Energy and Delay Efficient Computation Offloading Solutions for Edge Computing

Mobile devices may use mobile edge computing to improve energy efficiency and responsiveness by offloading computation tasks to edge servers. However, the transmissions of mobile devices may result in interference that decreases the upload rate and prolongs transmission delay. Clustering has been shown as an effective approach to improve the transmission efficiency for dense devices, but there is no distributed algorithm for the optimization of clustering and computation offloading. In this work, we study the optimization problem of computation offloading to minimize the energy consumption of mobile devices in mobile edge computing by adaptively clustering devices to improve the transmission efficiency. To address the optimization problem in a distributed manner, the decision problem of clustering and computation offloading for mobile devices is formulated as a potential game. We introduce the construction of the potential game and show the existence of Nash equilibrium in the game with a finite enhancement ability. Then, we propose a distributed algorithm of clustering and computation offloading based on game theory. We conducted a simulation to evaluate the proposed algorithm. The numerical results from our simulation show that our algorithm can improve offloading efficiency for mobile devices in mobile edge computing by improving transmission efficiency. By offloading more tasks to edge servers, both the energy efficiency of mobile devices and the responsiveness of computation-intensive applications can be improved simultaneously.

Mobile Edge Computing (MEC) migrates the computing center to the network edge to provide computing services for Mobile Users (MUs). However, due to the limited capacity of MUs and insufficient types of services stored by edge servers, it brings great challenges to computation offloading and service caching in Internet of Things (IoT) network. In this paper, we investigate the joint optimization problem of computation offloading, resource allocation and service caching replacement in a collaborative MEC system to minimize the total cost of all MUs under the latency and resource constraints. Accordingly, we formulate the problem as a Mixed Integer Non-linear Programming (MINLP) problem. In order to solve this problem, the Deep Deterministic Policy Gradient (DDPG)-based algorithm is proposed. The simulation results show that the proposed algorithm is better than other benchmark schemes in different scenarios.

Deep Neural Networks meet computation offloading in mobile edge networks: Applications, taxonomy, and open issues

Wireless devices in Internet of Things (IoT) applications, such as wireless sensors and Radio Frequency Identifications (RFIDs), are faced with challenges of heavy computation tasks and limited energy, which can be solved by the importation of mobile edge computing (MEC) and wireless power transfer (WPT) techniques. As MEC can effectively enhance computation capability, and the wireless power transfer can ensure a sustainable supply of energy, it has drawn significant research interest in IoT applications. In this paper, we will study the resource allocation problem in the wireless-powered MEC system for IoT applications with one access point (AP) and many other wireless devices, and propose a Stackelberg dynamic game model to obtain the optimal allocated resource for the nodes in the IoT environment. The AP is a wireless power source that can charge wireless devices based on wireless power transfer techniques. The AP is also integrated with a MEC server that can carry out computation tasks that offload from wireless devices. The wireless devices can use the harvested energy to execute and offload computation tasks to the AP. Based on the proposed game model, the AP and wireless devices can control their optimal transmit power for energy transfer, and computation tasks offloading to the AP, respectively. The numerical simulation results show the correctness and effectiveness of the proposed model.

Collaborative computation offloading and wireless charging scheduling in multi-UAV-assisted MEC networks: A TD3-based approach

This paper considers the computation offloading and resource allocation for system sum cost minimization in a mobile edge computing (MEC)network. The computation offloading decision of the user equipments (UEs)can be made through wireless link to access the MEC server. Our intent is to minimize the weighted sum cost of the system, which takes into account the energy consumption and time delay, for both task processing and data transmissions, subject to the force delay requirement of the devices. We first formulate the problem as a mixed-integer and non-convex optimization scheme. Enlightened by the superior performance of reinforcement learning (RL)on solving resource control problems, we put forward a novel RL-based scheme for computation offloading and resource allocation in MEC network. Specifically, the MEC node is considered as an “agent”, which first makes decision on whether to offloading the computing task to the MEC server, and then utilizes convex optimization algorithms to settle the radio spectrum and computation resource scheduling problem in each decision phase. The simulation results present that the RL-based method can achieve better computation offloading and resource allocation performance compared to those of local computing and remote computing modes. It also achieves very similar or slightly better performance compared to the genetic algorithm based method.

Mobile edge computing (MEC) has appeared as a promising solution to fill the gap between the growing computationally intensive applications and limited computation capability of mobile devices by providing powerful computing services at the edge of the wireless access network. To use the services provided by the MEC more effectively, making efficient and reasonable offloading decisions is crucial. In this paper, we study the computation offloading of tasks from multiple users to a single-cell edge server under a dynamic environment. We consider a practical case wherein a group of mobile users with random mobility patterns use a common set of time-varying stochastic transmission channels to perform computation offloading, and the number of active users in the system randomly changes. To reduce the mutual interference among users when accessing the wireless channels, we adopt game theory to formulate the users’ computation offloading decision process as a stochastic game model. Next, we prove the existence of the Nash Equilibrium (NE) for the proposed game model by showing its equivalency to a weighted potential game which has at least one pure-strategy NE point. Then, we present distributed computation offloading algorithms by adopting a payoff-based multi-agent reinforcement learning (MARL) approach to reach the NE of the game. Finally, through simulation, we validate the effectiveness of the proposed algorithms by comparing them with the results obtained from other previously studied multi-agent learning algorithms as well as conventional Q-learning and deep Q-learning algorithms.

Cloud Computing (CC) is powerful for the computation offloading of services, promoting the implementation of various modern applications. Mobile Edge Computing (MEC) can provide low-latency services utilizing edge servers locating in proximity to users. The combination of MEC and CC can give play to the dual advantages of both. However, it is a challenging problem to offload service requests to the collaborative edge-cloud networks aiming at minimizing costs due to the resource limitation of edge servers and the online feature of services. To address this issue, we mathematically model the service requests with multiple inter-connected functions. Then, the problem of computation offloading of multi-function service requests in collaborative edge-cloud networks is formulated to be an Integer Linear Programming (ILP) and is proved to be NP-hard. Furthermore, a Cost-minimized Computation Offloading with Reconfiguration (CCOR) algorithm is proposed to minimize the total cost of online services. Finally, simulation results show that the proposed CCOR algorithm can effectively reduce the cost of computation offloading with higher resource utilization of edge cloud compared with baseline algorithms.

Mobile edge computing (MEC) emerged as a promising network paradigm that provides computation, storage and networking features within the edge of the pervasive mobile radio access network. This paper jointly considers resource allocation and computation offloading problem in device-to-device (D2D)-assisted and non-orthogonal multiple access (NOMA)- based MEC systems, where each computing user (CU) is allowed to execute its task in one of the three ways, i.e., local computing, MEC offloading or D2D offloading. The original problem is formulated as an overall cost minimization problem, which proves to be NP-Hard, making it intractable to solve optimally. So, we transform the original problem into two sub-problems, i.e., resource allocation sub-problem and computation offloading sub-problem and propose two heuristic algorithms to obtain the sub-optimal solutions of both sub-problems. Finally, simulation results demonstrate the efficiency of proposed scheme when compared with related scheme.

Computation offloading services provide required computing resources for vehicles with computation-intensive tasks. Past computation offloading research mainly focused on mobile edge computing (MEC) or cloud computing, separately. This paper presents a collaborative approach based on MEC and cloud computing that offloads services to automobiles in vehicular networks. A cloud-MEC collaborative computation offloading problem is formulated through jointly optimizing computation offloading decision and computation resource allocation. Since the problem is non-convex and NP-hard, we propose a collaborative computation offloading and resource allocation optimization (CCORAO) scheme, and design a distributed computation offloading and resource allocation algorithm for CCORAO scheme that achieves the optimal solution. The simulation results show that the proposed algorithm can effectively improve the system utility and computation time, especially for the scenario where the MEC servers fail to meet demands due to insufficient computation resources.

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.

Computationally intensive and latency sensitive applications put forward strict requirements on mobile user equipments (UEs) to provide the required computation capacity and delay. Mobile edge computing (MEC) in ultra-dense network (UDN) is considered as a promising solution that can reduce the computation limitations of UEs and extend their battery life by computation offloading. But the dense deployment of UEs, and the limited resources of small cell base stations (SBSs), which imposes more delays on computation offloading in MEC environment. In this paper, using the characteristics of UDN, we study the computation offloading management scheme based on the weighted sum of task completion time and energy consumption in MEC system. Then, we propose a computation offloading framework and formulate joint task offloading and resource allocation problem in UDNs to minimize overall cost consumption. Based on Genetic algorithm (GA) and Whale optimization algorithm (WOA), we adopt a heuristic algorithm named as GAWOA to solve this problem. Numerical results show that the algorithm has a fast convergence speed and better performance than other benchmark algorithms.

Mobile edge computing (MEC) has attracted great interests as a promising approach to augment computational capabilities of mobile devices. An important issue in the MEC paradigm is computation offloading. In this paper, we propose an integrated framework for computation offloading and interference management in wireless cellular networks with mobile edge computing. In this integrated framework, the MEC server makes the offloading decision according to the local computation overhead estimated by all user equipments (UEs) and the offloading overhead estimated by the MEC server itself. Then, the MEC server performs the PRB allocation using graph coloring. The outcomes of the offloading decision and PRB allocation are then used to allocate the computation resource of the MEC server to the UEs. Simulation results are presented to show the effectiveness of the proposed scheme with different system parameters.

In mobile edge computing (MEC), mobile devices limited to computation and memory resources offload compute-intensive tasks to nearby edge servers. User movement causes frequent handovers in 5G urban networks. The resultant delays in task execution due to unknown user position and base station lead to increased energy consumption and resource wastage. The current MEC offloading solutions separate computation offloading from user mobility. For task offloading, techniques that predict the user’s future location do not consider user direction. We propose a framework termed COME-UP Computation Offloading in mobile edge computing with Long-short term memory (LSTM) based user direction prediction. The nature of the mobility data is nonlinear and leads to a time series prediction problem. The LSTM considers the previous mobility features, such as location, velocity, and direction, as input to a feed-forward mechanism to train the learning model and predict the next location. The proposed architecture also uses a fitness function to calculate priority weights for selecting an optimum edge server for task offloading based on latency, energy, and server load. The simulation results show that the latency and energy consumption of COME-UP are lower than the baseline techniques, while the edge server utilization is enhanced.

The rapid advancement of mobile edge computing (MEC) has led to a growing need for executing complex in‐vehicle applications at the endpoint. These applications are often modeled as workflows with clearly defined task dependencies. In MEC environments, computation offloading plays a crucial role in enhancing Quality of Service (QoS). Nevertheless, most existing offloading strategies primarily address independent tasks, paying little attention to the intricate dependencies among tasks. Moreover, key factors such as budgets and privacy protection are not well tackled. Hence, in this paper, we propose a computation offloading model for workflow applications in mobile edge computing, where the makespan is optimized under budget and privacy constraints. To address it, we introduce an intelligent scheduling method, which consists of task offloading and resource preparation stages, and offloads tasks from the end to the edge and clouds with awareness of task urgency, device computing capabilities, economic costs, and privacy protection. Simulation experiments are conducted based on realistic datasets. Results demonstrate that the proposed method can improve task execution efficiency and ensure data security, thereby enhancing the performance of workflow applications in mobile edge computing scenarios.