5,605 research outputs found
Dominant Resource Fairness in Cloud Computing Systems with Heterogeneous Servers
We study the multi-resource allocation problem in cloud computing systems
where the resource pool is constructed from a large number of heterogeneous
servers, representing different points in the configuration space of resources
such as processing, memory, and storage. We design a multi-resource allocation
mechanism, called DRFH, that generalizes the notion of Dominant Resource
Fairness (DRF) from a single server to multiple heterogeneous servers. DRFH
provides a number of highly desirable properties. With DRFH, no user prefers
the allocation of another user; no one can improve its allocation without
decreasing that of the others; and more importantly, no user has an incentive
to lie about its resource demand. As a direct application, we design a simple
heuristic that implements DRFH in real-world systems. Large-scale simulations
driven by Google cluster traces show that DRFH significantly outperforms the
traditional slot-based scheduler, leading to much higher resource utilization
with substantially shorter job completion times
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