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With the growth of big data problems, nowadays the size of cloud-scale computing clusters is growing rapidly to run complicated parallel processing jobs . To full utilize cluster resources, the cluster management system is being challenged by the scaling cloud size and the often more complicated application requirements. Omega scheduling software p rovides a flexible and scalable shared-state scheduling architecture for large scale cluster scheduling. One of its key idea s is using an optimistic concurrency control (OCC) algorithm to let parallel schedulers concurrently make decisions. However, there are few studies exploring to extend OCC for a shared-state scheduling architecture. Furthermore, most of the traditional’ s hared-state scheduling architectures also use the same OCCs as Omega does. In this paper, we present a multi attribute Dempster–Shafer (D-S) e vidence theory based OCC for shared-state scheduling. This OCC ad a pts the multi attribute D-S evidence theory to help making conflict decisions for some scheduling transactions. Experiment s’ results show that our method can obtain in some respects more optimized scheduling results compared to coarse-grained conflict detection of Omega.

Multi Attribute D-S Evidence Theory Based OCC for Shared-State Scheduling in Large Scale Cluster