A Knee Point-Driven Set-Based Swarm Optimizer for Computing Tasks Allocation Oriented to Marginal Utility in Fog Computing

초록

A significant challenge in fog computing is task allocation, in which computing tasks are assigned to distributed nodes for processing. This problem is NP-hard and involves multiple conflicting objectives. Although some studies have considered these objectives jointly, effectively balancing them while honoring user preferences remains difficult. In this paper, we consider the effect of marginal utility on achieving multi objective balance under user preferences, and formulate a multi objective task allocation problem for fog computing. We introduce a knee-driven set-based level-based learning optimizer (KSLLSO) to find preferred solutions. The knee-point driven strategy identifies regions with high marginal utility, while the set-based operators and level-based learning strategy efficiently search the discrete decision space. Moreover, a constructive solution generation approach and a capacity-guided allocation strategy allow KSLLSO to exploit problem structure and improve optimization performance. Comprehensive experiments comparing KSLLSO with classical and recent methods demonstrate the effectiveness of the proposed approach. © 2008-2012 IEEE.

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