Dynamic Task Offloading for Mobile Edge Computing with Hybrid Energy Supply

Wireless Powered Mobile Edge Computing (WPMEC) is an integration of Mobile Edge Computing (MEC) and Wireless Power Transfer (WPT) technologies, to both improve computing capabilities of mobile devices and energy compensation for their limited battery capabilities. Generally, energy transmitters, mobile devices, and edge servers form a WPMEC system that realizes a closed loop of sending and collecting energy as well as offloading and receiving task data. Due to constraints of time-varying network environments, time-coupled battery levels, and the half-duplex character of mobile devices, the joint design of computation offloading and resource allocation solutions in WPMEC systems has become extremely challenging, and a great number of studies have been devoted to it in recent years. In this article, we first introduce the basic model of the WPMEC system. Then, we present key issues and techniques related to WPMEC. In addition, we summarize solutions for computation offloading and resource allocation to solve critical issues in WPMEC networks. Finally, we discuss some research challenges and open issues.

https://dl.acm.org/doi/pdf/10.1145/3579992

Wireless Powered Mobile Edge Computing Networks: A Survey

Considering the user mobility and unpredictable mobile edge computing (MEC) environments, this paper studies the intelligent task offloading problem in unmanned aerial vehicle (UAV)-enabled MEC with the assistance of digital twin (DT). We aim at minimizing the energy consumption of the entire MEC system by jointly optimizing mobile terminal users (MTUs) association, UAV trajectory, transmission power distribution and computation capacity allocation while respecting the constraints of mission maximum processing delays. Specifically, double deep Q-network (DDQN) algorithm stemming from deep reinforcement learning is first proposed to effectively solve the problem of MTUs association and UAV trajectory. Then, the closed-form expression is employed to handle the problem of transmission power distribution and the computation capacity allocation problem is further addressed via an iterative algorithm. Numerical results show that our proposed scheme is able to converge and significantly reduce the total energy consumption of the MEC system compared to the benchmark schemes. 

/pdf/digital-twin-assisted-task-offloading-for-aerial-edge-23iscg4o.pdf

Digital Twin Assisted Task Offloading for Aerial Edge Computing and Networks

Considering the user mobility and unpredictable mobile edge computing (MEC) environments, this paper studies the intelligent task offloading problem in unmanned aerial vehicle (UAV)-enabled MEC with the assistance of digital twin (DT). We aim at minimizing the energy consumption of the entire MEC system by jointly optimizing mobile terminal users (MTUs) association, UAV trajectory, transmission power distribution and computation capacity allocation while respecting the constraints of mission maximum processing delays. Specifically, double deep Q-network (DDQN) algorithm stemming from deep reinforcement learning is first proposed to effectively solve the problem of MTUs association and UAV trajectory. Then, the closed-form expression is employed to handle the problem of transmission power distribution and the computation capacity allocation problem is further addressed via an iterative algorithm. Numerical results show that our proposed scheme is able to converge and significantly reduce the total energy consumption of the MEC system compared to the benchmark schemes.

A green, secure, and deep intelligent method for dynamic IoT-edge-cloud offloading scenarios

In multi-access edge computing systems with energy harvesting (MEC-EH), the mobile devices are empowered with unstable energy harvested from renewable energy sources. To prolong the life of mobile devices, as many computation-intensive tasks as possible should be offloaded to the MEC server. However, when the system states of mobile device and MEC server are unstable, e.g. poor communication channel conditions, a great number of tasks will be executed locally, leading to a long execution time. Even worse, some tasks may be dropped due to low energy levels. To address this problem, in this paper, we propose a multi-relay assisted computation offloading framework for MEC-EH systems. In this framework, a computation task can be executed by offloading to the MEC server with the help of multiple relay nodes, such as the neighboring nodes. We introduce execution cost as a performance metric to incorporate both the task execution time and task failure. We then develop a low-complexity online algorithm, namely MRACO algorithm, to minimize the average execution cost. MRACO algorithm can select the optimal execution strategy for each task from (1) executing the task locally, (2) offloading it to the MEC server directly, (3) offloading it to the MEC server with the help of the most suitable neighboring nodes, and (4) simply dropping it. Moreover, we also develop an algorithm for selecting the suitable neighboring devices to act as relays and determining the optimal task splitting ratio between them. Finally, performance evaluation shows that the proposed MRACO algorithm greatly outperforms the benchmarks in terms of both average execution time and task drop rate.

Multi-Relay Assisted Computation Offloading for Multi-Access Edge Computing Systems With Energy Harvesting

In mobile edge computing (MEC) systems with energy harvesting, the mobile devices are empowered with the energy that harvested from renewable energy sources. On the other hand, mobile devices can offload their computation-intensive tasks to the MEC server to further save energy and reduce the task execution latency. However, the energy harvested is unstable and the mobile devices have to make sure that the energy should not be run out. Moreover, the wireless channel condition between the mobile device and the MEC server is dynamically changing, leading to unstable communication delay. Considering the energy constraints and unstable communication delay, the benefit of computation offloading is limited. In this paper, we investigate D2D-assisted computation offloading for mobile edge computing systems with energy harvesting. In our method, the mobile device is allowed to offload its tasks to the MEC server with the help of its neighbor node. More Specifically, the neighbor node acts as a relay to help the mobile device to communicate with the MEC server. Our goal is to minimize the average task execution time by selecting an optimal execution strategy for each task, i.e., whether to execute the task locally, or offload it to the MEC server directly, or offload it to the MEC server with the help of the most suitable neighbor node, or just to drop it. We propose a low-complexity online algorithm, which stem from Lyapunov Optimization-based Dynamic Computation Offloading (LODCO) algorithm, to solve this problem. Extensive simulations verified the effectiveness of the proposed algorithm, where the average task execution time is reduced around 50% as compared to that of the original LODCO algorithm.

D2D-Assisted Computation Offloading for Mobile Edge Computing Systems with Energy Harvesting

Mobile edge computing (MEC) is a promising technique to reduce users’ response latency by deploying computation and storage resources at the network edge. In a multi-user MEC system, the MEC server may not be able to process the tasks offloaded from all users in its coverage area since it has limited computation resources as compared to the remote cloud. Device-to-device (D2D) assisted computation offloading may help to relieve the pressure on the MEC server, however, D2D communication is not always possible due to its short communication range. To overcome the limitation of D2D-assisted computation offloading, in this paper, we study the computation offloading of computation-intensive tasks in a single base station multi-user MEC system and propose a cooperative computation offloading scheme to fully utilize the computation resources of the idle devices. The key idea is that the busy devices partially offload tasks to the MEC server, and the server can further offload tasks that cannot be accommodated to the idle devices. We model the computation offloading process as a sequential game and prove that the process can reach Nash equilibrium. Thus, each busy device can obtain the optimal decision for task offloading. A multi-user cooperative offloading algorithm is also proposed to solve the problem. Through extensive simulations, we verify the effectiveness of the proposed scheme and demonstrate that the proposed scheme can reduce the average task execution delay, as compared with other three schemes where cooperative computation offloading is not allowed.

Multi-user Cooperative Computation Offloading in Mobile Edge Computing

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Multi-Relay Assisted Computation Offloading for Multi-Access Edge Computing Systems With Energy Harvesting

D2D-Assisted Computation Offloading for Mobile Edge Computing Systems with Energy Harvesting

Multi-user Cooperative Computation Offloading in Mobile Edge Computing