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Enhancing the Performance Predictability of Grid Applications with Patterns & Process Algebras

Enhance

http://homepages.inf.ed.ac.uk/stg/research/ENHANCE/

One of the most promising technical innovations in present-day computing is the invention of Grid technologies which harness the computational and storage power of widely distributed collections of computers. Grid technologies are breaking new ground in e-Science where scientists across the globe can collaborate and share data sets, processing power and specialised scientific instruments to accelerate the pace of scientific development. Grid technologies are also opening new doors in e-Business where small or medium-scale businesses can now have access to supercompting power which was once the province of only the most wealthy banks and multi-nationals. Data and resource sharing schemes such as these are changing the basic ground rules of computing.

All such schemes raise difficult issues of resource allocation and scheduling (roughly speaking, how to decide which computer does what, and when, and how they interact) which are made all the more complex by the inherent unpredictability of resource availability and performance. For example, I may forget to switch on my PC, a supercomputer may be required for a more important task, the internet connections I need may be particularly busy.

The Enhance project aims to simplify the effective programming of such systems by exploiting and synthesizing results from two underlying research programmes. Stochastic process algebras such as PEPA are used to model the behaviour of concurrent systems in which some aspects of behaviour are not precisely predictable. Pattern based programming recognizes that many real applications draw from a range of well known solution paradigms and seeks to make it easy for an application developer to tailor such a paradigm to a specific problem without re-inventing the wheel. The choice of a particular paradigm carries with it considerable information about implied scheduling dependencies. By modelling these with PEPA, and thereby being able to include aspects of uncertainty which are inherent to Grid computing, we believe that we will be able to underpin systems which can make better scheduling and dynamic rescheduling decisions than less sophisticated approaches.

Maturity: Initial Research
Region: UK
Type: Open Call
Grant Value: £182,357.00
Start Date: 01/06/2003
End Date: 31/05/2006
Project Status: completed
Funding Agency: EPSRC


Project Members
PI
Dr Murray Cole


Collaborating Organisations
School of Physics (University of Edinburgh)


Component(s) Project Develops
This project is not associated with any components at present.


Application Area(s) associated with Project
This project is not associated with any application areas at present.

 

Last Updated: 22 Jun 12 10:56
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