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By N. Cindro, P. Kulisic, T. Mayer-Kuckuk

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Extra resources for Proceedings of the Europhysics Study Conference on Intermediate Processes in Nuclear Reactions

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Zheng et al. features. The feature of sensors’ scalable broadcast discovery at cluster level is learned from Ganglia project [8]. Message protocol design is enlightened by Supermon [9]’s kernel mode protocol in the form of s-expression. Stream processing design is inspired by Aurora [12], a data stream project. Finally, MonALISA [10]’s intuitive and impressive visualization work, which has a rich set of visualization forms, has a great impact on CGSV presentation works. Besides, CGSV attempts to integrate data stream in re-publishers and has adaptable design of runtime configured sensors and extensible protocols for both data transfer and control.

Unlike many existing monitor tools, the most significant characteristic of our sensor is runtime configurable, which means that the monitor metadata, such as each metric’s switch, collection frequency and granularity, is able to be changed over runtime on demand. For example, we demand turning off all the resource monitoring metrics except CPU load and also lower down the information collecting frequency to alleviate intrusiveness on some machines with heavy load. For many other monitor tools, configuration is written in files and is load only at startup; therefore the required action needs us to login on that computing node, shutdown the tool, change configuration file and start the tool again.

Od ijxy tdijxy cijxy aijxy eijxy lsijxy xy ij q sijxy Fig. 3. A Refined Sub-Model of the Data Grid Scheduling System It follows from the steady-state probabilities that system performance measures can be obtained. (1) The average throughput for transition t in steady state is: T (t ) = ∑ M ∈H ( t ) Pr [ M ] × θ ( t , M ) , where Pr[M] is the steady-state probability of marking M, θ (t, M ) is the firing rate of transition t in marking M, and H(t) is the subset of reachable markings that enable t.

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