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

Abstract

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 the number 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 sharing-incentive, and Pareto-efficiency properties are improved under MRFS.

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“…This is another high-efficiency model that supports SLA-based task scheduling. Approaches that are comparable to these models are described by authors Li et al (2015), Hamzeh et al (2020), andJun et al (2016). These methods make use of Big Data processing by using Hadoop, various resources to ensure fair scheduling, and energyaware job scheduling by employing deadline limitations.…”