Optimal Offloading Strategy Research Articles

Computation Offloading with Privacy-Preserving in Multi-Access Edge Computing: A Multi-Agent Deep Reinforcement Learning Approach

The rapid development of mobile communication technologies and Internet of Things (IoT) devices has introduced new challenges for multi-access edge computing (MEC). A key issue is how to efficiently manage MEC resources and determine the optimal offloading strategy between edge servers and user devices, while also protecting user privacy and thereby improving the Quality of Service (QoS). To address this issue, this paper investigates a privacy-preserving computation offloading scheme, designed to maximize QoS by comprehensively considering privacy protection, delay, energy consumption, and the task discard rate of user devices. We first formalize the privacy issue by introducing the concept of privacy entropy. Then, based on quantified indicators, a multi-objective optimization problem is established. To find an optimal solution to this problem, this paper proposes a computation offloading algorithm based on the Twin delayed deep deterministic policy gradient (TD3-SN-PER), which integrates clipped double-Q learning, prioritized experience replay, and state normalization techniques. Finally, the proposed method is evaluated through simulation analysis. The experimental results demonstrate that our approach can effectively balance multiple performance metrics to achieve optimal QoS.

Edge computing collaborative offloading strategy for space‐air‐ground integrated networks

SummaryDue to geographical factors, it is impossible to build large‐scale communication network infrastructure in remote areas, which leads to poor network communication quality in these areas and a series of delay‐sensitive tasks cannot be timely processed and responded. Aiming at the problem of limited coverage in remote areas, the space‐air‐ground integrated networks (SAGIN) combined with mobile edge computing (MEC) can provide low latency and high reliability transmission for offloading delay‐sensitive tasks for users in remote areas. Considering the strong limitation of satellite resources in the space‐ground integrated network and insufficient energy of local user equipment, firstly, a satellite‐UAV cluster‐ground three‐layer edge computing network architecture is proposed in this paper. Under the condition that the delay requirements of various ground tasks are met, the task offloading problem is transformed into a Stackelberg game between ground user equipment and edge servers. In addition, it is proved that the existence of Nash equilibrium in non‐cooperative game between ground user equipment by using potential game. Finally, a Nash equilibrium iterative offloading algorithm based on Stackelberg game (NEIO‐SG) is proposed to find the optimal offloading strategy for tasks to minimize the system offloading cost and the optimal forwarding percentage strategy for offloading tasks to maximize the utility function of the edge server. Simulation results show that compared to other baseline algorithms, NEIO‐SG reduces the total system latency during task offloading by about 13 and the energy consumption of the edge server by about 35.

Research on Decomposition and Offloading Strategies for Complex Divisible Computing Tasks in Computing Power Networks

With the continuous emergence of intelligent network applications and complex tasks for mobile terminals, the traditional single computing model often fails to meet the greater requirements of computing and network technology, thus promoting the formation of a new computing power network architecture, of ‘cloud, edge and end’ three-level heterogeneous computing. For complex divisible computing tasks in the network, task decomposition and offloading help to realize a distributed execution of tasks, thus reducing the overall running time and improving the utilization of fragmented resources in the network. However, in the process of task decomposition and offloading, there are problems, such as there only being a single method of task decomposition; that too large or too small decomposition granularity will lead to an increase in transmission delay; and the pursuit of low-delay and low-energy offloading requirements. Based on this, a complex divisible computing task decomposition and offloading scheme is proposed. Firstly, the computational task is decomposed into multiple task elements based on code partitioning, and then a density-peak-clustering algorithm with an improved adaptive truncation distance and clustering center (ATDCC-DPC) is proposed to cluster the task elements into subtasks based on the task elements themselves and the dependencies between the task elements. Secondly, taking the subtasks as the offloading objects, the improved Double Deep Q-Network subtask offloading algorithm (ISO-DDQN) is proposed to find the optimal offloading scheme that minimizes the delay and energy consumption. Finally, the proposed algorithms are verified by simulation experiments, and the scheme in this paper can effectively reduce the task delay and energy consumption and improve the service experience.

Blockchain-based 6G task offloading and cooperative computing resource allocation study

In the upcoming era of 6G, the accelerated development of the Internet of Everything and high-speed communication is poised to provide people with an efficient and intelligent life experience. However, the exponential growth in data traffic is expected to pose substantial task processing challenges. Relying solely on the computational resources of individual devices may struggle to meet the demand for low latency. Additionally, the lack of trust between different devices poses a limitation to the development of 6G networks. In response to this issue, this study proposes a blockchain-based 6G task offloading and collaborative computational resource allocation (CERMTOB) algorithm. The proposed first designs a blockchain-based 6G cloud-network-edge collaborative task offloading model. It incorporates a blockchain network on the edge layer to improve trust between terminals and blockchain nodes. Subsequently, the optimization objective is established to minimize the total latency of offloading, computation, and blockchain consensus. The optimal offloading scheme is determined using the wolf fish collaborative search algorithm(WF-CSA) to minimize the total delay. Simulation results show that the WF-CSA algorithm significantly reduces the total delay by up to 42.58% compared to the fish swarm algorithm, wolf pack algorithm and binary particle swarm optimisation algorithm. Furthermore, the introduction of blockchain to the cloud-side-end offloading system improves the communication success rate by a maximum of 14.93% compared to the blockchain-free system.

An offloading and pricing mechanism based on virtualization in edge–cloud computing

Integrate users, edge servers and cloud into a system can improve resource utilization and quality of service (QoS). The users can send the same request to multiple Edge Servers. However, edge servers have limited scope, which may result in repeated offloading tasks for the same user. In this paper, we consider the cloud resource migration in the form of virtual machines (VMs) to achieve high computing efficiency and low latency requirements. Obviously, the migration of VMs causes changes in the number and price of edge resources, which further affects the task offloading strategy. Therefore, it is necessary to optimize the VMs migration strategy and resource pricing strategy successively to achieve the optimal offloading strategy suitable for users. First, based on the competition between edge servers, a static VMs migration algorithm (SVMMA) is designed to formulate the strategy and price of VMs migration. Then, according to the users’ resource needs and the resource possession of different edge servers, a resource partitioning approach (RPA) have been proposed. Finally, two different QoS functions are evaluated, representing the QoS matching degree between tasks and multiple resource combination blocks (MRCBs). Wherein, a dynamic matching and pricing of tasks algorithm (DMPT) is conducted to obtain the optimal offloading strategy and price of the task. Experiments show that our algorithms can improve the offloading rate of tasks and achieve the relative balance among user, cloud server, and edge server benefits.

Online task offloading algorithm based on multi-objective optimization caching strategy

Within the realm of Mobile Edge Computing (MEC), task offloading has consistently garnered significant attention. Within the context of intricate caching environments and multi-user scenarios, conventional solutions frequently encounter difficulties in simultaneously fulfilling the demands for latency reduction and energy consumption optimization. This paper presents a novel online task offloading algorithm that leverages a multi-objective optimization caching strategy. This algorithm addresses two challenges: the Online Task Offloading (OTO) problem and the Online Task File Caching (OTFC) problem. The OTO problem is conceptualized as a multi-user game, where Nash equilibrium is employed to effectively characterize and address it. This ensures the determination of the optimal offloading strategy in the presence of various caching scenarios. Meanwhile, the OTFC problem is transformed into a Markov decision process, and through the utilization of Deep Q-Networks, we can forecast the requirements of online tasks and subsequently determine the optimal caching vector. The incorporation of the Multi-Objective Cache Policy (MOCP) algorithm precedes the finalization of the caching vector. Rooted in multi-objective optimization, this algorithm adeptly balances various caching decisions, achieving a Pareto optimal outcome. The proposed offloading model that effectively caters to the requirements of task offloading while incorporating the demands of task file caching. Moreover, the MOCP algorithm ensures optimal caching decisions across a broad range of scenarios. Simulation tests reveal that this enhanced offloading algorithm, grounded in multi-objective optimization, outperforms traditional methods in energy conservation, boasting energy savings of up to 15%.

Deep Meta Q-Learning Based Multi-Task Offloading in Edge-Cloud Systems

Resource-constrained edge devices can not efficiently handle the explosive growth of mobile data and the increasing computational demand of modern-day user applications. Task offloading allows the migration of complex tasks from user devices to the remote edge-cloud servers thereby reducing their computational burden and energy consumption while also improving the efficiency of task processing. However, obtaining the optimal offloading strategy in a multi-task offloading decision-making process is an NP-hard problem. Existing Deep learning techniques with slow learning rates and weak adaptability are not suitable for dynamic multi-user scenarios. In this article, we propose a novel deep meta-reinforcement learning-based approach to the multi-task offloading problem using a combination of first-order meta-learning and deep Q-learning methods. We establish the meta-generalization bounds for the proposed algorithm and demonstrate that it can reduce the time and energy consumption of IoT applications by up to 15%. Through rigorous simulations, we show that our method achieves near-optimal offloading solutions while also being able to adapt to dynamic edge-cloud environments.

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 novel blockchain enabled resource allocation and task offloading strategy in cloud computing environment

Large amounts of processing resources are required for the sensed raw big data processing during the data generation process. Furthermore, as sensed data are typically privacy sensitive, blockchain technology can be used to ensure the privacy concerns. This study examines a multiuser mobile offloading network that consists of a cloud server located remotely and an edge node. We formulate the offloading problem as the joint optimization of task offloading decision making of all users, the computation resource allocation among the edge executing applications, and the radio resource assignment among all the remote-processing applications. The goal is to minimize the maximum weighted cost of all users. When compared to other benchmark approaches, the simulation results show that the proposed algorithm achieves optimal results in terms of both energy consumption and delay as a result of collaboration. Finally the resource allocation and optimal offloading strategy with 93% efficiency is obtained.

Edge computing offloading strategy for space-air-ground integrated network based on game theory

The limited coverage of terrestrial networks makes it difficult to provide low-latency and high-reliable computing services for users and Internet of Thing (IoT) devices in remote areas such as mountainous regions and oceans. Space-Air-Ground Integrated Network (SAGIN) combined with Mobile Edge Computing (MEC) can provide seamless three-dimensional services for users by deploying edge servers on satellites, Unmanned Aerial Vehicles (UAVs) and ground infrastructures. However, due to the limited heterogeneous computing resources in satellites-UAV clusters network and the energy resources of IoT devices, it brings a significant challenge in determining how to offload the computing tasks generated by ground user devices to satellite edge nodes, UAV edge nodes or locally for processing. In this paper, we first propose a satellites-UAV clusters-ground three-layer edge computing network architecture consisting of a global controller, inter-domain controllers and MEC servers. We then model the task offloading problem as a Binary Integer Linear Programming (BILP) aiming at minimizing the offloading cost composed of delay and energy consumption and prove it is NP-hard. Next, the original offloading problem is transformed to a noncooperative strategic game and the existence of Nash equilibrium is proven using potential games. Finally, we propose a Nash Equilibrium Iteration Offloading algorithm based on Game theory (NEIO-G) to find the optimal offloading strategy. Compared with other baseline algorithms, simulation results demonstrate that the NEIO-G can significantly reduce the system offloading overhead in terms of delay and energy consumption.

Location Privacy-Aware Task Offloading in Mobile Edge Computing

In mobile edge computing (MEC), users can offload tasks to nearby MEC servers to reduce computation cost. Considering that the size of offloaded tasks could disclose user location information, several location privacy-preserving task offloading mechanisms have been proposed under the single-server scenario. However, to the best of our knowledge, none of them could provide a strict privacy protection guarantee or be applicable to the multi-server scenario where the user's location can be inferred more accurately if servers collude with each other. In this paper, we propose a novel location privacy-aware task offloading framework (LPA-Offload) for both single-server and multi-server scenarios, which provides strict and provable location privacy protection while achieving efficient task offloading. Specifically, we propose a location perturbation mechanism that allows each user to perturb its real location within a rational perturbation region and provides a differential privacy guarantee. To make a satisfactory offloading strategy, we propose a perturbation region determination mechanism and an offloading strategy generation mechanism that adaptively select a proper perturbation region according to the customized privacy factor, and then generate an optimal offloading strategy based on the perturbed location within the decided region. The determination of the perturbation region could achieve personalized privacy requirements while reducing computation cost. LPA-Offload is proved to satisfy <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$(\epsilon ,\delta )$</tex-math></inline-formula> -differential privacy, and the experiments demonstrate the effectiveness of our framework.

Edge Computation Offloading With Content Caching in 6G-Enabled IoV

Using the powerful communication capability of 6G, various in-vehicle services in the Internet of Vehicles (IoV) can be offered with low delay, which provide users with a high-quality driving experience. Edge computing in 6G-enabled IoV utilizes edge servers distributed at the edge of the road, enabling rapid responses to delay-sensitive tasks. However, how to execute computation offloading effectively in 6G-enabled IoV remains a challenge. In this paper, a Computation Offloading method with Demand prediction and Reinforcement learning, named CODR, is proposed. First, a prediction method based on Spatial-Temporal Graph Neural Network (STGNN) is proposed. According to the predicted demand, a caching decision method based on the simplex algorithm is designed. Then, a computation offloading method based on twin delayed deterministic policy gradient (TD3) is proposed to obtain the optimal offloading scheme. Finally, the effectiveness and superiority of CODR in reducing delay are demonstrated through a large number of simulation experiments.

Load balance -aware dynamic cloud-edge-end collaborative offloading strategy.

Cloud-edge-end (CEE) computing is a hybrid computing paradigm that converges the principles of edge and cloud computing. In the design of CEE systems, a crucial challenge is to develop efficient offloading strategies to achieve the collaboration of edge and cloud offloading. Although CEE offloading problems have been widely studied under various backgrounds and methodologies, load balance, which is an indispensable scheme in CEE systems to ensure the full utilization of edge resources, is still a factor that has not yet been accounted for. To fill this research gap, we are devoted to developing a dynamic load balance -aware CEE offloading strategy. First, we propose a load evolution model to characterize the influences of offloading strategies on the system load dynamics and, on this basis, establish a latency model as a performance metric of different offloading strategies. Then, we formulate an optimal control model to seek the optimal offloading strategy that minimizes the latency. Second, we analyze the feasibility of typical optimal control numerical methods in solving our proposed model, and develop a numerical method based on the framework of genetic algorithm. Third, through a series of numerical experiments, we verify our proposed method. Results show that our method is effective.

P4toNFV: Offloading from P4 switches to NFV in programmable data planes

SummaryP4 combines the benefits of hardware‐based networking with the adaptability of software‐based network operations. However, when faced with intricate network functions, P4 switches reveal constraints in memory and processing primitives. To address these, we advocate offloading traffic demanding intricate processing from the programmable data plane to network function virtualization (NFV). By leveraging this approach, P4 switches handle the core data plane, ensuring maximum performance, whereas virtualized network functions (VNF) cater to the intricate processing. Central to our research is the optimization of this offloading process, specifically considering delay constraints. We developed an analytical model that examines a P4 switch overseen by an SDN controller, integrating an offloading capability to NFV. The principal objective was to determine an offloading rate that minimizes packet processing delay. To this end, we employed a Bounded method, an advancement from Brent's method, to determine this optimal rate. The findings indicate that offloading approximately 66% of packets to the VNF achieves the lowest total delay, registering at 0.1505 s. This strategy of optimal offloading can notably reduce the system's average delay as the demand for network functions increases. The optimization technique we adopted exhibited rapid convergence in our experiments, reflecting the method's efficacy. Furthermore, a rigorous parametric sensitivity analysis spanning no offloading, full offloading, and optimal offloading strategies underscores that optimal offloading to NFV consistently augments system performance, particularly in terms of delay reduction. Conclusively, our study furnishes valuable insights into offloading strategies, augmenting the narrative on resource allocation in both PNFs and VNFs.

Multiagent Reinforcement Learning-Based Orbital Edge Offloading in SAGIN Supporting Internet of Remote Things

We investigate a computing task scheduling problem in space-air-ground integrated network (SAGIN) for Internet of remote Things (IoRT). In the considered scenario, the unmanned aerial vehicles (UAVs) collect computing tasks from IoRT devices and make offloading decisions, in which the tasks can be computed at the UAVs or offloaded to the low earth orbital (LEO) satellite. The optimization objective is to design offloading strategies for each UAV that maximum the number of tasks satisfies delay constraint and reduces the energy consumption of UAVs. We formulate this problem as a nonlinear integer optimization problem, and remodel it as a stochastic game to solve it. To copy with the dynamic and complexity environment, we propose a learning-based orbital edge offloading (LOEF) approach which can coordinate all UAVs to learn the optimal offloading strategies. This method is based on the Actor-Critic framework of multi-agent reinforcement learning, the Actor observes the environment and output the current offloading decision, the Critic evaluates the action of the Actor and coordinate the actions of all UAV to increase the reward of system. The simulation results show that the proposed offloading strategy converges fast, increases the number of satisfying the delay constraints tasks and the energy utilization of UAVs compared with the baseline strategies.

Dynamic Edge Computation Offloading for Internet of Vehicles With Deep Reinforcement Learning

Recent developments in the Internet of Vehicles (IoV) enabled the myriad emergence of a plethora of data-intensive and latency-sensitive vehicular applications, posing significant difficulties to traditional cloud computing. Vehicular edge computing (VEC), as an emerging paradigm, enables the vehicles to utilize the resources of the edge servers to reduce the data transfer burden and computing stress. Although the utilization of VEC is a favourable support for IoV applications, vehicle mobility and other factors further complicate the challenge of designing and implementing such systems, leading to incremental delay and energy consumption. In recent times, there have been attempts to integrate deep reinforcement learning (DRL) approaches with IoV-based systems, to facilitate real-time decision-making and prediction. We demonstrate the potential of such an approach in this paper. Specifically, the dynamic computation offloading problem is constructed as a Markov decision process (MDP). Then, the twin delayed deep deterministic policy gradient (TD3) algorithm is utilized to achieve the optimal offloading strategy. Finally, findings from the simulation demonstrate the potential of our proposed approach.

Minimizing energy consumption of IoT devices for O-RAN based IoT systems

The implementation of Open Radio Access Network (O-RAN) architecture in Internet of Things (IoT) systems has garnered significant attention as a means to fulfill the stringent requirements of ultra-low latency and ultra-low energy consumption in future IoT systems. Although the traditional edge network architecture has been extensively employed, it continues to pose challenges in terms of synchronizing the integration of global and local information for the design of an optimal offloading strategy in edge servers, thus hindering the reduction of latency and energy consumption in IoT devices. In an effort to decrease the latency and energy consumption of IoT devices (IoTDs), we propose a computation offloading problem and employs the Successive Convex Approximation (SCA) algorithm to convert the non-convex problem into a convex problem. The proposed strategy aims to minimize the energy consumption of IoTDs while ensuring Quality of Service (QoS) requirements. The results of the experiment demonstrate that the average energy consumption of terminal devices can be reduced by 20%. Additionally, this strategy is found to be more effective in reducing IoTDs' latency and energy consumption as compared to the traditional edge network architecture.

Privacy-preserving offloading scheme in multi-access mobile edge computing based on MADRL

With the development of industrialization and intelligence, the Industrial Internet of Things (IIoT) has gradually become the direction for traditional industries to transform into modern ones. In order to adapt to the emergence of a large number of edge access devices such as sensors, as well as the demand for high-consumption and low-latency computing tasks, Mobile Edge Computing (MEC) has been proposed as an effective paradigm by the academic community. Users can offload tasks to MEC servers, greatly reducing the computing latency and energy consumption when using services. However, traditional single access point edge computing networks cannot meet the usage requirements of a large number of users. In addition, the privacy leakage issues arising from the offloading process are also easily overlooked. In this paper, we propose a privacy-preserving offloading scheme based on stochastic game theory considering multiple access points. We first construct a multi-access point offloading framework and quality of service (QoS) model. In terms of privacy, the privacy risks caused by the offloading preferences of different edge nodes are studied, and the privacy entropy is used to evaluate the privacy protection level. We comprehensively consider the energy consumption, latency requirements, user experience, and privacy protection of the system and formulate the problem as a Markov Decision Process (MDP). Finally, a joint optimal DRL algorithm with privacy preservation (JODRL-PP) is proposed to achieve the optimal offloading scheme of the system. Simulation results verify the effectiveness of our model and algorithm.

Joint Power Control and Task Offloading in Collaborative Edge–Cloud Computing Networks

Mobile edge computing arises as a promising technology to allow mobile devices to offload delay-sensitive and computation-intensive tasks to the nearby edge servers. However, overloaded tasks from mobile devices (MDs) lead to a large latency due to limited computing and channel resources. To mitigate this situation, a collaborative edge-cloud computing network is considered in this paper. Based on power control and task offloading, we formulate a mixed-integer nonlinear programming (MINLP) problem to minimize the weighted sum of the energy consumption and the latency. This NP-hard problem is decomposed into a real variable problem and an integer linear programming problem. By leveraging the proposed extremevalue descent (EVD) method, the optimal power strategy is obtained by solving the real variable problem. The simplex method and branch-and-bound method are adopted to find the optimal offloading strategy. Although these two sub-problems are well solved, the solutions may be unsatisfactory for the original problem. To find a high-quality solution, we use an alternating optimization (AO) method to jointly optimize the decomposed problems. Theoretical analysis demonstrates that the EVD method converges at the rate of O(1/s) and the AO method can converge to a sub-optimal solution if not the optimal solution. Simulation results show that the proposed algorithms significantly outperform other benchmark schemes.

Dingo‐optimization‐based task‐offloading algorithm in multihop V2V/V2I‐enabled networks

AbstractIn the Vehicle‐to‐Vehicle‐ (V2V) and Vehicle‐to‐Infrastructure‐ (V2I) enabled edge computing networks, the task vehicle may fail to offload tasks to the nodes outside the one‐hop communication with high computing resources, resulting in longer offloading time. Therefore, in this paper, we first construct a multihop V2V/V2I communications‐enabled edge computing architecture, which innovatively adopts vehicles and roadside units (RSUs) task offloading jointly to expand the communication resources of the task vehicle. Then, for the offloading node selection strategy, we create a vehicle adjacency table and propose a quickly depth‐first search‐based scheme to search the optimal multihop path. Finally, we develop an optimal offloading scheme based on the discrete dingo optimization algorithm (DDOA) to solve the task‐processing‐time minimization problem, which can converge quickly and achieve lower task offloading latency. Each dingo in DDOA is designed to choose one of the group attack, persecution, and scavenger strategies to search the solution space and accelerate the convergence of the DDOA by a survival rule. The numerical experimental results reveal that our proposed algorithm can outperform the other algorithms and reduce the delay time by 80% compared with the local scheme.