A Rigorous Evaluation of State-of-the-Art Scheduling Algorithms for Cloud Computing
Cloud computing has recently been evolved in terms of the dynamic provision of computing resources to the users based on payment for usage on a pay-as-you-go basis. This provides feasibility to gain access to the large-scale and high-speed resources without establishing their own computing infrastru...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2018-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8556472/ |
| _version_ | 1818415320627412992 |
|---|---|
| author | Altaf Hussain Muhamamd Aleem Muhammad Arshad Islam Muhammad Azhar Iqbal |
| author_facet | Altaf Hussain Muhamamd Aleem Muhammad Arshad Islam Muhammad Azhar Iqbal |
| author_sort | Altaf Hussain |
| collection | DOAJ |
| description | Cloud computing has recently been evolved in terms of the dynamic provision of computing resources to the users based on payment for usage on a pay-as-you-go basis. This provides feasibility to gain access to the large-scale and high-speed resources without establishing their own computing infrastructure to execute high-performance computing (HPC) applications. However, for the past several years, the efficient utilization of resources on a compute cloud has become a prime interest of the scientific community. One of the major causes behind inefficient resource utilization is the imbalance distribution of workload in a distributed computing. This paper contemplates the scheduling objectives of contemporary state-of-the-art heuristics to investigate their behavior to map HPC jobs to resources. Furthermore, the status of workload distribution in cloud computing is also critically assessed. A set of nine scheduling heuristics is validated in the CloudSim simulation environment. The potential of all the heuristics in terms of resource utilization is assessed by combining the workload balancing and machine-level load imbalance using different instances of benchmark scientific datasets (i.e., Heterogeneous Computing Scheduling Problems instances and Google Cloud Jobs dataset). The empirical assessment shows that it is not only an optimal solution to schedule the independent jobs on machines solely based on the execution time, throughput, and average resource utilization ratio; instead, the machine-level load balancing must also be considered to effectuate the usage of full capacity of computing power in a cloud system. Among all the heuristics, Resource-aware load balancing algorithm (RALBA) heuristic has outperformed, and it seems an optimal choice in terms of the tradeoff between complexity and the performance in terms of resource utilization and machine-level load balancing. |
| first_indexed | 2024-12-14T11:33:07Z |
| format | Article |
| id | doaj.art-37d0ea1fec584cb9b8cff5a4cb8b44c8 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T11:33:07Z |
| publishDate | 2018-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-37d0ea1fec584cb9b8cff5a4cb8b44c82022-12-21T23:03:11ZengIEEEIEEE Access2169-35362018-01-016750337504710.1109/ACCESS.2018.28844808556472A Rigorous Evaluation of State-of-the-Art Scheduling Algorithms for Cloud ComputingAltaf Hussain0https://orcid.org/0000-0002-0558-1380Muhamamd Aleem1Muhammad Arshad Islam2Muhammad Azhar Iqbal3Department of Computer Science, Faculty of Computing, Capital University of Science and Technology, Islamabad, PakistanDepartment of Computer Science, Faculty of Computing, Capital University of Science and Technology, Islamabad, PakistanDepartment of Computer Science, Faculty of Computing, Capital University of Science and Technology, Islamabad, PakistanDepartment of Computer Science, Faculty of Computing, Capital University of Science and Technology, Islamabad, PakistanCloud computing has recently been evolved in terms of the dynamic provision of computing resources to the users based on payment for usage on a pay-as-you-go basis. This provides feasibility to gain access to the large-scale and high-speed resources without establishing their own computing infrastructure to execute high-performance computing (HPC) applications. However, for the past several years, the efficient utilization of resources on a compute cloud has become a prime interest of the scientific community. One of the major causes behind inefficient resource utilization is the imbalance distribution of workload in a distributed computing. This paper contemplates the scheduling objectives of contemporary state-of-the-art heuristics to investigate their behavior to map HPC jobs to resources. Furthermore, the status of workload distribution in cloud computing is also critically assessed. A set of nine scheduling heuristics is validated in the CloudSim simulation environment. The potential of all the heuristics in terms of resource utilization is assessed by combining the workload balancing and machine-level load imbalance using different instances of benchmark scientific datasets (i.e., Heterogeneous Computing Scheduling Problems instances and Google Cloud Jobs dataset). The empirical assessment shows that it is not only an optimal solution to schedule the independent jobs on machines solely based on the execution time, throughput, and average resource utilization ratio; instead, the machine-level load balancing must also be considered to effectuate the usage of full capacity of computing power in a cloud system. Among all the heuristics, Resource-aware load balancing algorithm (RALBA) heuristic has outperformed, and it seems an optimal choice in terms of the tradeoff between complexity and the performance in terms of resource utilization and machine-level load balancing.https://ieeexplore.ieee.org/document/8556472/Scheduling algorithmhigh-performance computingmachine-level load balancingload-balanced scheduling |
| spellingShingle | Altaf Hussain Muhamamd Aleem Muhammad Arshad Islam Muhammad Azhar Iqbal A Rigorous Evaluation of State-of-the-Art Scheduling Algorithms for Cloud Computing IEEE Access Scheduling algorithm high-performance computing machine-level load balancing load-balanced scheduling |
| title | A Rigorous Evaluation of State-of-the-Art Scheduling Algorithms for Cloud Computing |
| title_full | A Rigorous Evaluation of State-of-the-Art Scheduling Algorithms for Cloud Computing |
| title_fullStr | A Rigorous Evaluation of State-of-the-Art Scheduling Algorithms for Cloud Computing |
| title_full_unstemmed | A Rigorous Evaluation of State-of-the-Art Scheduling Algorithms for Cloud Computing |
| title_short | A Rigorous Evaluation of State-of-the-Art Scheduling Algorithms for Cloud Computing |
| title_sort | rigorous evaluation of state of the art scheduling algorithms for cloud computing |
| topic | Scheduling algorithm high-performance computing machine-level load balancing load-balanced scheduling |
| url | https://ieeexplore.ieee.org/document/8556472/ |
| work_keys_str_mv | AT altafhussain arigorousevaluationofstateoftheartschedulingalgorithmsforcloudcomputing AT muhamamdaleem arigorousevaluationofstateoftheartschedulingalgorithmsforcloudcomputing AT muhammadarshadislam arigorousevaluationofstateoftheartschedulingalgorithmsforcloudcomputing AT muhammadazhariqbal arigorousevaluationofstateoftheartschedulingalgorithmsforcloudcomputing AT altafhussain rigorousevaluationofstateoftheartschedulingalgorithmsforcloudcomputing AT muhamamdaleem rigorousevaluationofstateoftheartschedulingalgorithmsforcloudcomputing AT muhammadarshadislam rigorousevaluationofstateoftheartschedulingalgorithmsforcloudcomputing AT muhammadazhariqbal rigorousevaluationofstateoftheartschedulingalgorithmsforcloudcomputing |