Coded distributed computing(CDC) has shown great potentials to solve the unexpected delay caused by stragglers in distributed computing. In this paper, we focus on the auction design for efficient resource allocation in CDC. Specifically, we aim to design a learning auction mechanism to handle heterogeneous user demands and also to free users from the complexity of specifying valuations for resource combinations, which increases exponentially with the resource dimensions. The user demand type is heterogeneous according to different variation trends of the value with finish time and workload, which is modeled by deep learning. The platform would allocate resources according to the user value function. Then users do not need to consider the complex relationship between uncertain finish time and resource configuration in CDC. Due to the inference error of the learning model and the complexity of calculating uncertain finish time, the considered social welfare optimization problem is a non-linear and non-convex integer problem. Even worse, the typical VCG-based payment scheme cannot guarantee truthfulness with the inference error. In response to these difficulties, we transform the social welfare optimization problem into a mixed integer programming problem which already has efficient solutions. The social welfare gap caused by the inference error is analyzed theoretically. The relationship between the utility regret of reporting truthfully and the inference error is also analyzed. We prove that our mechanism satisfies incentive alignment and individual rationality. Extensive experiments show the superiority of our mechanism compared with existing ones.

中文翻译：

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具有预测异构用户需求的编码分布式计算：一种学习拍卖方法

编码分布式计算（CDC）在解决分布式计算中落后者造成的意外延迟方面显示出巨大的潜力。在本文中，我们专注于 CDC 中有效资源分配的拍卖设计。具体来说，我们的目标是设计一种学习拍卖机制来处理异构用户需求，并使用户摆脱为资源组合指定估值的复杂性，这种复杂性随着资源维度呈指数增长。用户需求类型是异构的，根据价值随完成时间和工作量的不同变化趋势，这是通过深度学习建模的。平台将根据用户价值函数分配资源。这样用户就不需要考虑CDC中不确定完成时间和资源配置之间的复杂关系。由于学习模型的推理误差和计算不确定完成时间的复杂性，所考虑的社会福利优化问题是一个非线性非凸整数问题。更糟糕的是，典型的基于 VCG 的支付方案无法保证推理错误的真实性。针对这些困难，我们将社会福利优化问题转化为已经有有效解的混合整数规划问题。从理论上分析了推理错误导致的社会福利差距。还分析了如实报告的效用遗憾与推理错误之间的关系。我们证明了我们的机制满足激励对齐和个体理性。大量实验表明我们的机制与现有机制相比具有优越性。