Back to Search
Start Over
Efficient Dependent Task Offloading for Multiple Applications in MEC-Cloud System
- Source :
- IEEE Transactions on Mobile Computing. 22:2147-2162
- Publication Year :
- 2023
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2023.
-
Abstract
- With the proliferation of versatile mobile applications, offloading compute-intensive tasks to the MEC/Cloud becomes a dramatic technique due to the limited resources and high user experience requirements at mobile devices. However, most existing works design their task offloading schemes without considering the dependence of tasks and the orchestration of the MEC and Cloud, and thus may limit the system performance. In this paper, we propose a dependent task offloading framework for multiple mobile applications, named COFE, where mobile devices can offload their compute-intensive tasks with dependent constraints to the MEC-Cloud system. It can assign the offloaded tasks to the MEC and Cloud adaptively to improve the user experience. Based on COFE, we formulate the task offloading problem as an average makespan minimization problem, which is proved to be NP-hard. Then, we propose a heuristic ranking-based algorithm to assign the offloaded tasks according to their bottom levels. Theoretical analysis proves the stability of the system under the proposed algorithm and extensive simulations validate that the proposed algorithm can significantly reduce the average makespan and deadline violation probabilities of offloaded applications.
- Subjects :
- Job shop scheduling
Computer Networks and Communications
Computer science
Heuristic (computer science)
business.industry
Distributed computing
Stability (learning theory)
Cloud computing
Task (project management)
User experience design
Orchestration (computing)
Electrical and Electronic Engineering
business
Mobile device
Software
Subjects
Details
- ISSN :
- 21619875 and 15361233
- Volume :
- 22
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Mobile Computing
- Accession number :
- edsair.doi...........6f9325768dff291f5c902109b34fabd9
- Full Text :
- https://doi.org/10.1109/tmc.2021.3119200