MRFS: A Multi-resource Fair Scheduling Algorithm in Heterogeneous Cloud Computing

• Published in: 2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC) pp. 1653 - 1660
• Main Authors: Hamzeh, Hamed, Meacham, Sofia, Khan, Kashaf, Phalp, Keith, Stefanidis, Angelos
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-07-2020
• Subjects: Allocation; Cloud; Cloud computing; Dominant; fairness; Lagrangian; Processor scheduling; resource; Resource management; Schedules; Scheduling; server; Servers; task; Task analysis; utility
• DOI: 10.1109/COMPSAC48688.2020.00-18

Task scheduling in cloud computing is considered as a significant issue that has attracted much attention over the last decade. In cloud environments, users expose considerable interest in submitting tasks on multiple Resource types. Subsequently, finding an optimal and most efficient server to host users' tasks seems a fundamental concern. Several attempts have suggested various algorithms, employing Swarm optimization and heuristics methods to solve the scheduling issues associated with cloud in a multi-resource perspective. However, these approaches have not considered the equalization of dominant resources on each specific resource type. This substantial gap leads to unfair allocation, SLA degradation and resource contention. To deal with this problem, in this paper we propose a novel task scheduling mechanism called MRFS. MRFS employs Lagrangian multipliers to locate tasks in suitable servers with respect to the number of dominant resources and maximum resource availability. To evaluate MRFS, we conduct time-series experiments in the cloudsim driven by randomly generated workloads. The results show that MRFS maximizes per-user utility function by %15-20 in FFMRA compared to FFMRA in absence of MRFS. Furthermore, the mathematical proofs confirm that the sharingincentive, and Pareto-efficiency properties are improved under MRFS.