Euro-Par 2008 Workshops - Parallel Processing: VHPC 2008, by Michael Alexander, Stephen Childs (auth.), Eduardo César,

By Michael Alexander, Stephen Childs (auth.), Eduardo César, Michael Alexander, Achim Streit, Jesper Larsson Träff, Christophe Cérin, Andreas Knüpfer, Dieter Kranzlmüller, Shantenu Jha (eds.)

This e-book constitutes the completely refereed joint post-workshop lawsuits of 7 overseas occasions, held in Las Palma de Gran Canaria, Spain, in August 2008 in the scope of Euro-Par 2008, the 14th foreign convention on Parallel Computing.

The forty three papers awarded including 7 keynote lectures have been rigorously reviewed and chosen in the course of rounds of reviewing and development from a variety of displays on the following workshops: the third Workshop on Virtualization in High-Performance Cluster and Grid Computing (VHPC 2008), the UNICORE Summit 2008, the overseas Workshop on hugely Parallel Processing on a Chip (HPPC 2008), the Workshop on safe, depended on, practicable and Controllable Grid prone (SGS 2008), the Workshop on productiveness and function (PROPER 2008), the Workshop on Real-Time on-line Interactive purposes at the Grid (ROIA 2008), and the Workshop on Abstractions for dispensed structures (DPA 2008).

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Additional info for Euro-Par 2008 Workshops - Parallel Processing: VHPC 2008, UNICORE 2008, HPPC 2008, SGS 2008, PROPER 2008, ROIA 2008, and DPA 2008, Las Palmas de Gran Canaria, Spain, August 25-26, 2008, Revised Selected Papers

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SR: SGE registering. 88 Fig. 2. Deployment overhead for simultaneous and sequential approaches Shut Down Overhead. When shutting down a VM (Table 3), we have measured three relevant values. In this case the time is constant regardless of the number of VMs being shut down. Overhead introduced by SGE when shutting down a VM can be minimized by reducing the polling time. Default value is 300 seconds, so it takes an average of 150 seconds to detect the new situation. Reducing polling time limits the overhead, although increments network usage.

To do this the framework is able to dynamically vary resources available to the application. To facilitate performance optimization an application performance profile can be created using stress-testing tools. A software toolkit that allows running single and multiple VM applications has been created. Sample services (including both computing oriented and web oriented) have been tested and performance-resource dependences studied. We present an ongoing work on dynamic resource allocation, involving optimal control and optimization methods.

6. After a given time, the Grid information system (MDS) detects the new node and publishes it. This step is labeled “PUB” in Figure 1. 7. Finally, the meta-scheduler (GridWay) will refresh the information of the available Grid resources, and detect the new worker node (label “GW“). Then, according to the scheduling policies, it will allocate jobs on this new resource by interfacing with the Grid execution service (GRAM). 1 M. Rodr´ıguez et al. 1. The testbed consists of three resources: two SGE clusters, and a dedicated system hosting the management services (meta-scheduler and infrastructure).

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