sequence of instructions (also called 'prefetch size daemon') computes the prefetch size based on, for example, the number of prefetched blocks aged out before use. The prefetch size daemon dynamically revises the prefetch size based on usage of the buffer cache, thereby to form a feedback loop. Depending on the embodiment, at times of excessive use of the buffer cache, prefetching may even be turned off. Although in one embodiment described herein the prefetch size daemon is implemented in a database, in other embodiments other kinds of applications and/or the operating system itself can use a prefetch size daemon of the type described herein to dynamically determine and change prefetch behavior.

System load based adaptive prefetch

A method and apparatus for efficiently transmitting a result set. A data server receives a data request from a client or another server. The data server determines whether a threshold portion of the contents of each requested row or record is being retrieved (e.g., X% of the number of columns in a table or X% of the total amount of data in a table row). The requested rows are retrieved in the same format in which they are stored (e.g., within a disk block), and streamed to the client, one row at a time. The client is then responsible for disassembling a row into its columns, reordering them if necessary, and performing any necessary post-processing (e.g., datatype conversion, data formatting) before consuming the data.

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San Ramon, CA, United States of America

Chi Young Ku