Distributed and Parallel Systems: Cluster and Grid Computing by Andrzej M. Goscinski (auth.), Péter Kacsuk, Dieter

By Andrzej M. Goscinski (auth.), Péter Kacsuk, Dieter Kranzlmüller, Zsolt Németh, Jens Volkert (eds.)

Distributed and Parallel platforms: Cluster and Grid Computing is the complaints of the fourth Austrian-Hungarian Workshop on disbursed and Parallel structures equipped together via Johannes Kepler college, Linz, Austria and the MTA SZTAKI desktop and Automation study Institute.

The papers during this quantity disguise a huge diversity of analysis themes provided in 4 teams. the 1st one introduces cluster instruments and strategies, specifically the problems of load balancing and migration. one other six papers take care of grid and worldwide computing together with grid infrastructure, instruments, functions and cellular computing. the following 9 papers current common questions of allotted improvement and purposes. The final 4 papers deal with an important factor in dispensed computing: fault tolerance and loyal platforms.

This quantity should be worthy to researchers and students drawn to all components regarding parallel and allotted computing structures.

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Example text

The JVMs containing more than the average of the number of objects generally tend to remove objects to JVMs containing fewer objects than the average. 3, objects rarely move from one JVM to another, a case which corresponds to different fluctuations. In the balanced case, when considering a initial distribution de, the overhead of 2% up to 7% (depending on the frequency of an imbalance checking) measures both the observation mechanism overhead and the overhead due to the load balancing mechanism.

T h e mean d" 2::~ em IS eVIatIOn, d efi ne d as em = -1 nIX1-ml . Exploiting Runtime Information in Load Balancing Strategies 27 If cv :s; cst then there is no imbalance, and if cv > cst there is an imbalance and the previous algorithm is applied in order to detect the too small and too big values. cv does not depend on the data, so the cst constant does not depend on the application, and refers to unbalanced distributions. 5) in order to tune parameters. A learning approach could be used in order to adapt the cst constant to allow more or fewer differences in the analysed data.

V. Kale. Run-time Support for Adaptive Load Balancing. In IPDPS 2000 Workshops - LNCS 1800, pages 1152-1159. [3] A. Bouchi, R. Olejnik, and B. Toursel. A New Estimation Method for Distributed Java Object Activity. In IPDPS 2002 - Workshop on Java for Parallel and Distributed Computing. [4] A. Bouchi, R. Olejnik, and B. Toursel. Java Tools for Measurement of the Machine Loads. In NATO ARWR 2001 - Advanced Environments, Tools and Applications for Cluster Computing, September 2001. [5] A. Corradi, L.

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