The patent's inventors are Huetter, Raymond J. (
This patent was filed on
From the background information supplied by the inventors, news correspondents obtained the following quote: "Explosive growth in global data volume, ubiquity of devices connecting to networks, lower barriers of entry for user content creation and sharing, digitization of many formerly offline functions (banking, medical prescriptions, etc.), emergence of virtualized and offsite networked systems (clouds), among other factors, have contributed to the emergence of the 'Big Data' era. This presents challenges for systems, as applications processing faces extreme massive volume throughput and requirements to deliver or distribute processed data to any number of destination points. These systems additionally must be scalable to keep pace with the continuing growth of Big Data and enable interactivity for pervasive, large audience Internet and cloud applications.
"The utilization of multi-core processors has increased dramatically in the computing industries. In general, the term 'processor' refers to the unit of hardware that reads and executes program instructions. Historically, processors originally utilized a single 'core,' which refers to the portion of the processor that reads and executes a sequence of instructions. A multi-core processor refers to a single hardware unit in which two or more independent processing 'cores' are integrated onto a single package. Recently, computing systems having upwards of 128 to 256 processing cores have become available. Such multi-core computing platforms present challenges over traditional programming techniques."
Supplementing the background information on this patent, VerticalNews reporters also obtained the inventors' summary information for this patent: "In general, this disclosure is directed to a software virtual machine that provides high-performance transactional data acceleration optimized for multi-core computing platforms. The virtual machine utilizes an underlying parallelization engine that seeks to maximize the efficiencies of multi-core computing platforms to provide a highly scalable, high performance (lowest latency) virtual machine.
"In some embodiments, the underlying parallelization engine of the software virtual machine provides self-organization in its ability to parallelize and store relevant data for transaction processing to data partitions each associated with different execution units for the software virtual machine. In addition, tasks collectively processing transactions and corresponding transactional data provide self-tuning in their ability to autonomously determine and migrate among execution units that process the tasks. As a result, the software virtual machine may employ multiple distributed transaction delegation units and so avoid both a centralized transaction administrator to manage data organization and transactional delegation and the inherent bottlenecks associated with such centralized administration.
"These techniques may be useful in systems required to address the particular needs of dynamic and interactive data acceleration for large audience web applications and 'Big Data' clouds. In particular, a system that implements the described techniques can aggregate transactional data to effectively manage ingestion of massive data emanating from manifold sources and received by the system as well as disaggregate transactional data to deliver processed data to select destinations. For example, a unique data communications feature is the platform's ability to push interactively push broadcast selected data to individual devices (users) and create interactive private broadcast sessions (channels) within a homogenous mass broadcast data stream. The platform techniques may also enable customer-provided transaction and messaging application acceleration in a system operating a scalable, in-memory database cache with integrated on-demand, real-time indefinite run-time extension to secondary storage. Such a system may leverage platform techniques for scaling out the cache beyond physical in-memory boundaries and, when required, integrate as part of the virtual machine memory boundaries an automatic extension and use of physically external memory devices (e.g., hard drives). The software virtual machine described herein, in other words, supports a move from `static information architectures` that have difficulty supporting or creating value from Big Data to a dynamic architecture model. With low latency, scalable processing in conjunction with reduced complexity and increased cost effectiveness, the described techniques specifically address the conditions of Big Data processing to provide the ability to concurrently consume and process massive transaction volumes from large numbers of data producers along with the ability to push processed data to billions of data consumers in an interactive manner.
"In one example, a device comprises a multi-core hardware processor having a plurality of execution cores. The device also comprises an in-memory database comprising data stored within a plurality of memory partitions, wherein each of the memory partitions is associated with a different one of the execution cores. The device further comprises a content delivery engine that configures a plurality of private data channels to each deliver unique data to a corresponding one of a plurality of data consumers. The device also comprises a parallelization engine that deploys a plurality of tasks to concurrently execute on the cores to concurrently perform transactions on the in-memory database in response to queries from the content delivery engine.
"In another example, a method comprises configuring, with a content delivery engine, a plurality of private data channels to each deliver unique data to a corresponding one of a plurality of data consumers. The method also comprises deploying, with a parallelization engine executing on a multi-core hardware processor having a plurality of execution cores, a plurality of tasks to concurrently execute on the cores to concurrently perform transactions on an in-memory database in response to queries from the content delivery engine, wherein the in-memory database comprises data stored within a plurality of memory partitions, wherein each of the memory partitions is associated with a different one of the execution cores.
"In another example, a computer-readable storage device comprises instructions that, when executed, cause a multi-core hardware processor having a plurality of execution cores to configure, with a content delivery engine, a plurality of private data channels to each deliver unique data to a corresponding one of a plurality of data consumers. The instructions, when executed, also cause the multi-core hardware processor to deploy, with a parallelization engine, a plurality of tasks to concurrently execute on the cores to concurrently perform transactions on an in-memory database in response to queries from the content delivery engine, wherein the in-memory database comprises data stored within a plurality of memory partitions, wherein each of the memory partitions is associated with a different one of the execution cores.
"The details of one or more embodiments of the invention are set forth in the accompanying drawings and the description below. Other features, objects, and advantages of the invention will be apparent from the description and drawings, and from the claims."
For the URL and additional information on this patent, see: Huetter, Raymond J.; Yamarti, Alka. Software Virtual Machine for Content Delivery. U.S. Patent Number 8645958, filed
Keywords for this news article include: uCIRRUS, Software.
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