Loading...

Joint Optimization of Computation Offloading and Resource Allocation in Mobile Edge Computing Networks

Shokouhi, Mohammad Hossein | 2023

59 Viewed
  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 55868 (05)
  4. University: Sharif University of Technology
  5. Department: Electrical Engineering
  6. Advisor(s): Pakravan, Mohammad Reza; Hadi, Mohammad
  7. Abstract:
  8. Mobile edge computing (MEC) is a promising technology that aims to resolve cloud computing’s issues by deploying computation resources at the edge of mobile network and in the proximity of users. The advantages of MEC include reduced latency, energy consumption, and load on access and mobile core networks, to name but a few. Despite all the aforementioned advantages, the mobility of mobile network users causes the traditional MEC architecture to suffer from several issues, such as decreased efficiency and frequent service interruption. One of the methods to manage users’ mobility is virtual machine (VM) migration, where the VM containing the user’s task is migrated to somewhere closer to him. However, VM migration is extremely costly, requires high bandwidth, and causes undesirable latency. These characteristics render VM migration impractical for real-time tasks with stringent latency requirements. In this research, we propose a novel hierarchical architecture for MEC networks that is capable of facilitating mobility management and mitigating the need for VM migration. In order to utilize this architecture efficiently, a Markov chain-based predictive strategy is proposed that predicts the mobility of users in the future. Afterward, an optimization problem is formulated that minimizes the long-term cost of users for using network resources, such as bandwidth, energy, and computation resources. This objective ensures the optimal usage of network resources. In order to demonstrate the desired functionality of this architecture, we perform several simulations that investigate the changes in the total cost versus several parameters and compare them to traditional methods. The simulation results demonstrate that the proposed architecture improves mobility management and reduces the total cost by up to 25% compared to traditional methods
  9. Keywords:
  10. Resources Allocation ; Mobility Management ; Mobile Edge Computing ; Computation Offloading ; Edge Computing ; Virtual Machine ; Energy Conservation

 Digital Object List

 Bookmark

...see more