Fairness constraint efficiency optimization for multiresource allocation in a cluster system serving internet of things

• Published in: International journal of communication systems 2023-02, Vol.36 (3), p.n/a
• Main Authors: Chen, Shaomiao, Yang, Ce, Huang, Weihong, Liang, Wei, Ke, Nai, Souri, Alireza, Li, Kuan‐Ching
• Format: Article
• Language: English
• Subjects: Algorithms; cluster system; Clusters; Constraints; Efficiency; efficiency‐aware; fairness; Internet of Things; IoT; multiresource; Optimization; Resource allocation; Resource utilization
• ISSN: 1074-5351; 1099-1131
• DOI: 10.1002/dac.5395

Summary Massive, diverse, and high‐frequency Internet of Things (IoT) applications pose challenges to the operation of cluster systems that serve it. Fair and efficient multidimensional resource allocation is of great significance to the sustainable operation of these systems. However, most of the existing cluster multiresource allocation optimization researches focus too much on the fairness of resource allocation and ignore the efficiency. The unbalanced use of multidimensional system resources reduces the effective utilization of system resources, which seriously affects the service quality of IoT applications. In this paper, we define the multiresource fair and efficient sharing optimization as a fairness‐constrained efficiency optimization problem, which is from dynamics, discrete resources, and heterogeneous perspectives according to the characteristics of cluster system in practical. Moreover, we present a dynamic efficiency‐aware multiresource fair allocation algorithm, DEF, which can improve the ability of the cluster system to serve diverse IoT applications. In the algorithm, large jobs schedule to the servers that expect the least remaining resources. Simulations performed using Google cluster‐usage traces show that DEF can improve system resource utilization and guarantee the fairness of sharing among users. It is difficult to balance fairness and resource allocation efficiency when diverse IoT application tasks are allocated to discrete computing resources. We define this problem as a fairness‐constrained efficiency optimization problem from the perspective of dynamics, discrete resources, and heterogeneity. And a dynamic efficiency‐aware multiresource fair allocation algorithm is proposed. It can schedule task to the server with the least expected remaining resources while satisfying the fairness of allocation.