TY - JOUR
T1 - Cost-effective and Low-complexity Non-constrained Workflow Scheduling for Cloud Computing Environment
AU - Kamanga, Célestin Tshimanga
AU - Bugingo, Emmanuel
AU - Badibanga, Simon Ntumba
AU - Mukendi, Eugène Mbuyi
AU - Habimana, Olivier
N1 - Publisher Copyright:
© IJASEIT is licensed under a Creative Commons Attribution-Share Alike 4.0 International License
PY - 2023
Y1 - 2023
N2 - Cloud computing possesses the merit of being a faster and cost-effective platform in terms of executing scientific workflow applications. Scientific workflow applications are found in different domains, such as security, astronomy, science, etc. They are represented by complex sizes, which makes them computationally intensive. The main key to the successful execution of scientific workflow applications lies in task resource mapping. However, task-resource mapping in a cloud environment is classified as NPcomplete. Finding a good schedule that satisfies users' quality of service requirements is still complicated. Even if different studies have been carried out to propose different algorithms that address this issue, there is still a big room for improvement. Some proposed algorithms focused on optimizing different objectives such as makespan, cost, and energy. Some of those studies fail to produce lowtime complexity and low-runtime scientific workflow scheduling algorithms. In this paper, we proposed a non-constrained, low-runtime, and low-time-complexity scientific workflow scheduling algorithm for cost minimization. Since the proposed algorithm is a list scheduling algorithm, its key success is properly selecting computing resources and its operating CPU frequency for each task using the maximum cost difference and minimum cost-execution difference from the mean.
AB - Cloud computing possesses the merit of being a faster and cost-effective platform in terms of executing scientific workflow applications. Scientific workflow applications are found in different domains, such as security, astronomy, science, etc. They are represented by complex sizes, which makes them computationally intensive. The main key to the successful execution of scientific workflow applications lies in task resource mapping. However, task-resource mapping in a cloud environment is classified as NPcomplete. Finding a good schedule that satisfies users' quality of service requirements is still complicated. Even if different studies have been carried out to propose different algorithms that address this issue, there is still a big room for improvement. Some proposed algorithms focused on optimizing different objectives such as makespan, cost, and energy. Some of those studies fail to produce lowtime complexity and low-runtime scientific workflow scheduling algorithms. In this paper, we proposed a non-constrained, low-runtime, and low-time-complexity scientific workflow scheduling algorithm for cost minimization. Since the proposed algorithm is a list scheduling algorithm, its key success is properly selecting computing resources and its operating CPU frequency for each task using the maximum cost difference and minimum cost-execution difference from the mean.
KW - Workflow scheduling
KW - difference from the mean
KW - low complexity
KW - resource management
KW - weighted sum difference
UR - http://www.scopus.com/inward/record.url?scp=85150945464&partnerID=8YFLogxK
U2 - 10.18517/ijaseit.13.1.17752
DO - 10.18517/ijaseit.13.1.17752
M3 - 文章
AN - SCOPUS:85150945464
SN - 2088-5334
VL - 13
SP - 371
EP - 379
JO - International Journal on Advanced Science, Engineering and Information Technology
JF - International Journal on Advanced Science, Engineering and Information Technology
IS - 1
ER -