Game-Theory-Based Task Offloading and Resource Scheduling in Cloud-Edge Collaborative Systems

Today, with the presence of 5G communication systems, including Internet of Things (IoT) technology, there is a high demand for mobile devices (especially smartphones, tablets, wearable technology, and so on). Regarding this proliferation and high demand, the massive adoption of mobile devices (MDs) has led to an exponential increase in network latency; the heavy demand for cloud servers causes the degradation of data traffic, which considerably impacts the real-time communication and computing aspects of mobile devices. As a result, mobile edge computing (MEC), an efficient framework capable of enhancing processing, optimizing energy usage, and offloading computation tasks, is considered a promising solution. In current research, numerous models have been implemented to achieve resource allocation and task offloading. However, these techniques are ineffective due to privacy issues and a lack of sufficient resources. Hence, this study proposes secure task offloading and resource allocation strategies in mobile devices using the Probit Mish–Gated Recurrent Unit (PM-GRU) and Entropic Linear Interpolation-Serval Optimization Algorithm (ELI-SOA). Primarily, the tasks to be offloaded and their attributes are gathered from mobile users and passed to a local computing model to identify the edge server. Here, the task attributes and the server attributes are compared with a cache table using the Sorensen–Dice coefficient. If the attributes match, then details about the appropriate edge server are produced. If the attributes do not match, then they are inputted into a global scheme that analyzes the attributes and predicts the edge server based on the Probit Mish-Gated Recurrent Unit (PM-GRU). Then, the server information is preserved and updated in the cache table in the local scheme. Further, the attributes, along with the predicted edge server, are inputted into a system for privacy-preserving smart contract creation by using Exponential Earth Mover’s Distance Matrix-Based K-Anonymity (EEMDM-KA) to develop a secure smart contract. Subsequently, the traffic attributes in the smart contract are extracted, and the request load is balanced by using HCD-KM. Load-balanced requests are assigned to the edge server, and the optimal resources are allocated in the cloud server by using the Entropic Linear Interpolation-Serval Optimization Algorithm (ELI-SOA). Finally, the created smart contract is hashed based on KECCAK-512 and stored in the blockchain. With a high accuracy of 99.84%, the evaluation results showed that the proposed approach framework performed better than those used in previous efforts.

Federated deep reinforcement learning for task offloading and resource allocation in mobile edge computing-assisted vehicular networks

With the ongoing advancement of electric power Internet of Things (IoT), traditional power inspection methods face challenges such as low efficiency and high risk. Unmanned aerial vehicles (UAVs) have emerged as a more efficient solution for inspecting power facilities due to their high maneuverability, excellent line-of-sight communication capabilities, and strong adaptability. However, UAVs typically grapple with limited computational power and energy resources, which constrain their effectiveness in handling computationally intensive and latency-sensitive inspection tasks. In response to this issue, we propose a UAV task offloading strategy based on deep reinforcement learning (DRL), which is designed for power inspection scenarios consisting of mobile edge computing (MEC) servers and multiple UAVs. Firstly, we propose an innovative UAV-Edge server collaborative computing architecture to fully exploit the mobility of UAVs and the high-performance computing capabilities of MEC servers. Secondly, we established a computational model concerning energy consumption and task processing latency in the UAV power inspection system, enhancing our understanding of the trade-offs involved in UAV offloading strategies. Finally, we formalize the task offloading problem as a multi-objective optimization issue and simultaneously model it as a Markov Decision Process (MDP). Subsequently, we proposed a task offloading algorithm based on a Deep Deterministic Policy Gradient (OTDDPG) to obtain the optimal task offloading strategy for UAVs. The simulation results demonstrated that this approach outperforms baseline methods with significant improvements in task processing latency and energy consumption.

The task offloading and resource allocation strategies aim to provide efficient edge computing services for ground mobile devices in mobile edge computing (MEC) environments. In this paper, we propose a UAV-assisted dynamic deployment and decision-making framework (dDDM) to address the issue of insufficient base station coverage in remote areas or emergency scenarios, which leverages the flexibility and computational capabilities of unmanned aerial vehicle (UAV) to supplement terrestrial cellular networks. We focus on optimizing the UAV trajectory to support and enhance the effectiveness of task offloading and resource allocation strategies under conditions of user mobility and stochastic task arrivals. Therefore, a joint optimization problem is formulated with the objective of minimizing the weighted sum of the total latency of ground mobile devices and the overall energy consumption of UAV. To solve this problem, the original optimization problem is decomposed into two subproblems: UAV trajectory planning, and decision-making for task offloading & computing resource allocation. For the UAV trajectory planning subproblem, we propose an improved particle swarm optimization(PSO) algorithm integrated with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i>-means clustering, i.e., KMPSO algorithm, enabling dynamic UAV deployment in response to user mobility. For the task offloading and resource allocation decision-making, an improved black-winged kite algorithm (IBKA) is developed to determine optimal offloading and allocation strategies. Simulation results demonstrate that the proposed approach outperforms existing benchmark algorithms in terms of both latency and energy consumption.

The traditional cloud computing model can no longer satisfy the current demand due to the limitations of backhaul bandwidth and high latency. Therefore, a new fog computing architecture is proposed, which relieves the bandwidth load and energy pressure on the backhaul link by reducing the number of communications between the cloud computing center and the users. The latency is drastically reduced by proximity to the devices. However, the performance of fog computing is highly dependent on a variety of resource allocation strategies. Therefore, task offloading strategies and resource allocation strategies are a great challenge. In this paper, we use the advantage actor-critic (A2C) algorithm in Deep reinforcement learning (DRL) to jointly optimize the offloading strategy, and network resource allocation strategy to reduce latency for dependent computational tasks in fog computing. One of the major challenges of such problems is that there are multiple action dimensions, which makes it difficult to converge the network. Therefore, this paper uses the multi-agent method to simplify the problem by splitting the complete offload decision action into three sub-actions. We demonstrate through numerical simulations that the algorithm can effectively reduce the cost and also discuss the effects of the different number of devices and the different number of fog nodes on the cost.

To alleviate communication pressure and terminal resource constraints in mobile edge computing (MEC) networks, this paper proposes a resource allocation optimization method for MEC systems that integrates data compression technology and non-orthogonal multiple access technology. This method considers practical constraints such as terminal device battery capacity and computational resource limitations. By jointly optimizing computational resource allocation, task offloading strategies, and data compression ratios, it constructs an optimization model aimed at minimizing the total task processing latency. Addressing the challenges stemming from the non-convex nature of the problem and the dynamic variations in channel conditions and task requirements, this paper proposes a softmax deep double deterministic policy gradient algorithm, where softmax operator function mitigates both overestimation and underestimation biases inherent in traditional reinforcement learning frameworks, enhancing convergence performance. Utilizing a deep reinforcement learning framework, the algorithm achieves joint decision-making optimization for computational resources, task offloading, and compression ratios, thereby minimizing the total task processing latency while satisfying transmit power and computational resource constraints. Simulation results demonstrate that the proposed scheme exhibits significant advantages over benchmark algorithms in terms of convergence speed and task processing latency.

In medical vehicular networks, medical vehicles can serve as efficient mobile medical service points to provide necessary and critical medical services for patients while in motion. The delay requirement is very vital for medical services to guarantee service quality and save the lives of patients. Mobile Edge Computing (MEC), as an emerging network paradigm, enables the computation extensive tasks to be offloaded to edge servers, efficiently reducing the delay and bandwidth demands. MEC technology is a promising solution to provide high-quality medical services for users in medical vehicular networks. However, task offloading and resource allocation incurs additional service delay and energy consumption, affecting the overall service performance and Quality of Experience (QoE) of users. Thus, realizing the optimal task offloading and resource allocation in MEC-enabled medical vehicular networks, to reduce task completion time and energy consumption, becomes a potential challenge. To address the challenge, we investigate the joint task offloading and resource allocation problem in MEC-enabled medical vehicular networks to improve the QoE of users. Considering the resource requirements and QoS constraint, we formulate a multi-objective optimization model, with the target of average task completion time and average energy consumption minimization. On this basis, we propose a MOEAD-based task offloading and resource allocation (IMO) algorithm to solve it. Furthermore, in order to obtain the optimal solution and speed up the algorithm convergence, we design a greedy strategy-based population initialization algorithm. The extensive simulations demonstrate that compared to existing algorithms, our proposed IMO algorithm can obtain a smaller average completion time, and achieve better tradeoff between task completion time and energy consumption.

Digital Twin (DT) and mobile edge computing are two promising solutions for achieving latency-sensitive and computing-intensive applications in Industrial Internet of Things (IIoT). However, existing task offloading schemes with DT empowerment are faced with challenges, such as the spatio-temporal heterogeneity of edge server (ES) resources, resource-constrained ESs, and the explosive growth of data in emerging applications. This paper investigates the issues of task offloading and resource allocation under the assistance of DT and multiple ESs parallel collaboration. One novel scheme, abbreviated as Mes-PCORA, is proposed. With comprehensive information within the digital space, the Mes-PCORA scheme dynamically adjusts task allocation ratios across multiple ESs to achieve collaborative task offloading. The offloading request is formulated as a non-convex problem. To make the non-convex problem solvable in polynomial time, the original problem transformed into a bilevel optimization problem. Then, a bilevel iterative optimization approach is proposed. Specifically, the upper-level optimization problem is formulated as a multi-agent Markov Decision Process, and a deep reinforcement learning-based resource allocation algorithm is designed to solve it. Subsequently, for the lower-level optimization problem, it is solved by the interior point method. Numerical results demonstrate that the proposed scheme reduces the average task completion latency by 27.16%–63.44% and decreases the task offloading failure rate by 35.83%–73.95%, compared to state-of-the-art baselines.

With the increasing number of tasks in vehicular networks year by year, optimizing task offloading has become critical to improving system performance and resource utilization efficiency. However, due to the dynamic nature of network topology, the high mobility of vehicular devices, and the multi-objective demands of task offloading, designing effective offloading strategies poses significant challenges. To address these issues and achieve multi-objective optimization, this study proposes a task offloading strategy optimization method that combines reinforcement learning and evolutionary algorithms, referred to as PPO-enhanced NSGA-III Offloading Strategy (PONS). This method leverages the PPO reinforcement learning algorithm to dynamically adjust the solution set distribution during the NSGA-III offloading process, thereby enhancing the algorithm’s exploration capability and adaptability in complex environments. Specifically, PONS learns to generate balanced Pareto-optimal solution sets by optimizing communication, computational resource allocation, and task scheduling strategies between vehicles and servers under various network conditions, thereby meeting multiple optimization objectives. Experimental results based on real-world datasets, including the Shanghai Taxi Trajectory and Telecom Base Station datasets, demonstrate that PONS achieves significant improvements in both task delay and energy consumption compared to benchmark algorithms. These results confirm the effectiveness and superiority of PONS in optimizing task offloading strategies within vehicular network environments.

Innovative soft computing-enabled cloud optimization for next-generation IoT in digital twins

With the advent of the 5G era, the demands for features such as low latency and high concurrency are becoming increasingly significant. These sophisticated new network applications and services require huge gaps in network transmission bandwidth, network transmission latency, and user experience, making cloud computing face many technical challenges in terms of applicability. In response to cloud computing's shortcomings, edge computing has come into its own. However, many factors affect task offloading and resource allocation in the edge computing environment, such as the task offload latency, energy consumption, smart device mobility, end-user power, and other issues. This paper proposes a dynamic multi-winner game model based on incomplete information to solve multi-end users' task offloading and edge resource allocation. First, based on the history of end-users storage in edge data centers, a hidden Markov model can predict other end-users' bid prices at time t. Based on these predicted auction prices, the model determines their bids. A dynamic multi-winner game model is used to solve the offload strategy that minimizes latency, energy consumption, cost, and to maximizes end-user satisfaction at the edge data center. Finally, the authors designed a resource allocation algorithm based on different priorities and task types to implement resource allocation in edge data centers. To ensure the prediction model's accuracy, the authors also use the expectation-maximization algorithm to learn the model parameters. Comparative experimental results show that the proposed model can better results in time delay, energy consumption, and cost.

With the development of artificial intelligence technology and intelligent devices, people show great interest in intelligent applications and services, but it is impossible to complete these compute-intensive AI tasks locally, especially video analysis tasks. Edge computing is regarded as an appropriate solution to these problems. In this paper, we study the multi-user multi-server edge-end collaboration video analytics task offloading problem aiming at minimizing the overall delay for each device to finish its task. Each device chooses whether to execute the task locally or to offload the task to an edge server, and which edge server to select. At the theoretical level, we model the joint problem of task offloading and resource allocation as a mixed integer programming problem. We first determine the optimal resource allocation policy with a given task offloading decision profile. Then, task offloading problem is modeled as a congestion game and propose a decentralized mechanism to achieve a Nash equilibrium. Moreover, experimental results demonstrate that the proposed method is efficient and can significantly and steadily improve the system performance, reducing the overall delay by 33.96% on average, compared with other algorithms.

With the rapid advancement of the Internet of Things (IoT), the typical application of wireless body area networks (WBANs) based smart healthcare has drawn wide attention from all sectors of society. To alleviate the pressing challenges, such as resource limitations, low-latency service provision, mass data processing, rigid security demands, and the lack of a central entity, the advanced solutions of fog computing, software-defined networking (SDN) and blockchain are leveraged in this work. On the basis of these solutions, a task offloading strategy with a centralized low-latency, secure and reliable decision-making algorithm having powerful emergency handling capacity (LSRDM-EH) is designed to facilitate the resource-constrained edge devices for task offloading. Additionally, to well ensure the security of the entire network, a comprehensive blockchain-based two-layer and multidimensional security strategy is proposed. Furthermore, to tackle the inherent time-inefficiency problem of blockchain, we propose a blockchain sharding scheme to reduce system time latency. Extensive simulation has been conducted to validate the performance of the proposed measures, and numerical results verify the superiority of our methods with lower time-latency, higher reliability and security.

In this paper, the task offloading and resource allocation problem for the Internet of Vehicles (IoV) is investigated. In our considered offloading scheme, a Bayesian classifier is first adopted to classify the task according to its different requirements in latency and energy consumption. Based on the classification results, each vehicle user equipment (VUE) then selects the corresponding offloading mode. More specifically, if the VUE has higher requirements for energy consumption, the task will be carried out at other vehicles through the vehicle to vehicle (V2V) offloading mode. Otherwise, it will choose to offload the task through mobile edge computing (MEC) offloading mode. To achieve a trade-off between latency requirement and energy consumption in the task executing process through offloading decision, we formulate the offloading and resource allocation scheme as a mixed-integer non-linear problem. To achieve an approximate solution, a Q-learning based solution is proposed. Simulation results demonstrate that the proposed scheme has better performance in terms of higher system throughput, lower latency, and lower energy consumption compared with the existing schemes.

With the increased penetration of cloud computing and virtualization, a plethora of internet of things devices have been deployed globally. As a result, computationally intensive tasks are transmitted from the edge towards the centralized cloud for processing that leads to increased energy utilization in the cloud data centers while at the same increasing significant latency for critical applications. Recent years have witnessed a paradigm shift from centralized cloud computing towards mobile edge computing (MEC), where computational tasks are offloaded at the edge servers near user equipment (UE). This paradigm leads to lowering the energy utilization in the cloud data centers, along with low latency for UE and efficient resource utilization at the edge. In this context, the scale and complexity of the MEC networks is drastically increasing and, consequently, finding effective energy-efficient solutions for computational task offloading and resource allocation in MEC networks has become an ambitious task. To address the aforementioned challenges, this work surveys the state of the art in different categorizations of algorithm-based computational task offloading and resource allocation strategies focusing on energy utilization. It also provides a detailed cross-comparison of existing strategies in terms of their implementation specifications. Additionally, this paper also highlights open challenges and potential future research directions to facilitate efficient task offloading and resource allocation at the edge with reduced energy consumption at the centralized data centers. Our work also paves the way for the deployment of critical applications at the edge that require low latency and high service quality guarantees.