QoE Aware and Cell Capacity Enhanced Computation Offloading for Multi-Server Mobile Edge Computing Systems with Energy Harvesting Devices

Abstract

The increasing complexity of intelligent services requires new paradigm to overcome the problems caused by resource-limited mobile devices. Mobile edge computing systems with energy harvesting devices is such a promising technology. By offloading the computation tasks from the mobile devices to the MEC servers, users could experience services with low latency. In addition, energy harvesting technology releases the tension between high energy consumption of intelligent services and capacity-constrained mobile device batteries. However, in multi-user and multi-server scenarios where mobile devices can move arbitrarily, computation offloading strategies are faced with new challenges because of resource competition and server selection. In this paper, we will develop an intelligent computation offloading strategy. The quality of user experience cost and the cell capacity in terms of the ratio of computation tasks offloaded will be adopted as the performance metrics. An online algorithm, namely, the LODCO-Based Genetic Algorithm with Greedy Policy, will be proposed. Specifically, the algorithm is based on Lyapunov Optimization and the LODCO Algorithm. By choosing the execution mode among local execution, offloading execution and task dropping for each mobile device, our algorithm can asymptotically obtain the optimal results for the whole system. The algorithm proposed is low-complexity and could work without too much priori knowledge. Moreover, the algorithm not only inherits every advantage from the LODCO Algorithm but also adapts perfectly to the more complex environment. Simulation results illustrate that the algorithms could improve the ratio of offloading computation tasks by more than 10% while the QoE is guaranteed.

Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence

“…Computation offloading studies the load balancing of various computational and communication resources in the manner of edge server selection and frequency spectrum allocation. More and more research efforts focus on dynamically managing the radio and computational resources for multi-user multi-server edge computing systems, utilizing Lyapunov optimization technologies [18] [19]. In recent years, optimizing computation offloading decisions via DQN is popular [20] [21].…”

QoE‐aware mobile computation offloading in mobile edge computing

“…Zhao et al, 25 have presented a QoE aware and cell capacity upgraded computation offloading for multiple server mobile edge computing systems along energy harvesting devices. An intelligent computation offloading model was developed.…”

“…From related work [19][20][21][22][23][24][25][26] shows that high latency, unstable QoS with maximum cost. To overcome these issues, in this manuscript presents QoE-aware mobile computation offloading in mobile edge computing.…”

Offloading Cost Optimization in Multiserver Mobile Edge Computing Systems with Energy Harvesting Devices

“…There have been many works [9][10][11][12][13][14][15][19][20][21] focus on developing offloading strategies to reduce the energy consumption and computation latency. Chen et al [10] devel-oped an offloading strategy based on a self-adaptive particle swarm optimization algorithm to reduce the system energy consumption for DNN-based smart IoT systems.…”

“…Many offloading techniques [9][10][11][12][13][14][15] have been proposed to minimize task execution latency and energy consumption or achieve the trade-off between them [16,17]. However, in terms of energy management, most of those works focus on saving the energy cost of edge computing systems which may cause excessive computation offloading.…”