| 1. |
- Abdi, Somayeh, et al.
(författare)
-
Task Offloading in Edge-cloud Computing using a Q-Learning Algorithm
- 2024
-
Ingår i: International Conference on Cloud Computing and Services Science, CLOSER - Proceedings. - : Science and Technology Publications, Lda. - 9789897587016 ; , s. 159-166
-
Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Task offloading is a prominent problem in edge−cloud computing, as it aims to utilize the limited capacityof fog servers and cloud resources to satisfy the QoS requirements of tasks, such as meeting their deadlines.This paper formulates the task offloading problem as a nonlinear mathematical programming model to maximizethe number of independent IoT tasks that meet their deadlines and to minimize the deadline violationtime of tasks that cannot meet their deadlines. This paper proposes two Q-learning algorithms to solve theformulated problem. The performance of the proposed algorithms is experimentally evaluated with respect toseveral algorithms. The evaluation results demonstrate that the proposed Q-learning algorithms perform wellin meeting task deadlines and reducing the total deadline violation time.
|
|
| 2. |
- Nguyen, Chanh Le Tan, et al.
(författare)
-
State-aware application placement in mobile edge clouds
- 2024
-
Ingår i: Proceedings of the 14th international conference on cloud computing and services science. - Portugal : Science and Technology Publications. - 9789897587016 ; , s. 117-128
-
Konferensbidrag (refereegranskat)abstract
- Placing applications within Mobile Edge Clouds (MEC) poses challenges due to dynamic user mobility. Maintaining optimal Quality of Service may require frequent application migration in response to changing user locations, potentially leading to bandwidth wastage. This paper addresses application placement challenges in MEC environments by developing a comprehensive model covering workloads, applications, and MEC infrastructures. Following this, various costs associated with application operation, including resource utilization, migration overhead, and potential service quality degradation, are systematically formulated. An online application placement algorithm, App EDC Match, inspired by the Gale-Shapley matching algorithm, is introduced to optimize application placement considering these cost factors. Through experiments that employ real mobility traces to simulate workload dynamics, the results demonstrate that the proposed algorithm efficiently determines near-optimal application placements within Edge Data Centers. It achieves total operating costs within a narrow margin of 8% higher than the approximate global optimum attained by the offline precognition algorithm, which assumes access to future user locations. Additionally, the proposed placement algorithm effectively mitigates resource scarcity in MEC.
|
|
