By Lee-hing Yan, Lee Bu Sung, Teo Yong Meng
The decade has obvious a considerable raise in commodity laptop and community functionality. more and more, computing addresses collaboration, cycle and information sharing and different modes of interplay regarding disbursed assets. Grid computing is an rising expertise that allows large-scale sharing of broadly disbursed assets and coordinated problem-solving and collaboration among teams of scientists. using at the good fortune of the 1st workshops, this year's workshop keeps the culture of supplying an invaluable discussion board for dialogue between researchers, builders and clients of grid computing from academia, enterprise and undefined. This quantity is a suite of the overseas contributions provided on the workshop, with a spotlight on grid computing and its purposes in technology and engineering.
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Extra resources for GCA 2007: Proceedings of the 3rd International Workshop on Grid Computing and Applications, Biopolis, Singapore, 5-8 June 23007
Scheduling Algorithm Figure 1 is the pseudo-code of the job scheduling algorithm using the proposed reputation assessment and reputation balancing strategy. When the number of jobs in the job set becomes a fixed maximum number, like 100, we call this a scheduling event. When scheduling event occurs, the scheduler first computes RV j , RV , and k j . Then, the scheduler schedule tasks to host m j . At the same time, the status of entire running jobs is recorded and stored in the job table. If the scheduler receives a job completion message from one of the remote host, it will record the job information in the job table and update its accumulative successful execution rate.
When the number of ﬁles stored on the metadata server is small, the overhead of the metadata access via the gfarm agent is dominant. When huge numbers of ﬁles are stored on the Gfarm ﬁlesystem, the gfarm agent caches the directory information so that the Gfarm client accesses them with low latency and saves memory usage. 3, when ﬁle path information is cached and ﬁle size is more than 5MB on average, the performance will scale up to 112 clients. 6. Summary Gfarm uses the access locality to increase the total I/O throughput for dataintensive applications, which is a primary goal of Gfarm.
NET-Based Enterprise Grid Computing System. 6th International Conference on Internet Computing, 2005, Las Vegas, USA. 2. Arvind and R. Nikhil. Executing a program on the MIT tagged-token dataflow architecture. IEEE Transaction Computers. 39, 3, 300–318, 1990. 3. David P. Anderson. BOINC: A System for Public-Resource Computing and Storage. Proceedings of the Fifth IEEE/ACM International Workshop on Grid Computing, R. ), IEEE CS Press, USA, 2004. 4. D. Tannenbaum, et al. Distributed computing in practice: The Condor experience.