.png){kind=icon}
返回
Coded Distributed Computing With Predictive Heterogeneous User Demands: A Learning Auction Approach
.png){kind=icon}
PRE
AI
DOI：10.1109/JSAC.2022.3180811.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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.
Keyword:
Cost accounting
Task analysis
Resource management
Computational modeling
Cloud computing
Optimization
Encoding
Coded distributed computing
machine learning
auction and pricing
social welfare maximization
MIP optimization
期刊
IF:
17.2
论文数:
6.4K
被引数:
3.1W
机构
暂无机构信息
引用论文
.png){kind=icon}
Fast and Secure Computational Offloading With Lagrange Coded Mobile Edge Computing基于拉格朗日编码移动边缘计算的快速安全计算卸载
PREAI
分享收藏On Batch-Processing Based Coded Computing for Heterogeneous Distributed Computing Systems异构分布式计算系统中基于批处理的编码计算研究
OAAI
OAAI分享收藏
查看更多内容