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To cope with bursty arrivals of high-volume data, a DSMS has to shed load while minimizing the degradation of Quality of Service (QoS). In this paper, the authors show that this problem can be formalized as a classical optimization task from operations research, in ways that accommodate different requirements for multiple users, different query sensitivities to load shedding, and different penalty functions. Standard nonlinear programming algorithms are adequate for non-critical situations, but for severe overloads, they propose a more efficient algorithm that runs in linear time, without compromising optimality.
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