By Zoran Constantinescu
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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. Li, C. & Li, L. (2007). Utility-based qos optimisation strategy for multi-criteria scheduling on the grid, J. Parallel Distrib. Comput. 67(2): 142–153. , Zhao, D. & Li, J. (2007). Scheduling algorithm based on integrated utility of multiple qos attributes on service grid, GCC ’07: Proceedings of the Sixth International Conference on Grid and Cooperative Computing, IEEE Computer Society, Washington, DC, USA, pp.
The three methods for matching are proposed by using summation in (4). 1 Optimum Assigning m resources to n tasks is the NP complete problem. The Optimum method has time complexity O(n3) to find the best matched resource for each task globally. Practically the Optimum method cannot be used because it has high overhead. We take into consideration the Optimum solution just for the sake of the comparison. The problem is assigning m resources to n tasks with k QoS parameters. There are three matrices, one is Tn*k matrix given by (5) for task requirements, another is Wn*k matrix given by (6) for weight of requirements, and the other is Rk*m matrix given by (7) for resource capabilities.
Optimum 2. Grid-JQA 3. Dup Grid-JQA That we can consider: • n: the task number • m: the resource number • k: the number of QoS parameters As it was explained, the resources should introduce themselves to the broker. Each introduction includes resource specifications as CPU, Memory, etc. 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.
Advances in Grid Computing by Zoran Constantinescu