Abstract
Task scheduling is a non-deterministic polynomial-time hard (NP-hard) optimisation problem, thus applying metaheuristics is important. In this paper, we employ glowworm swarm optimisation (GSO) to solve the task scheduling problem in cloud computing to minimise the total execution cost of tasks while keeping the total completion time within the deadline. Simulation results show that GSO based task scheduling (GSOTS) algorithm outperforms shortest task first (STF), largest task first (LTF) and particle swarm optimisation (PSO) algorithms in reducing the total completion time and the cost of executing tasks.
Original language | English |
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Title of host publication | Proceedings of the 2nd International Conference on Internet of Things and Cloud Computing, ICC 2017 |
Publisher | Association for Computing Machinery (ACM) |
Number of pages | 7 |
ISBN (Print) | 9781450347747 |
DOIs | |
Publication status | Published - 22 Mar 2017 |
Event | Second International Conference on Internet of Things, Data and Cloud Computing - Churchill College, University of Cambridge, Cambridge, United Kingdom Duration: 22 Mar 2017 → 23 Mar 2017 |
Conference
Conference | Second International Conference on Internet of Things, Data and Cloud Computing |
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Abbreviated title | ICC 2017 |
Country/Territory | United Kingdom |
City | Cambridge |
Period | 22/03/17 → 23/03/17 |
Keywords
- cloud computing
- resource management
- glowworm swarm optimisation (GSO)
- task scheduling