A Universal Fairness Evaluation Framework for Resource Allocation in Cloud Computing

Abstract

Abstract: Machine learning is nowadays ubiquitous, providing mechanisms for supporting decision making that leverages big data analytics. However, this recent rise in importance of machine learning also raises societal concerns about the dependability and trustworthiness of systems which depend on such automated predictions. In cloud computing, fairness is one of the most significant indicators to evaluate resource allocation algorithms, which reveals whether each user is allocated as much as that of all other users having the same bottleneck. However, how fair an allocation algorithm is remains an urgent issue. In this paper, we propose Dynamic Evaluation Framework for Fairness (DEFF), a framework to evaluate the fairness of an resource allocation algorithm. In our framework, two sub-models, Dynamic Demand Model (DDM) and Dynamic Node Model (DNM), are proposed to describe the dynamic characteristics of resource demand and the computing node number under cloud computing environment. Combining Fairness on Dominant Shares and the two sub-models above, we finally obtain DEFF. In our experiment, we adopt several typical resource allocation algorithms to prove the effectiveness on fairness evaluation by using the DEFF framework.