Computation Offloading Model Research Articles

Research on collaborative edge network service migration strategy based on crowd clustering

The innovative application of Crowd Intelligent Devices (CIDS) in edge networks has garnered attention due to the rapid development of artificial intelligence and computer technology. This application offers users more reliable and low-latency computing services through computation offloading technology. However, the dynamic nature of network terminals and the limited coverage of edge servers pose challenges, such as data loss and service interruption. Furthermore, the high-speed mobility of intelligent terminals in the dynamic edge network environment further complicates the design of computation offloading and service migration strategies. To address these challenges, this paper explores the computation offloading model of cluster intelligence collaboration in a heterogeneous network environment. This model involves multiple intelligences collaborating to provide computation offloading services for terminals. To accommodate various roles, a switching strategy of split-cluster group collaboration is introduced, assigning the cluster head, the alternate cluster head, and the ordinary user are assigned to a group with different functions. Additionally, the paper formulates the optimal offloading strategy for group smart terminals as a Markov decision process, taking into account factors such as user mobility, service delay, service accuracy, and migration cost. To implement this strategy, the paper utilizes the deep reinforcement learning-based CCSMS algorithm. Simulation results demonstrate that the proposed edge network service migration strategy, rooted in groupwise cluster collaboration, effectively mitigates interruption delay and enhances service migration efficiency.

A Deep-Reinforcement-Learning-Based Computation Offloading With Mobile Vehicles in Vehicular Edge Computing

Vehicular edge networks involves edge servers that are close to mobile devices to provide extra computation resource to complete the computation tasks of mobile devices with low latency and high reliability. Considerable efforts on computation offloading in vehicular edge networks have been developed to reduce the energy consumption and computation latency, in which roadside units (RSUs) are usually considered as the fixed edge servers. Nonetheless, the computation offloading with considering mobile vehicles as mobile edge servers in vehicular edge networks still needs to be further investigated. To this end, in this paper, we propose a deep reinforcement learning based computation offloading with mobile vehicles in vehicular edge computing, namely DRL-COMV, in which some vehicles (such as autonomous vehicle) are deployed and considered as the mobile edge servers that move in vehicular edge networks and cooperate with fixed edge servers to provide extra computation resource for mobile devices, in order to assist in completing the computation tasks of these mobile devices with great QoE (i.e.,low latency) for mobile devices. Particularly, the computation offloading model with considering both mobile and fixed edge servers is conducted to achieve the computation tasks offloading through vehicle-to-vehicle (V2V) communications, and a collaborative route planning is considered for these mobile edge servers to move in vehicular edge networks with objective of improving efficiency of computation offloading. Then, a deep reinforcement learning approach with designing rational reward function is proposed to determine the effective computation offloading strategies for multiple mobile devices and multiple edge servers with objective of maximizing both QoE (i.e., low latency) for mobile devices. Through performance evaluations, our results show that our proposed DRL-COMV scheme can achieve a great convergence and stability. Additionally, our results also demonstrate that our DRL-COMV scheme also can achieve better both QoE and task offloading requests hit ratio for mobile devices in comparison with existing approaches (i.e., DDPG, IMOPSOQ and GABDOS).

Computational rate maximization for SWIPT-based mobile edge computing with alternative optimization algorithm

With the rapid integration of Intelligent Computing and Internet of Things technology (IoT), Iot sensor network devices are expected not only gather signal but also handle data computing. However, the intrinsic shortcomings of IoT devices on limited computing capacity and battery energy are the bottlenecks. Recently, Wireless Power Transmission (WPT) integrated with Mobile Edge Computing (MEC) provides an effective method for energy and computing resource allocation of IoT devices. However,the offloading ratio strategy and computing and energy resources allocation problems are challenging because the coupling of WPT and MEC, especially when the cost of feedback cannot be ignored. In this paper, we consider simultaneous power transfer and wireless information (SWIPT) strategy for an MEC powered IoT network, which allows the hybrid access point (HAP) to transfer energy by the feedback signal so that IoT devices can capture energy and receive feedback data simultaneously Firstly,the WP-MEC network model, local computation model and computation offloading model of the edge wireless devices (WDs) were built. Secondly, the causal relationship among computational rate ,network channel gain, energy transmission time and transmission power of HAP, CPU computing cycle frequency of WDs and offloading ratio allocation of local computing tasks were analyzed. Then,the above resource allocation problem was modeled as a non-convex problem and was decomposed into two convex sub-problems of offloading ratio strategy and computing and energy allocation. Finally, an alternative optimization methods were proposed to solve these two sub-problems in a sequential and the optimal solution was derived. The proposed algorithm and strategy were effective with the simulation results.

Optimizing Task Offloading Energy in Multi-User Multi-UAV-Enabled Mobile Edge-Cloud Computing Systems

With the emergence of various new Internet of Things (IoT) devices and the rapid increase in the number of users, enormous services and complex applications are growing rapidly. However, these services and applications are resource-intensive and data-hungry, requiring satisfactory quality-of-service (QoS) and network coverage density guarantees in sparsely populated areas, whereas the limited battery life and computing resources of IoT devices will inevitably become insufficient. Unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) is one of the most promising solutions that ensures the stability and expansion of the network coverage area for these applications and provides them with computational capabilities. In this paper, computation offloading and resource allocation are jointly considered for multi-user multi-UAV-enabled mobile edge-cloud computing systems. First, we propose an efficient resource allocation and computation offloading model for a multi-user multi-UAV-enabled mobile edge-cloud computing system. Our proposed system is scalable and can support increases in network traffic without performance degradation. In addition, the network deploys multi-level mobile edge computing (MEC) technology to provide the computational capabilities at the edge of the radio access network (RAN). The core network is based on software-defined networking (SDN) technology to manage network traffic. Experimental results demonstrate that the proposed model can dramatically boost the system performance of the system in terms of time and energy.

Multiuser Computing Offload Algorithm Based on Mobile Edge Computing in the Internet of Things Environment

As traditional cloud computing is not efficient enough to support large-scale computational task execution in IoT environments, a task offloading and resource allocation algorithm for mobile edge computing (MEC) is proposed in this paper. First, a multiuser computation offloading model is constructed, including a communication model and computation offloading model, which is transformed into the minimization of users’ time delay and energy consumption (i.e., total system overhead) in the MEC system. Then, the task offloading model is formulated into a Markov decision process, and an offloading strategy based on a deep Q network (DQN) is designed to dynamically make fine tunings on the offloading proportion of each user so as to realize a low-cost MEC system. The proposed algorithm is analyzed based on the constructed simulation platform. The simulation results show that when the number of user terminals is 40, the average delay of the proposed algorithm does not exceed 0.9 s, and the average energy consumption tends to 65 J, which is better than the comparison method. Therefore, the proposed algorithm has certain application prospects.

Computation offloading strategy based on deep reinforcement learning for connected and autonomous vehicle in vehicular edge computing

Connected and Automated Vehicle (CAV) is a transformative technology that has great potential to improve urban traffic and driving safety. Electric Vehicle (EV) is becoming the key subject of next-generation CAVs by virtue of its advantages in energy saving. Due to the limited endurance and computing capacity of EVs, it is challenging to meet the surging demand for computing-intensive and delay-sensitive in-vehicle intelligent applications. Therefore, computation offloading has been employed to extend a single vehicle’s computing capacity. Although various offloading strategies have been proposed to achieve good computing performace in the Vehicular Edge Computing (VEC) environment, it remains challenging to jointly optimize the offloading failure rate and the total energy consumption of the offloading process. To address this challenge, in this paper, we establish a computation offloading model based on Markov Decision Process (MDP), taking into consideration task dependencies, vehicle mobility, and different computing resources for task offloading. We then design a computation offloading strategy based on deep reinforcement learning, and leverage the Deep Q-Network based on Simulated Annealing (SA-DQN) algorithm to optimize the joint objectives. Experimental results show that the proposed strategy effectively reduces the offloading failure rate and the total energy consumption for application offloading.

Three-stage Stackelberg game based edge computing resource management for mobile blockchain

Blockchain can provide a dependable environment for mobile applications. Mining, as an important component in blockchain, requires a lot of computing resources, and hence resource limited moblie devices are unable to perform the mining. Offloading mining computation tasks to an edge computing service provider (ESP) or a cloud computing service provider (CSP) is considered as a feasible solution to mobile blockchain mining. However, the computing resources of the ESP are not unlimited. Therefore, rational edge computing resource management is critical to maximizing the utilities of the ESP and the miners. Most of the existing work assumes the computation is offloaded to either the CSP or the ESP which serves the terminal devices. However, an ESP can also offload the computation to the other ESPs, when the ESP is overloaded. In this paper, we construct a computation offloading model composed of multiple miners, multiple ESPs, and a CSP, where an overloaded ESP can offload the mining tasks to the CSP or the other ESPs or both. We propose a three-stage Stackelberg game for optimal pricing-based edge computing resource management. We analyze the existence and uniqueness of Stackelberg game equilibrium and derive the optimal amount of computing resource requests from the miners. We then propose a simple yet effective golden section based Stackelberg game equilibrium searching algorithm SES for resource pricing. We conduct experiments through simulations. Experimental results show that the proposed computing offloading model and algorithm can achieve high unit service utilities of both the ESPs and the terminal devices.

Dynamic computation offloading algorithm based on particle swarm optimization with a mutation operator in multi-access edge computing.

The current computation offloading algorithm for the mobile cloud ignores the selection of offloading opportunities and does not consider the uninstall frequency, resource waste, and energy efficiency reduction of the user's offloading success probability. Therefore, in this study, a dynamic computation offloading algorithm based on particle swarm optimization with a mutation operator in a multi-access edge computing environment is proposed (DCO-PSOMO). According to the CPU utilization and the memory utilization rate of the mobile terminal, this method can dynamically obtain the overload time by using a strong, locally weighted regression method. After detecting the overload time, the probability of successful downloading is predicted by the mobile user's dwell time and edge computing communication range, and the offloading is either conducted immediately or delayed. A computation offloading model was established via the use of the response time and energy consumption of the mobile terminal. Additionally, the optimal computing offloading algorithm was designed via the use of a particle swarm with a mutation operator. Finally, the DCO-PSOMO algorithm was compared with the JOCAP, ECOMC and ESRLR algorithms, and the experimental results demonstrated that the DCO-PSOMO offloading method can effectively reduce the offloading cost and terminal energy consumption, and improves the success probability of offloading and the user's QoS.

Joint Optimization Offloading Strategy of Execution Time and Energy Consumption of Mobile Edge Computing

As mobile devices become more and more powerful, applications generate a large number of computing tasks, and mobile devices themselves cannot meet the needs of users. This article proposes a computation offloading model in which execution units including mobile devices, edge server, and cloud server. Previous studies on joint optimization only considered tasks execution time and the energy consumption of mobile devices, and ignored the energy consumption of edge and cloud server. However, edge server and cloud server energy consumption have a significant impact on the final offloading decision. This paper comprehensively considers execution time and energy consumption of three execution units, and formulates task offloading decision as a single-objective optimization problem. Genetic algorithm with elitism preservation and random strategy is adopted to obtain optimal solution of the problem. At last, simulation experiments show that the proposed computation offloading model has lower fitness value compared with other computation offloading models.

Computation offloading model for smart factory

Internet of Things (IoT) is coming up in a rapid pace in various application domains. In smart factories, IoT can be deployed using sensors and actuators for taking smart manufacturing decisions. To maintain the smartness, huge computational power is required to handle the generated data by the IoT sensors. Local servers, in smart factories, usually organize the sensors/actuators and takes decisions at the local level. However, they are not equipped with enough computational power to handle all types of computational tasks and therefore some tasks need to be offloaded to the upper layer such as Cloud. Hybrid cloud i.e. public cloud along with some local servers can better handle this requirement. Recently, an additional layer called fog computing is introduced in the cloud architecture to complement it with added power. Offloading of tasks, generated by the industrial applications of IoT devices, should be done only when existing computational power of local server is not able to meet the quality requirements of the tasks. An ultimate objective of the smart factory owner is to earn revenue and for that, IoT devices need to meet their quality of service expectation. For offloading, tasks can be categorized as delay sensitive and delay tolerant and making decision on offloading by the local server is non-trivial. This work proposes an offloading decision model using game theory in a non-cooperative environment considering the categorization of tasks and it is shown that dominant strategy exists for the local server. For the performance study of the proposed model, simulation is done using iFogSim simulator. A comparative study with state-of-art exhibits that the proposed offloading scheme outperforms.

Joint Computation and Communication Cooperation for Energy-Efficient Mobile Edge Computing

This paper proposes a novel user cooperation approach in both computation and communication for mobile edge computing (MEC) systems to improve the energy efficiency for latency-constrained computation. We consider a basic three-node MEC system consisting of a user node, a helper node, and an access point (AP) node attached with an MEC server, in which the user has latency-constrained and computation-intensive tasks to be executed. We consider two different computation offloading models, namely, the partial and binary offloading, respectively. For partial offloading, the tasks at the user are divided into three parts that are executed at the user, helper, and AP, respectively; while for binary offloading, the tasks are executed as a whole only at one of three nodes. Under this setup, we focus on a particular time block and develop an efficient four-slot transmission protocol to enable the joint computation and communication cooperation . Besides the local task computing over the whole block, the user can offload some computation tasks to the helper in the first slot, and the helper cooperatively computes these tasks in the remaining time; while in the second and third slots, the helper works as a cooperative relay to help the user offload some other tasks to the AP for remote execution in the fourth slot. For both cases with partial and binary offloading, we jointly optimize the computation and communication resources allocation at both the user and the helper (i.e., the time and transmit power allocations for offloading, and the central process unit frequencies for computing), so as to minimize their total energy consumption while satisfying the user’s computation latency constraint. Although the two problems are nonconvex in general, we develop efficient algorithms to solve them optimally. Numerical results show that the proposed joint computation and communication cooperation approach significantly improves the computation capacity and energy efficiency at the user and helper, as compared to other benchmark schemes without such a joint design.

Resource allocation and computation offloading with data security for mobile edge computing

Abstract With the considerable growth of mobile users (MUs) and IoT devices, complex applications and multimedia services are rapidly increasing, thereby requiring additional computations and high data communication. However, these devices are still resource-constrained with limited computation power and energy. Furthermore, security is considered a critical issue for sensitive information communication. This study presents a multiuser resource allocation and computation offloading model with data security to address the limitations of such devices. First, the computation and radio resources are jointly considered for multiuser scenarios to guarantee the efficient utilization of shared resources. In addition, an AES cryptographic technique is introduced as a security layer to protect sensitive information from cyber-attacks. Furthermore, an integrated model, which jointly considers security, computation offloading, and resource allocation, is formulated to minimize time and energy consumption of the entire system. Finally, an offloading algorithm is developed with detailed processes to determine the optimal computation offloading decision for MUs. Simulation results show that our model and algorithm can significantly improve the performance of the entire system compared with local execution and full offloading schemes.

Smart-Edge-CoCaCo: AI-Enabled Smart Edge with Joint Computation, Caching, and Communication in Heterogeneous IoT

The development of mobile communication technology, hardware, distributed computing, and artificial intelligence (AI) technology has promoted the application of edge computing in the field of heterogeneous Internet of Things (IoT). In order to overcome the defects of the traditional cloud computing model in the era of big data. In this article, we first propose a new AIenabled smart edge with heterogeneous IoT architecture which combines edge computing, caching, and communication. Then, we propose the Smart-Edge-CoCaCo algorithm. To minimize total delay and confirm the computation offloading decision, Smart-Edge-CoCaCo uses joint optimization of the wireless communication model, the collaborative filter caching model in edge cloud, and the computation offloading model. Finally, we built an emotion interaction testbed to perform computational delay experiments in real environments. The experiment results showed that the computation delay of the Smart-Edge-CoCaCo algorithm is lower than that of traditional cloud computing model with the increase of computing task data and the number of concurrent users.

An Efficient Computation Offloading Management Scheme in the Densely Deployed Small Cell Networks With Mobile Edge Computing

To tackle the contradiction between the computation intensive applications and the resource-hungry mobile user equipments (UEs), mobile edge computing (MEC) has been provisioned as a promising solution, which enables the UEs to offload the tasks to the MEC servers. Considering the characteristics of small cell networks (SCNs), integrating MEC into SCNs is natural. But in terms of the high interference, multi-access property, and limited resources of small cell base stations (SBSs), an efficient computation offloading scheme is essential. However, there still lack comprehensive studies on this problem in the densely deployed SCNs. In this paper, we study the energy-efficient computation offloading management scheme in the MEC system with SCNs. The aim of this paper is to minimize the energy consumption of all UEs via jointly optimizing computation offloading decision making, spectrum, power, and computation resource allocation. Specially, the UEs need not only to decide whether to offload but also to determine where to offload. First, we present the computation offloading model and formulate this problem as a mix integer non-linear programming problem, which is NP-hard. Taking advantages of genetic algorithm (GA) and particle swarm optimization (PSO), we design a suboptimal algorithm named as hierarchical GA and PSO-based computation algorithm to solve this problem. Finally, the convergence of this algorithm is studied by simulation, and the performance of the proposed algorithm is verified by comparing with the other baseline algorithms.