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Desktop Grids have become very popular nowadays, with projects that include hundred of thousands computers. Desktop grid scheduling faces two challenges. First, the platform is volatile, since users may reclaim their computer at any time, which makes centralized schedulers inappropriate. Second, desktop grids are likely to be shared among several users, thus we must be particularly careful to ensure a fair sharing of the resources.In this paper, we propose a decentralized scheduler for bag-of-tasks applications on desktop grids, which ensures a fair and efficient use of the resources. It aims to provide a similar share of the platform to every application by minimizing their maximum stretch, using completely decentralized algorithms and protocols. After presenting our algorithms, we evaluate them through extensive simulations. We compare our solution to already existing centralized ones under similar conditions, and show that its performance is close to the best centralized algorithms.

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Abstract-Scheduling large amounts of tasks in distributed computing platforms composed of millions of nodes is a challenging goal, even more in a fully decentralized way and with low overhead. Thus, we propose a new scalable scheduler for task workflows with deadlines following a completely decentralized architecture. It's built upon a tree-based P2P overlay that supports efficient and fast aggregation of resource availability information. Constraints for deadlines and the correct timing of tasks in workflows are guaranteed with a suitable distributed management of availability time intervals of resources. A local scheduler in each node provides its available time intervals to the distributed global scheduler, which summarizes them in the aggregation process. A two phase reservation protocol looks for suitable resources that comply with workflow structure and deadline. Experimental results, from simulations of a system composed of one million nodes, show scalable fast scheduling with low overhead that can allow a high dynamic usage of computational resources.

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