Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A method of pipelining re-shuffled data of a distributed column oriented relational database management system (RDBMS). A request is received from a consumer process that requires RDBMS column data to be shuffled in a specific order according to an order that each of a plurality of columns will be used by the consumer process. For each of the plurality of columns, the method re-shuffles the RDBMS column data according to the specific order to form re-shuffled RDBMS column data, and sends the re-shuffled RDBMS column data to the consumer process.

Abstract: Embodiments of the present invention generate and optimize query plans that are at least partially executable in hardware. Upon receiving a query, the query is rewritten and optimized with a bias for hardware execution of fragments of the query. A template-based algorithm may be employed for transforming a query into fragments and then into query tasks. The various query tasks can then be routed to either a hardware accelerator, a software module, or sent back to a database management system for execution. For those tasks routed to the hardware accelerator, the query tasks are compiled into machine code database instructions.

Abstract: A container object data structure for storing metadata associated with multiple queues is provided for processing data elements in first-in, first-out fashion. In one embodiment, the container object is implemented in a database environment providing statement syntax for creating data objects, such as tables and views, to implement user schema. Queue metadata can comprise one or more pointers for data element access and control during one or more queue operations, such as an enqueue, dequeue, or update operation.