Advances in Grid Computing by Zoran Constantinescu

By Zoran Constantinescu

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The effect of computational tasks weight vector on execution success ratio Fig. 6. The effect of data tasks weight vector on execution success ratio success ratio is the metric used in the experiments to measure the framework’s ability to host and schedule variant task triplets. Other metrics such as resource utilization and task waiting time are important as well, but due to space limitation, results in terms of execution success ratio only are illustrated to evidate that a system can function whilst benefiting from subjective QoS attributes.

K Ri ₪ Tj = (( ∑ l =1 qlRe si task j ql × wl ) ≥ 1) (k = the number of QoS parameters) (4) The solution proposed here is that, we normalize the resource specification by the client requirement (qires / qitask) and therefore the summation will be possible whereas the units of Grid-JQA: A QoS Guided Scheduling Algorithm for Grid Computing 35 each parameter are different such as byte, bps, Mflops. When the resource capability exceeds the task demand, (qires / qitask) is more than one. Also, the client introduces a weight for each parameter to show the importance of the parameter.

Using schedulers, such as (Xen Scheduler Howto, 2005), a resource can assign a percentage of CPU to a given machine effectively regulating the CPU usage of the group of processes encapsulated in it. And also it includes some parameters such as the bandwidth and the delay were calculated or estimated by the broker. Finally the broker inserts the record of QoS parameters for each resource into the database. We define the vector qres which gives the capabilities of a resource as follows. q res = q1res , q2res ,… , q kres (1) Generally it can be said that the client sends its request accompanied by vector of QoS parameters and the weights for the parameters as it appears in equations 2 and 3.

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Advances in Grid Computing by Zoran Constantinescu
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