By Zoran Constantinescu
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Extra resources for Advances in Grid Computing
2007) to iteratively update the selection probabilities of the selected resources. For each task Ti a decision matrix Q = q ij m×d is created, among it, m represents the number of resources that can host Ti , d represents the dimension of QoS attributes considered by the this type of task. e. ). x and y denote the start and end of the QoS attributes subset from the set of all QoS attributes deﬁned. By sorting and computing Ui (ω ), the best resource can be selected. Weights for utility functions in the mentioned related work are calculated by maximizing the deviations in utiliy values.
Dogan, A. & özgüner, F. (2006). Scheduling of a meta-task with qos requirements in heterogeneous computing systems, J. Parallel Distrib. Comput. 66(2): 181–196. Foster, I. (2006). Globus toolkit version 4: Software for service-oriented systems, pp. 2–13. Jong, K. A. D. (1992). , PPSN, pp. 3–14. , Hensgen, D. , Siegel, H. , John, D. , Porter, N. , Prasanna, V. K. & Freund, R. F. (2006). A ﬂexible multi-dimensional qos performance measure framework for distributed heterogeneous systems, Cluster Computing 9(3): 281–296.
The x-axis in all three ﬁgures is a conﬁguration state for the QoS attributes considered. e. 5 are considered. e. C1 to C5) the weights are having values with an increasing mean from C1 to C5 and also an increasing standard deviation to represent variant weight vector settings. Note that upon moving from C1 to C5 the success rate tends to decrease which is normal as the more the mean of the weight vector increases the more the scheduling process relay on QoS other than the time and cost. This consequently minimizes the set of resource candidates for each task and causes less success rate.
Advances in Grid Computing by Zoran Constantinescu