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Distributed computing

a distributed computing and computing technology, applied in the field of high-performance computing, can solve the problems of low priority background jobs and restricted utilization to overnight idle periods, and achieve the effects of increasing integration and deployment, on-going savings in maintenance and administrative overhead, and enormous savings

Inactive Publication Date: 2006-08-31
BERNARDIN JAMES +1
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Benefits of technology

"The invention provides a solution for commercial users who need to integrate multiple applications with parallel tasks. It offers an off-the-shelf product that eliminates the need for customized middleware and reduces maintenance and administrative overhead. The invention utilizes a peer-to-peer messaging implementation and a powerful underlying SDK to seamlessly incorporate enterprise features like fault-tolerant scheduling and idle detection. It also adapts to changing priorities and provides both serial and interleaved priority execution for jobs of the same priority. The invention allows for unlimited scaling over commoditized resource pools and cancels redundant instances of bottleneck or long-running tasks to ensure reliable service levels."

Problems solved by technology

(In contrast, previous efforts to harness idle capacity have run low-priority background jobs, restricted utilization to overnight idle periods, or imposed intrusive measures, such as checkpointing.)

Method used

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  • Distributed computing
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Examples

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example 1

[0263] Discriminators may be attached to Tasks or Jobs to ensure that they are assigned to Engines that are equipped with specific resources. Common examples include: Tasks that must run under a particular operating system or subset of operating systems; Tasks that must have at least a specified minimum of memory or disk space to run; Tasks that must run on a specific subset of Engines because of administrative or security restrictions, possibly including database or file system access restrictions; etc. Discriminators may also be used to impose analogous restrictions in order to optimize performance. For example, they may restrict very long-running Tasks to run only on processors meeting minimum performance requirements or on dedicated (as opposed to interruptible) Engines.

example 2

[0264] Scoring Discriminators may be used in connection with distributed caching to maximize reuse of distributed resources, such as objects or data. In this case, the Engines maintain a local cache, and update their associated property list whenever entries are added or removed from the cache. The Scoring Discriminator associated with each Task returns a score for each Engine based on the overlap between the resources that are available in the Engine's local cache and the resources specifically required by the Task.

[0265] The simplest examples of this kind are those in which each Task requires a single object, for example, each Task within a portfolio pricing application might look for a single deal or subportfolio. In this case, the Scoring Discriminator might return 1 in case the relevant deal or subportfolio is present in the cache, and 0 otherwise. A slightly more complex example would be one in which the Scoring Discriminator examines the Engine property list for multiple key...

example 3

[0266] Engine Discriminators may be used to dedicate some number, N, of Engines to a specific Job. This is accomplished by having the first N Tasks within the Job install an Engine Discriminator that imposes two requirements: (1) The Job Id must match the Job Id for the given Task, and (2) the Task Id must be greater than N.

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Abstract

A distributed computing system manages execution of jobs and their associated tasks. Multiple scheduling strategies respect job priority preferences. A graphical user interface allows viewing of job status information and on-the-fly modification of job priorities.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS [0001] This application is a continuation of U.S. patent application Ser. No. 10 / 306,689 (“Adaptive Scheduling”), filed on Nov. 27, 2002 by the inventors herein.FIELD OF THE INVENTION [0002] The present invention relates generally to the field of high-performance computing (“HPC”); more specifically, to systems and techniques for distributed and / or parallel processing. BACKGROUND OF THE INVENTION [0003] HPC has long been a focus of both academic research and commercial development, and the field presents a bewildering array of standards, products, tools, and consortia. Any attempt at comparative analysis is complicated by the fact that many of these interrelate not as mutually exclusive alternatives, but as complementary component or overlapping standards. [0004] Probably the most familiar, and certainly the oldest, approach is based on dedicated supercomputing hardware. The earliest supercomputers included vector-based array processors, whose...

Claims

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Application Information

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Patent Type & Authority Applications(United States)
IPC IPC(8): G06F15/16G06F9/50
CPCG06F9/505G06F2209/503
Inventor BERNARDIN, JAMESLEE, PETER
Owner BERNARDIN JAMES
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