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On Distribution and parallelism in Event Processing
Post 302277666 by Linux Bot on Saturday 17th of January 2009 11:10:08 AM
01-17-2009
On Distribution and parallelism in Event Processing
2009-01-17T14:03:00.005+02:00
This picture taken from the site of Nature Reviews as part of an article about Parallel processing in the mammalian retina illustrates that structures like the human body distribute the functions it needs to perform, and performs many of them in parallel and by specialized systems.
Getting to event processing, the producers and consumers of event processing can be distributed, as events can come to many sources, and situations may be consumed by many sinks. The first generation of event processing was mostly centralized in processing, the centralization has been twofold: functional centralization using an monolithic engine that performs all processing fucntions, and location centralization, this engine runs on a single server.
Today I'll concentrate on the second aspect of centralization, there are various reasons to decentralize the processing, one is to do some of the activities closer to the producers or consumers, example: if a producer produces events, where only 1% is relevant to the defined event processing, and it can be done by independent filtering that does not depend on other events, then it will be more efficient that the filtering will take place at or close to the consumer site, and thus eliminate the unnecessary network traffic.
Another reason to distribute the functionality is the scalability aspect, which is really an old idea to "divide and conquer" problems. The challenge is how to do a "good" partition. First there is a need to define what a "good" partition is, i.e. looking at it as an optimization problem, what is the goal function, then solving it is a function of the topology, semantics and behavior of a particular application which can be dynamic.
IBM has recently released the first version of WBEXS (Websphere Business Event Exterme Scale) and made a statement of direction for another product: Infostreme Streams
both are aimed to handle scalability by distribution in different environments. While details about IBM products you can obtain from the appropriate people in IBM, we in the IBM Haifa Research lab are working on related topics, we have exposed initial results in DEBS 2008, in the fast abstract session introducing the statification approach.The project has substantially advanced since that time, and I'll discuss it further in future Blogs (well - I need to go over the Blog and list all the topics I promised to discuss later and have not done so yet...).
More - later
Source...
This picture taken from the site of Nature Reviews as part of an article about Parallel processing in the mammalian retina illustrates that structures like the human body distribute the functions it needs to perform, and performs many of them in parallel and by specialized systems.Getting to event processing, the producers and consumers of event processing can be distributed, as events can come to many sources, and situations may be consumed by many sinks. The first generation of event processing was mostly centralized in processing, the centralization has been twofold: functional centralization using an monolithic engine that performs all processing fucntions, and location centralization, this engine runs on a single server.
Today I'll concentrate on the second aspect of centralization, there are various reasons to decentralize the processing, one is to do some of the activities closer to the producers or consumers, example: if a producer produces events, where only 1% is relevant to the defined event processing, and it can be done by independent filtering that does not depend on other events, then it will be more efficient that the filtering will take place at or close to the consumer site, and thus eliminate the unnecessary network traffic.
Another reason to distribute the functionality is the scalability aspect, which is really an old idea to "divide and conquer" problems. The challenge is how to do a "good" partition. First there is a need to define what a "good" partition is, i.e. looking at it as an optimization problem, what is the goal function, then solving it is a function of the topology, semantics and behavior of a particular application which can be dynamic.
IBM has recently released the first version of WBEXS (Websphere Business Event Exterme Scale) and made a statement of direction for another product: Infostreme Streams
both are aimed to handle scalability by distribution in different environments. While details about IBM products you can obtain from the appropriate people in IBM, we in the IBM Haifa Research lab are working on related topics, we have exposed initial results in DEBS 2008, in the fast abstract session introducing the statification approach.The project has substantially advanced since that time, and I'll discuss it further in future Blogs (well - I need to go over the Blog and list all the topics I promised to discuss later and have not done so yet...).
More - later
Source...
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LEARN ABOUT LINUX
httppower
httppower(8) powerman httppower(8) NAME
httppower - communicate with HTTP based power distribution units SYNOPSIS
httppower [--url URL] DESCRIPTION
httppower is a helper program for powerman which enables it to communicate with HTTP based power distribution units. It is run interac- tively by the powerman daemon. OPTIONS
-u, --url URL Set the base URL. INTERACTIVE COMMANDS
The following commands are accepted at the httppower> prompt: auth user:pass Authenticate to the base URL with specified user and password, using ``basic'' HTTP authentication which sends the user and password over the network in plain text. seturl URL Set the base URL. Overrides the command line option. get [URL-suffix] Send an HTTP GET to the base URL with the optional URL-suffix appended. post [URL-suffix] key=val[&key=val]... Send an HTTP POST to the base URL with the optional URL-suffix appended, and key-value pairs as argument. FILES
/usr/sbin/httppower /etc/powerman/powerman.conf ORIGIN
PowerMan was originally developed by Andrew Uselton on LLNL's Linux clusters. This software is open source and distributed under the terms of the GNU GPL. SEE ALSO
powerman(1), powermand(8), httppower(8), plmpower(8), vpcd(8), powerman.conf(5), powerman.dev(5), powerman-devices(7). http://sourceforge.net/projects/powerman powerman-2.3.5 2009-02-09 httppower(8)