Offloading Decision Algorithm Research Articles

A communication link lifetime prediction-supported V2V partial computation offloading scheme for autonomous driving

Vehicle Edge Computing (VEC) is a promising technique to improve the quality of service (QoS) and quality of experience (QoE) in autonomous driving by exploiting the resources at the network edge. However, the high mobility of the vehicles leads to stochastic communication link duration, and the tasks generated by various applications in autonomous driving incur fierce competition for resources. These challenges cause excessive task completion delays. In this paper, we propose a vehicle-to-vehicle (V2V) partial computation offloading scheme that leverages the prediction results of the communication link lifetime between vehicles. A History track, Current interactions and Future planning trajectory-aware Gated Recurrent Units (HCF-GRU) network is built to capture the essential factors to improve the prediction accuracy. Then, we design a GRU-based Proximal Policy Optimization (GRU-PPO) algorithm to obtain an optimal one-to-many offloading decision to minimize the task execution cost. The HCF-GRU prediction algorithm is evaluated on a real world vehicle trajectory dataset, and the performance of the GRU-PPO algorithm is analyzed on extensive numerical simulations. Experimental results demonstrate that our prediction network and offloading decision algorithm outperform the baseline methods in terms of prediction accuracy and task execution cost.

Resource allocation strategy for blockchain-enabled NOMA-based MEC networks

Blockchain technology is getting more and more attention due to its decentralization, independence and security features. However, in wireless networks it faces a computational challenge: the proof-of-work problem. Mobile edge computing (MEC) leads to a vaild scheme by providing cloud computing capabilities to mobile devices. Non-orthogonal multiple access (NOMA) exploits the diversity properties in the power domain to further increase system throughput and spectral efficiency. In this paper, we suggest a new NOMA-based MEC wireless blockchain network to minimize system energy consumption through task offloading decision optimization, user clustering, computing resource and transmit power allocation. In order to effectively figure out this non-convex problem, we first propose a offloading decision and user clustering algorithm, and then propose a computing resource allocation algorithm based on user Quality of Service (QoS) requirements. Finally, the transmission power can be easily determined. The numerical simulation results verify that the proposed joint optimization algorithm can effectively decrease the system energy consumption.

Task scheduling of computation-intensive graph jobs in UAV-assisted hybrid vehicular networks

As a promising new form in mobile edge computing network, Vehicular Edge Computing (VEC) can further improve the Quality of Service (QoS) of users. However, when edge servers are damaged or overloaded, the requirements of users are difficult met by VEC servers owing to incomplete connectivity and time-varying channel conditions in dynamic and resource-limited vehicular networks. In this paper, we introduce Unmanned Aerial Vehicle (UAV) to enhance the connectivity and total resources in the vehicular networks. Added to that, we focus on the problem of computation-intensive graph jobs scheduling, where the information interactions among the traditional vehicles, autonomous vehicles and UAV are integrated. And the above scheduling problem is modeled as a binary nonlinear programming problem aiming at minimizing average completion delay of jobs. Then, we develop an effective multiple jobs scheduling scheme by considering the scheduling order of multiple jobs, the remaining processing delay of servers, and the available resources of vehicular networks, which consists of two stages: (1) the multiple jobs priority scheduling algorithm for optimizing the jobs scheduling order; (2) the task offloading decision algorithm for optimizing average completion delay of jobs. Finally, the effectiveness of proposed scheme is proved by extensive simulation experiments. Compared with the other schemes, the proposed scheme can effectively reduce average completion delay of jobs.

Edge Computing Task Offloading for Environmental Perception of Autonomous Vehicles in 6G Networks

The most basic requirement for autonomous vehicles is accurate environmental perception. However, the lack of onboard computing resources makes it difficult for such a vehicle to process all environmental information. Through the edge computing technology and 6G networks, vehicles can offload computing tasks to edge servers for execution, which alleviates the problem of insufficient onboard resources. The environmental perception task offloading problem for autonomous vehicles is studied. The purpose is to improve the environmental perception quality through reasonable task offloading. After assigning dynamic priorities to tasks generated by autonomous vehicles, the multi-vehicle and multi-server task offloading process is abstracted as a Markov decision process, and an offloading decision algorithm based on deep reinforcement learning is designed to select edge nodes for task execution to obtain better long-term benefits. The earliest deadline first algorithm is improved to consider the deadline and priority of each task, allowing the edge node to complete more high-priority tasks within the specified time. Experimental results show that the proposed method can guarantee the basic environmental perception requirements of every vehicle, and performs better than existing methods in terms of the total priority of the discarded tasks in each scheduling period and the completion rate of key tasks.

A Multihop Task Offloading Decision Model in MEC-Enabled Internet of Vehicles

As a new network technology, mobile-edge computing (MEC) combined with the Internet of Vehicles (IoV) can effectively improve the efficiency of task computing and offloading. However, the power of edge computing will be severely limited to the areas with poor MEC server coverage. Furthermore, there are a number of peripheral vehicles with temporarily idle computing resources on the road, so how to put the resources of these vehicles into use becomes the primary issue to be considered. In this article, a distributed multihop task offloading decision model for task execution efficiency is developed, which mainly consists of two parts: 1) a candidate vehicle selection mechanism for screening the neighboring vehicles that can participate in offloading and 2) a task offloading decision algorithm for obtaining the task offloading solution. Considering the impact of different hop and wireless communication ranges on communication ranges on task completion in a generic scenario, we introduce the hop count <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> and select the neighboring vehicles in the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -hop wireless communication range as the candidate vehicles. Then, the problem of offloading is modeled as a generalized allocation model with constraints which is solved by the greedy algorithm and discrete bat algorithm, respectively. The results show that compared with the scheme in which the task vehicle randomly selects the neighboring vehicles to offload and the scheme that all tasks are completed locally, the offloading scheme in which all tasks are completed under the greedy algorithm or bat-based algorithm has advantages in time delay performance in terms of different task number, task required computation power, and task size environment. Besides, this article also explores the influence of hop count <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> on the results when selecting candidate vehicles from the neighboring vehicles within the range of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> hop. The results show that the increase of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> will also increase the number of candidate vehicles, which makes the time delay lower. Under the parameters set in this article, the time delay required for the greedy algorithm offloading scheme to complete all tasks is a lower bound on the time delay of the bat algorithm scheme. The greedy algorithm scheme reduces latency by 0.2–2.4 s compared to the scheme where tasks are all completed locally, and it reduces latency by 0.16–2.3 s compared to the random offloading scheme.

Inter-Satellite Cooperative Offloading Decision and Resource Allocation in Mobile Edge Computing-Enabled Satellite–Terrestrial Networks

Mobile edge computing (MEC)-enabled satellite-terrestrial networks (STNs) can provide task computing services for Internet of Things (IoT) devices. However, since some applications' tasks require huge amounts of computing resources, sometimes the computing resources of a local satellite's MEC server are insufficient, but the computing resources of neighboring satellites' MEC servers are redundant. Therefore, we investigated inter-satellite cooperation in MEC-enabled STNs. First, we designed a system model of the MEC-enabled STN architecture, where the local satellite and the neighboring satellites assist IoT devices in computing tasks through inter-satellite cooperation. The local satellite migrates some tasks to the neighboring satellites to utilize their idle resources. Next, the task completion delay minimization problem for all IoT devices is formulated and decomposed. Then, we propose an inter-satellite cooperative joint offloading decision and resource allocation optimization scheme, which consists of a task offloading decision algorithm based on the Grey Wolf Optimizer (GWO) algorithm and a computing resource allocation algorithm based on the Lagrange multiplier method. The optimal solution is obtained by continuous iterations. Finally, simulation results demonstrate that the proposed scheme achieves relatively better performance than other baseline schemes.

Deep reinforcement learning based offloading decision algorithm for vehicular edge computing.

Task offloading decision is one of the core technologies of vehicular edge computing. Efficient offloading decision can not only meet the requirements of complex vehicle tasks in terms of time, energy consumption and computing performance, but also reduce the competition and consumption of network resources. Traditional distributed task offloading decision is made by vehicles based on local states and can’t maximize the resource utilization of Mobile Edge Computing (MEC) server. Moreover, the mobility of vehicles is rarely taken into consideration for simplification. This article proposes a deep reinforcement learning based task offloading decision algorithm, namely Deep Reinforcement learning based offloading decision (DROD) for Vehicular Edge Computing (VEC). In this work, the mobility of vehicles and the signal blocking commonly in VEC circumstance are considered in our optimal problem of minimizing the system overhead. For resolving the optimal problem, the DROD employs Markov decision process to model the interactions between vehicles and MEC server, and an improved deep deterministic policy gradient algorithm called NLDDPG to train the model iteratively to obtain the optimal decision. The NLDDPG takes the normalized state space as input and introduces LSTM structure into the actor-critic network for improving the efficiency of learning. Finally, two series of experiments are conducted to explore DROD. Firstly, the influences of core hyper-parameters on the performances of DROD are discussed, and the optimal values are determined. Secondly, the DROD is compared with some other baseline algorithms, and the results show that DROD is 25% better than DQN, 10% better than NLDQN and 130% better than DDDPG.

A New Model of Multiple Intelligence for Teaching English Informatics in the IoT Scenario.

This paper presents an in-depth study on the new mode of intelligent multidistance teaching of English with the help of virtual scenes of the Internet of Things. The virtual simulation technology is integrated into the traditional IoT teaching, and the professional education of IoT application technology is tapped; from the analysis of the current situation of IoT skills teaching and the feasibility of carrying out virtual simulation teaching, the “four-driven” design principle is proposed, and the teaching design is combined with the virtual simulation technology teaching, and the case design of the skill-based virtual simulation technology teaching of experience, demonstration, interaction, and assessment in the virtual environment is given. This paper presents the case design of virtual simulation teaching in a virtual environment with experience, demonstration, interaction, and assessment, and the multidimensional effect evaluation of IoT skills teaching and researches the application of virtual simulation to IoT skills teaching through the above four aspects. In this paper, a framework for distributed collaborative computing is built using an asynchronous message queue MQ, which enables multiple nodes to serve a task through task splitting. The DeepCluster module can effectively cluster the time series by deep representation learning and obtain the typical variation of time series patterns. In the task offloading module of the framework, a task offloading decision algorithm based on a value-constrained multi 0–1 backpacking model is designed to minimize task processing latency with an optimal offloading solution. The system test results show that the proposed distributed computing framework and offloading decision algorithm can significantly reduce the processing latency of large tasks.

Data Offloading in the Internet of Vehicles Using a Hybrid Optimization Technique

The Internet of Vehicles (IoV) is utilized for collecting enormous real time information driven traffics and alert drivers depending on situations. In recent times, all smart vehicles are developed with IoT devices. These devices communicate with a radio access unit (RAU) at road side. Moreover, a 5G system is equipped with a base station and connection interfaces that use optic fiber for their effective communication. For a fast mode of communication, the IoV must offload its data to the nearest edge nodes. The main problem with the IoV is that it generates enormous data which is offloaded randomly during the journey. This data exceeds the memory of the edge or road side unit (RSU) devices. This feature also causes substantial energy usage and high storage cost. To overcome the above issues, hybrid optimization techniques are suggested to offload the data with an energy efficient destination. In this research, the Clustered Block Chain Based Grasshopper Optimizer (BC-GAO) Based Task Offloading is implemented for a green IoV. The block chain provides a secure environment for sharing extensive data. The IoV data are clustered according to the vehicle location, and the grasshopper optimization is employed in selecting the RSU to offload the data. The result for the proposed technique is evaluated using OPNET Modulator and MATLAB. The simulated results are compared with the other existing techniques such as Random Offloading, Full Offloading, Mobility Aware Task Offloading, and the Lyapunov-based Dynamic Offloading Decision algorithm. The proposed algorithm computed 100 tasks in 33.27 s by consuming 19.23 joule energy, a value which is lower than all other existing techniques.

Efficient and Energy-Saving Computation Offloading Mechanism with Energy Harvesting for IoT

In order to effectively extend the lifetime of Internet of Things (IoT) devices, improve the energy efficiency of task processing, and build a self-sustaining and green edge computing system, this paper proposes an efficient and energy-saving computation offloading mechanism with energy harvesting for IoT. Specifically, based on the comprehensive consideration of local computing resource, time allocation ratio of energy harvesting, and offloading decision, an optimization problem that minimizes the total energy consumption of all user devices is formulated. In order to solve such optimization problem, a deep learning-based efficient and energy-saving offloading decision and resource allocation algorithm is proposed. The design of deep neural network architecture incorporating regularization method and the employment of the stochastic gradient descent method can accelerate the convergence rate of the developed algorithm and improve its generalization performance. Furthermore, it can minimize the total energy consumption of task processing by integrating the momentum gradient descent to solve the resource optimization allocation problem. Finally, the simulation results show that the mechanism proposed in this paper has significant advantage in convergence rate and can achieve an optimal offloading and resource allocation strategy that is close to the solution of greedy algorithm.

Implementing Practical DNN-Based Object Detection Offloading Decision for Maximizing Detection Performance of Mobile Edge Devices

In the last decade, deep neural network (DNN)-based object detection technologies have received significant attention as a promising solution to implement a variety of image understanding and video analysis applications on mobile edge devices. However, the execution of computationally intensive DNN-based object detection workloads in mobile edge devices is insufficient in fulfilling the object detection requirements with high accuracy and low latency, owing to the limited computation capacity. In this paper, we implement and evaluate a DNN-based object detection offloading framework to improve the object detection performance of mobile edge devices by offloading computation-intensive workloads to a remote edge server. However, preliminary experimental results have shown that offloading all object detection workloads of mobile edge devices may lead to worse performance than executing the workloads locally. This degradation is obtained from the inefficient resource utilization in the edge computing architectures, both for the edge server and mobile edge devices. To resolve the aforementioned problem with degradation, we devise a device-aware DNN offloading decision algorithm that is aimed to maximize resource utilization in the edge computing architecture. The proposed algorithm decides whether or not to offload the object detection workloads of edge devices by considering their computing power and network bandwidth, and therefore maximizing their average object detection processing frames per second. Through various experiments conducted in a real-life wireless local area network (WLAN) environment, we verified the effectiveness of the proposed DNN-based object detection offloading framework.

Long-term optimization for MEC-enabled HetNets with device–edge–cloud collaboration

For effective computation offloading with multi-access edge computing (MEC), both communication and computation resources should be properly managed, considering the dynamics of mobile users such as the time-varying demands and user mobility. Most existing works regard the remote cloud server as a special edge server. However, service quality cannot be met when some of the edge servers cannot be connected. Besides, the computation capability of the cloud has not been fully exploited especially when edge servers are congested. We develop an on-line offloading decision and computational resource management algorithm with joint consideration of collaborations between device–cloud, edge–edge and edge–cloud. The objective is to minimize the total energy consumption of the system, subject to computational capability and task buffer stability constraints. Lyapunov optimization technique is used to jointly deal with the delay-energy trade-off optimization and load balancing. The optimal CPU-cycle frequencies, best transmission powers and offloading scheduling policies are jointly handled in the three-layer system. Extensive simulation results demonstrate that, with V varies in [0.1,5]×109, the proposed algorithm can save more than 50% energy and over 120% task processing time than three existing benchmark algorithms averagely.

Distributed Task Offloading Game in Multiserver Mobile Edge Computing Networks

With the explosion of data traffic, mobile edge computing (MEC) has emerged to solve the problem of high time delay and energy consumption. In order to cope with a large number of computing tasks, the deployment of edge servers is increasingly intensive. Thus, server service areas overlap. We focus on mobile users in overlapping service areas and study the problem of computation offloading for these users. In this paper, we consider a multiuser offloading scenario with intensive deployment of edge servers. In addition, we divide the offloading process into two stages, namely, data transmission and computation execution, in which channel interference and resource preemption are considered, respectively. We apply the noncooperative game method to model and prove the existence of Nash equilibrium (NE). The real-time update computation offloading algorithm (RUCO) is proposed to obtain equilibrium offloading strategies. Due to the high complexity of the RUCO algorithm, the multiuser probabilistic offloading decision (MPOD) algorithm is proposed to improve this problem. We evaluate the performance of the MPOD algorithm through experiments. The experimental results show that the MPOD algorithm can converge after a limited number of iterations and can obtain the offloading strategy with lower cost.

Adaptive Real-Time Offloading Decision-Making for Mobile Edges: Deep Reinforcement Learning Framework and Simulation Results

This paper proposes a novel dynamic offloading decision method which is inspired by deep reinforcement learning (DRL). In order to realize real-time communications in mobile edge computing systems, an efficient task offloading algorithm is required. When the decision of actions (offloading enabled, i.e., computing in clouds or offloading disabled, i.e., computing in local edges) is made by the proposed DRL-based dynamic algorithm in each unit time, it is required to consider real-time/seamless data transmission and energy-efficiency in mobile edge devices. Therefore, our proposed dynamic offloading decision algorithm is designed for the joint optimization of delay and energy-efficient communications based on DRL framework. According to the performance evaluation via data-intensive simulations, this paper verifies that the proposed dynamic algorithm achieves desired performance.

Energy-efficient offloading decision-making for mobile edge computing in vehicular networks

Driven by the explosion transmission and computation requirement in 5G vehicular networks, mobile edge computing (MEC) attracts more attention than centralized cloud computing. The advantage of MEC is to provide a large amount of computation and storage resources to the edge of networks so as to offload computation-intensive and delay-sensitive applications from vehicle terminals. However, according to the mobility of vehicle terminals and the time varying traffic load, the optimal task offloading decisions is crucial. In this paper, we consider the uplink transmission from vehicles to road side units in the vehicular network. A dynamic task offloading decision for flexible subtasks is proposed to minimize the utility, which includes energy consumption and packet drop rate. Furthermore, a computation resource allocation scheme is introduced to allocate the computation resources of MEC server due to the differences in the computation intensity and the transmission queue of each vehicle. Consequently, a Lyapunov-based dynamic offloading decision algorithm is proposed, which combines the dynamic task offloading decision and computation resource allocation, to minimize the utility function while ensuring the stability of the queue. Finally, simulation results demonstrate that the proposed algorithm could achieve a significant improvement in the utility of vehicular networks compared with comparison algorithms.

Latency‐minimum offloading decision and resource allocation for fog‐enabled Internet of Things networks

AbstractThe rapid growth of the number of sensing devices enables computation offloading to be a promising solution to alleviate the burden of core network communication and provide low delay services, especially for those computation‐intensive and delay‐sensitive tasks. For meeting the processing requirements of these tasks sufficiently, a latency‐minimum offloading decision and resource allocation scheme for fog‐enabled Internet of Things (IoT) networks is developed in this article. Specifically, we formulate a joint optimization problem of the offloading decision, the local computation capability, and the computing resource allocation of fog node to minimize the task completion time with energy constraint, in which practically considers M/M/1 waiting queues in the wireless channel and fog node. To solve this mixed integer nonlinear programming problem with low complexity, we first calculate the optimal values of local computation capability and computing resource allocation of fog node by decomposing the original optimization problem into two independent subproblems. Subsequently, we propose a hybrid genetic simulated annealing‐based latency‐minimum offloading decision algorithm to optimize the offloading decision. Finally, the numerical results verify that our proposed scheme achieves significant advantages compared to other alternative schemes in terms of completion time and energy consumption, and they also confirm the advantage of convergence speed and quality.

Efficient Privacy Preserving Data Collection and Computation Offloading for Fog-Assisted IoT

The property of performing data processing near the source of data (i.e., at the edge of the network) enables fog computing that can effectively reduce computation latency, bandwidth and energy consumption, especially for big data network scenarios. For the sake of achieving efficient and secure big sensory data collection in fog-assisted Internet of Things (IoT), this paper proposes an efficient privacy preserving data collection and computation offloading scheme. In the proposed scheme, first, the designed layer-aware fog computing architecture provides effective support for efficient and secure data collection and fog computation offloading. Then the proposed sampling perturbation encryption method protects data privacy against eavesdroppers and active attackers without sacrificing data correlation, and it also facilitates the simultaneous execution of decrypting and decompressing operations on encrypted sampling data. Furthermore, the developed data processing method at fog nodes reduces the amount of redundant data transmissions significantly, and the formulated optimization model for the measurement matrix ensures the high precision of data reconstruction at the end user. Particularly, a completion time minimization problem is formulated for fog computation offloading, and an efficient offloading decision algorithm is developed to find the minimum completion time by determining the optimal offloading proportion with joint optimal allocation of local CPU, external CPU and channel bandwidth resources. Finally, the illustrative results reveal that the proposed scheme is an efficient data collection and computation offloading scheme with a strong privacy preservation property. For example, when the temporal compression ratio is 0.5, the redundant data can be reduced by 65 percent at fog node with a low relative recovery error 0.0139. At the same time when the task size is 9 Mb, the completion time of compression computation task at fog node can be reduced by 14.6 percent compared with other computation offloading method.

Offloading Decision Algorithm Based on Distance Weighted K-Nearest Neighbor in Power Internet of Things

Offloading Decision Algorithm Based on Distance Weighted K-Nearest Neighbor in Power Internet of Things

Resource allocation of English intelligent learning system based on reinforcement learning

The communication energy efficiency of the English intelligent learning system is affected by many factors. In order to improve the system operation efficiency, it is necessary to carry out analysis from the perspective of the mobile edge server and the system structure. However, there are still many research gaps and deficiencies. Based on a dynamic and heterogeneous English intelligent learning system, this paper designs an adaptive offloading decision algorithm ADCO and an online spring slide task scheduling algorithm SSLS. Moreover, this paper has proposed corresponding solutions to the computational offloading problem and online scheduling problem in a dynamic environment and compared and analyzed the performance of this research algorithm through simulation experiments. The research results show that the method proposed in this paper has certain effects.

Cooperative Runtime Offloading Decision Algorithm for Mobile Cloud Computing

Mobile cloud computing (MCC) provides a platform for resource-constrained mobile devices to offload their tasks. MCC has the characteristics of cloud computing and its own features such as mobility and wireless data transmission, which bring new challenges to offloading decision for MCC. However, most existing works on offloading decision assume that mobile cloud environments are stable and only focus on optimizing the consumption of offloaded applications but ignore the consumption caused by offloading decision algorithms themselves. This paper focuses on runtime offloading decision in dynamic mobile cloud environments with the consideration of reducing the offloading decision algorithm’s consumption. A cooperative runtime offloading decision algorithm, which takes advantage of the cooperation of online machine learning and genetic algorithm to make offloading decisions, is proposed to address this problem. Simulations show that the proposed algorithm helps offloaded applications save more energy and time while consuming fewer computing resources.