MRFS: A Multi-resource Fair Scheduling Algorithm in Heterogeneous Cloud Computing
Authors: Hamzeh, H., Meacham, S., Khan, K., Phalp, K. and Stefanidis, A.
Journal: Proceedings - 2020 IEEE 44th Annual Computers, Software, and Applications Conference, COMPSAC 2020
Pages: 1653-1660
ISBN: 9781728173030
DOI: 10.1109/COMPSAC48688.2020.00-18
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 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.
https://eprints.bournemouth.ac.uk/34367/
Source: Scopus
MRFS: A Multi-Resource Fair Scheduling Algorithm in Heterogeneous Cloud Computing
Authors: Hamzeh, H., Meacham, S., Khan, K., Phalp, K. and Stefanidis, A.
Journal: 2020 IEEE 44TH ANNUAL COMPUTERS, SOFTWARE, AND APPLICATIONS CONFERENCE (COMPSAC 2020)
Pages: 1653-1660
ISSN: 0730-3157
DOI: 10.1109/COMPSAC48688.2020.00-18
https://eprints.bournemouth.ac.uk/34367/
Source: Web of Science (Lite)
MRFS: A Multi-Resource Fair Scheduling Algorithm in Heterogeneous Cloud Computing
Authors: Hamzeh, H., Meacham, S., Khan, K., Phalp, K. and Stefanidis, A.
Conference: 2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC)
Dates: 14-17 July 2020
Journal: IEEE
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 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
https://eprints.bournemouth.ac.uk/34367/
Source: Manual
MRFS: A Multi-resource Fair Scheduling Algorithm in Heterogeneous Cloud Computing.
Authors: Hamzeh, H., Meacham, S., Khan, K., Phalp, K. and Stefanidis, A.
Journal: COMPSAC
Pages: 1653-1660
Publisher: IEEE
ISBN: 978-1-7281-7303-0
https://eprints.bournemouth.ac.uk/34367/
https://ieeexplore.ieee.org/xpl/conhome/9193853/proceeding
Source: DBLP
MRFS: A Multi-Resource Fair Scheduling Algorithm in Heterogeneous Cloud Computing
Authors: Hamzeh, H., Meacham, S., Khan, K., Phalp, K.T. and Stefanidis, A.
Conference: COMPSAC: The IEEE 44th Annual Computers, Software, and Applications Conference
Pages: 1653-1660
ISBN: 978-1-7281-7303-0
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 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
https://eprints.bournemouth.ac.uk/34367/
https://ieeecompsac.computer.org/2020/
Source: BURO EPrints