Grid Resource Negotiation: Survey with a Machine Learning Perspective
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Grid computing can be defined as coordinated re- source sharing and problem solving in dynamic, multi- institutional collaborations (1). As more Grids are de- ployed worldwide, the number of multi-institutional col- laborations is rapidly growing. However, for Grid comput- ing to realize its full potential, it is expected that Grid par- ticipants are able to use one another resources. Resource negotiation (i.e. exchange or trading of resources between Grids) enables Grid participants to face an unstable request environment. The aim of this position paper is to present a survey of the current state and challenges of resource negotiation research, with a Machine Learning perspective. We sup- port the view that negotiation and learning are intrinsically linked. In particular, we show the expected benefits of inte- grating Machine Learning techniques with resource nego- tiation.