Computation Offloading Decision Research Articles

Multi-objective optimization of SFC deployment using service aggregation and computing offload

New technologies such as virtualization and Software Defined Networking (SDN) have given traditional networks unprecedented flexibility. A Service Function Chain (SFC) formed by concatenating multiple Virtual Network Functions (VNFs) expands network functions to meet the growing personalized network requirements of applications. However, due to the decentralization of VNF instances and the tight cloud load, how to ensure Quality of Service (Qos) while deploying the SFC brings new challenges. Hence, a SFC deployment strategy based on multi-objective optimization, named MO-SACO is proposed, which regards delay optimization, reliability assurance and cost reduction as the main objectives. Specifically, we first design a VNF aggregation rule to handle raw SFC requests, which effectively reduces the SFC path latency and improves reliability. Then, VNF placement and traffic routing is modeled as a computation offloading decision based on cloud-fog-edge collaboration with the goal of target optimization according to functional, location, and resource constraints. Finally, we also take into account the performance changes of deployed SFCs and handle non-compliant requests in a timely manner. We conduct extensive simulations in networks of varying sizes and the results demonstrate that the proposed MO-SACO strategy not only achieves lower latency and cost, higher reliability compared with the state-of-the-art methods, but also has unexpected performance in terms of the deployment success rate and node load.

Joint computation offloading and resource allocation in space-air-terrestrial integrated networks for IoT Applications

Internet of Things (IoT) devices can reduce their energy consumption by computation offloading. However, IoT devices located in areas without deployed ground communication facilities face significant challenges in computation offloading. For this reason, we propose the space-air-terrestrial integrated networks (SATINs) and design a three-tier computing framework for providing computing services to IoT devices. In the computing framework, device's task can be computed locally, on mobile edge computing (MEC) servers in the air layer, or on cloud servers in the ground. In this article, we jointly optimize the computation offloading decisions of tasks and computing resource allocation of MEC servers. We aim at minimizing the total cost of executing tasks while satisfying both the time constraints of tasks and capacity constraints of MEC servers. And the total cost includes the energy cost of IoT devices and the usage cost of servers. Since the computation offloading decisions are binary variables, the joint optimization problem is a mixed integer nonlinear programming (MINLP) problem and is NP-hard. To tackle this problem, we use relaxation technique and Majorize-minimize (MM) method to transform the optimization problem into a series of convex problems for solving. Moreover, we propose a distributed algorithm based on Lagrange dual decomposition method with low time complexity. Experimental results demonstrate that our proposed distributed algorithm can effectively reduce the total cost of the system compared with other benchmark algorithms.

Joint Service Caching and Computation Offloading Scheme Based on Deep Reinforcement Learning in Vehicular Edge Computing Systems

Vehicular edge computing (VEC) is a new computing paradigm that enhances vehicular performance by introducing both computation offloading and service caching, to resource-constrained vehicles and ubiquitous edge servers. Recent developments of autonomous vehicles enable a variety of applications that demand high computing resources and low latency, such as automatic driving, auto navigation, etc. However, the highly dynamic topology of vehicular networks and limited caching space at resource-constrained edge servers calls for intelligent design of caching placement and computation offloading. Meanwhile, service caching decisions are highly correlated to the computation offloading decisions, which pose a great challenge to effectively design service caching and computation offloading strategies. In this paper, we investigate a joint optimization problem by integrating service caching and computation offloading in a general VEC scenario with time-varying task requests. To minimize the average task processing delay, we formulate the problem using long-term mixed integer non-linear programming (MINLP) and propose an algorithm based on deep reinforcement learning to obtain a suboptimal solution with low computation complexity. The simulation results demonstrate that our proposed scheme exhibits an effective performance improvement in task processing delay compared with other representative benchmark methods.

Offloading dependent tasks in MEC-enabled IoT systems: A preference-based hybrid optimization method

The rapid development of IoT-based services has resulted in an exponential increase in the number of connected smart mobile devices (SMDs). Processing the massive data generated by the large number of SMDs is becoming a big problem for mobile devices, servers, and wireless communication channels. A Multi-access Edge Computing (MEC) paradigm partially mitigates this problem by deploying edge server nodes at the edge of wireless networks nearby SMDs, but the challenge still remains due to the limited computation capacity of MEC servers and the bandwidth of wireless channels. In addition, the dependency of tasks generated by applications on SMDs increases the complexity of the problem. In this paper, we propose a constrained multiobjective computation offloading optimization solution to resolve the problem of task dependency under limited resources. This solution improves the Quality of Service (QoS) through minimizing the latency, energy consumption, and rate of task failure caused by limited resources. We propose a two-staged hybrid computation offloading optimization method to solve the problem. In the first stage, the computation offloading decisions are made based on the preferences of tasks. Then, in the second stage, nearly optimal solutions are found using the modified Non-Dominated Sorting Genetic Algorithm (NSGA-III). The overall efficiency of the proposed method is increased owing to the preference-based algorithm reinforcing the NSGA-III algorithm by generating a better initial population. The results of extensive experiments show that the efficiency of the proposed method is significantly better than the existing methods.

Computation Offloading Game for Multi-Channel Wireless Sensor Networks.

Computation offloading for wireless sensor devices is critical to improve energy efficiency and maintain service delay requirements. However, simultaneous offloadings may cause high interferences to decrease the upload rate and cause additional transmission delay. It is thus intuitive to distribute wireless sensor devices in different channels, but the problem of multi-channel computation offloading is NP-hard. In order to solve this problem efficiently, we formulate the computation offloading decision problem as a decision-making game. Then, we apply the game theory to address the problem of allowing wireless sensor devices to make offloading decisions based on their own interests. In the game theory, not only are the data size of wireless sensor devices and their computation capability considered but the channel gain of each wireless sensor device is also included to improve the transmission rate. The consideration could evenly distribute wireless sensor devices to different channels. We prove that the proposed offloading game is a potential game, where the Nash equilibrium exists in each game after all device states converge. Finally, we extensively evaluate the performance of the proposed algorithm based on simulations. The simulation results demonstrate that our algorithm can reduce the number of iterations to achieve Nash equilibrium by 16%. Moreover, it improves the utilization of each channel to effectively increase the number of successful offloadings and lower the energy consumption of wireless sensor devices.

Non-cooperative game algorithms for computation offloading in mobile edge computing environments

Mobile Edge Computing (MEC) has become a promising technology for 5G networks. Computation offloading is an essential issue of MEC, which enables mobile User Equipment (UE) to enjoy rich wireless resources and huge computing power anywhere. This paper considers the Quality-of-Experience (QoE) of UEs in 5G MEC systems and presents a dynamic non-cooperative game (QCOG-DG) algorithm and a static non-cooperative game (QCOG-SG) algorithm for computation offloading of MEC applications. We establish an MEC computation offloading model by considering the QoE requirements of UEs, and discuss the communication overheads, computation cost, and energy consumption models to minimize the energy consumption and time delay of each UE. Considering that there are multiple UEs who want to offload their computation tasks to a resource-constrained MEC server, and each UE is selfish and competitive, we formulate the problem of computation offloading decision as a non-cooperative game model. We prove the existence of a Nash Equilibrium (NE) solution for the proposed game model. In addition, we propose an algorithm that jointly optimizes energy consumption and time delay under QoE preferences to achieve optimal offloading benefits for each UE. Moreover, we respectively propose a dynamic non-cooperative game (QCOG-DG) algorithm and a static non-cooperative game (QCOG-SG) algorithm to efficiently find the NE solution. Extensive simulation experiments are conducted to verify the effectiveness of the proposed MEC computation offloading model and the QCOG-DG and QCOG-SG algorithms. Simulation results show that the proposed QCOG-DG algorithm can efficiently find the NE solutions in the MEC scenarios with UEs of different sizes.

Joint Optimization for MEC Computation Offloading and Resource Allocation in IoV Based on Deep Reinforcement Learning

In the Internet of Vehicle (IoV), the limited computing capacity of vehicles hardly processes the intensive computation tasks locally. The computation tasks can be offloaded to multiaccess edge computing (MEC) servers for processing, where MEC provides the required computing capacity to the nearby vehicles. In this paper, we consider a scenario where there are cooperation and competition between vehicles, the offloading decision of any vehicle will affect the decisions of the others, and the computing resource allocation strategies by MEC will dynamically change. Therefore, we propose a joint optimization scheme for computation offloading decisions and computing resource allocation based on decentralized multiagent deep reinforcement learning. The proposed scheme learns the optimal actions to minimize the total weighted cost which is designed as the vehicles’ satisfaction based on the type of stochastic arrival tasks and dynamic interaction between MEC server and vehicles within different RSUs coverages. The numerical results show that the proposed algorithms based on decentralized multiagent deep deterministic policy gradient (DDPG) which is named De-DDPG can autonomously learn the optimal computation offloading and resource allocation policy without a priori knowledge and outperform the other three baseline algorithms in terms of the rewards.

Twin delayed deep deterministic policy gradient-based intelligent computation offloading for IoT

In view of the randomness distribution of multiple users in the dynamic large-scale Internet of Things (IoT) scenario, comprehensively formulating available resources for fog nodes in the area and achieving computation services at low cost have become great challenges. As a result, this paper studies an efficient and intelligent computation offloading mechanism with resource allocation. Specifically, an optimization problem is formulated to minimize the total energy consumption of all tasks under the joint optimization of computation offloading decisions, bandwidth resources and transmission power. Meanwhile, a Twin Delayed Deep Deterministic Policy Gradient-based Intelligent Computation Offloading (TD3PG-ICO) algorithm is proposed to solve this optimization problem. By combining the concept of the actor critic algorithm, the proposed algorithm designs two independent critic networks that can avoid the subjective prediction of a single critic network and better guide the policy network to generate the global optimal computation offloading policy. Additionally, this algorithm introduces a continuous variable discretization operation to select the target offloading node with random probability. The available resources of the target node are dynamically allocated to improve the model decision-making effect. Finally, the simulation results show that this proposed algorithm has faster convergence speed and good robustness. It can always approach the greedy algorithm with respect to the lowest total energy consumption. Furthermore, compared with full local and Deep Q-learning Network (DQN)-based computation offloading schemes, the total energy consumption can be reduced by an average of 15.53% and 6.41%, respectively.

Distributed deep learning for cooperative computation offloading in low earth orbit satellite networks

Low earth orbit (LEO) satellite network is an important development trend for future mobile communication systems, which can truly realize the "ubiquitous connection" of the whole world. In this paper, we present a cooperative computation offloading in the LEO satellite network with a three-tier computation architecture by leveraging the vertical cooperation among ground users, LEO satellites, and the cloud server, and the horizontal cooperation between LEO satellites. To improve the quality of service for ground users, we optimize the computation offloading decisions to minimize the total execution delay for ground users subject to the limited battery capacity of ground users and the computation capability of each LEO satellite. However, the formulated problem is a large-scale nonlinear integer programming problem as the number of ground users and LEO satellites increases, which is difficult to solve with general optimization algorithms. To address this challenging problem, we propose a distributed deep learning-based cooperative computation offloading (DDLCCO) algorithm, where multiple parallel deep neural networks (DNNs) are adopted to learn the computation offloading strategy dynamically. Simulation results show that the proposed algorithm can achieve near-optimal performance with low computational complexity compared with other computation offloading strategies.

A review of optimization methods for computation offloading in edge computing networks

Handling the massive amount of data generated by Smart Mobile Devices (SMDs) is a challenging computational problem. Edge Computing is an emerging computation paradigm that is employed to conquer this problem. It can bring computation power closer to the end devices to reduce their computation latency and energy consumption. Therefore, this paradigm increases the computational ability of SMDs by collaboration with edge servers. This is achieved by computation offloading from the mobile devices to the edge nodes or servers. However, not all applications benefit from computation offloading, which is only suitable for certain types of tasks. Task properties, SMD capability, wireless channel state, and other factors must be counted when making computation offloading decisions. Hence, optimization methods are important tools in scheduling computation offloading tasks in Edge Computing networks. In this paper, we review six types of optimization methods - they are Lyapunov optimization, convex optimization, heuristic techniques, game theory, machine learning, and others. For each type, we focus on the objective functions, application areas, types of offloading methods, evaluation methods, as well as the time complexity of the proposed algorithms. We discuss a few research problems that are still open. Our purpose for this review is to provide a concise summary that can help new researchers get started with their computation offloading researches for Edge Computing networks.

Code Caching-Assisted Computation Offloading and Resource Allocation for Multi-User Mobile Edge Computing

Utilizing the data caching technology to reduce data transmission is a promising technique for improving the performance of mobile edge computing (MEC), because the delay and energy consumption produced by data transmission constitute the dominant cost of task execution in MEC. Besides, computation tasks generally consist of input parameters, executive codes, and computation results. The executive codes are fixed and can output difference computation results under different input parameters. Motivated by this, we consider to proactively cache executive codes of tasks at the MEC server to reduce the weighted sum of task execution delay and users’ energy consumption. Aiming at establishing optimal system design, we formulate the problem as a non-linear programming problem which involves jointly optimizing the executive code caching strategy, computation offloading decision, wireless resource allocation, and computing resource allocation. We propose to find the optimal solution by employing an alternating optimization framework. The optimal wireless resource and computing resource allocation problem are firstly addressed by utilizing convex optimization technology. Then, a dynamic programming-based algorithm has been developed to achieve the optimal executive code caching and computation offloading strategies. Extensive simulation results show that the proposed scheme operates well and can substantially reduce the system cost over other benchmark schemes.

Reliability-Guaranteed Admission Control for Mobile Computation Offloading Under Nakagami Fading Channel

The coupled stochastic channel interferences can lead to intermittent connectivity and invalid fragment transmissions, which pose a significant challenge to guarantee the reliability of computation offloading. A wide variety of conventional approaches to making offloading decisions are based on the fundamental condition that channels are sufficiently reliable for completing each transmission session. However, the reliability that a user can successfully offload a computation task before a restricted deadline remains unexplored under the interference channels. In this letter, we focus on the Nakagami- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${m}$ </tex-math></inline-formula> fading channel and propose an analytical framework to characterize the reliability of computation offloading with the restrictions of application deadline and offloading data size in the presence of coupled stochastic interferences. A lower-bound offloading reliability capturing coupled randomness is theoretically derived. Based on the analytical framework, we further propose an admission control method for users to make computation offloading decisions. Simulation results verify our theoretical framework and show the superior performance of the proposed method over other benchmark schemes in terms of guaranteeing reliability.

Computation offloading technique for energy efficiency of smart devices

The substantial number of wearable devices in the healthcare industry and the continuous growth of the market procreates the demand for computational offloading. Despite major development of wearable devices and offloading techniques, there are several concerns such as latency, battery power, and computation capability that requires significant development. In this paper, we focus on the fact that most smart wearable devices have Bluetooth pairing with smartphones, and Bluetooth communication is significantly energy-efficient compare to 3G/LTE or Wi-Fi. We propose a computation offloading technique that offloads from the smartphone to the cloud server considering the decision model of both wearable devices and smartphones. Mobile cloud computing can elevate the capacity of smartphones considering the battery state and efficient communications with the cloud. In our model, we increase the energy efficiency of smart devices. To accomplish this, a Dhrystone Millions of Instructions per Second (DMIPS)-based workload measurement model along with a computation offloading decision model were created. According to the performance evaluation, offloading from wearable devices to smartphones and offloading once to cloud server can reduce energy consumption significantly.

Freshness-Aware Information Update and Computation Offloading in Mobile-Edge Computing

Mobile-edge computing is a promising computing paradigm with the advantages of reduced delay and relieved outsourcing traffic to the core network. In mobile-edge computing, reducing the computation offloading cost of mobile users and maintaining fresh information at edge nodes are two critical while conflicted objectives, as both consume the limited wireless bandwidth of edge nodes. Although extensive efforts have been devoted to optimizing computation offloading decisions and some works have investigated freshness-aware channel allocation issues recently, no prior works have considered the above conflict. This article is the first work to jointly optimize the channel allocation and computation offloading decisions, aiming at reducing the computation offloading cost within freshness requirements of sensors. We analyze the recursiveness of Age of Information (AoI) in analogy to the evolvement of a queue and formulate the problem as a nonlinear integer dynamic optimization problem. To overcome the challenges of AoI-computation cost tradeoff, AoI time dependency and high complexity caused by the heterogeneity of users, we propose an algorithm to solve the problem with reduced computation complexity. Specifically, we first transform the original problem into a static optimization problem in each time slot (which is NP-hard) based on Lyapunov optimization techniques. To reduce the computation complexity, we exploit the finite improvement property of potential games and further enforce centralized control to reduce the number of improvement iterations. Simulations have been conducted and the results demonstrate that the proposed algorithm shows good effectiveness and scalability.

Cooperative computation offloading and resource allocation for delay minimization in mobile edge computing

Mobile edge computing (MEC) is a promising paradigm, which brings computation resources in proximity to mobile devices and allows the tasks of mobile devices to be offloaded to MEC servers with low latency. The joint problem of cooperative computation task offloading and resource allocation is a challenging issue. The joint problem of cooperative computation task offloading scheme and resource assignment in MEC is investigated in this paper, where the vertical cooperation among mobile devices, mobile edge server nodes and mobile cloud server nodes is considered, and the horizontal computation cooperation between edge nodes is considered as well. A computation offloading decision, cooperative selection, power allocation and CPU cycle frequency assignment problem is formulated. The objective is to minimize the latency while guaranteeing the constraint of transmission power, energy consumption and CPU cycle frequency. The formulated latency optimization problem is a nonconvex mixed-integer problem in general, which has binary variables and continuous variables. In order to solve the formulated problem. A joint iterative algorithm based on the Lagrangian dual decomposition, ShengJin Formula method, and monotonic optimization method is proposed. The CPU cycle frequence allocation is handled by the ShengJin Formula method due to the cubic equation of one variable about the CPU frequence allocation. The transmission power assignment is handled by the monotonic optimization method. In the algorithm convergence with different number of tasks, the proposed algorithm can quickly and effectively reach the convergence state and getting the minimum task execution delay. Numerical results demonstrate that the proposed algorithm outperforms the Full MEC, Full Local and Full Cloud three schemes in terms of execution latency.

Research on Task-Oriented Computation Offloading Decision in Space-Air-Ground Integrated Network

In Space–Air–Ground Integrated Networks (SAGIN), computation offloading technology is a new way to improve the processing efficiency of node tasks and improve the limitation of computing storage resources. To solve the problem of large delay and energy consumption cost of task computation offloading, which caused by the complex and variable network offloading environment and a large amount of offloading tasks, a computation offloading decision scheme based on Markov and Deep Q Networks (DQN) is proposed. First, we select the optimal offloading network based on the characteristics of the movement of the task offloading process in the network. Then, the task offloading process is transformed into a Markov state transition process to build a model of the computational offloading decision process. Finally, the delay and energy consumption weights are introduced into the DQN algorithm to update the computation offloading decision process, and the optimal offloading decision under the low cost is achieved according to the task attributes. The simulation results show that compared with the traditional Lyapunov-based offloading decision scheme and the classical Q-learning algorithm, the delay and energy consumption are respectively reduced by 68.33% and 11.21%, under equal weights when the offloading task volume exceeds 500 Mbit. Moreover, compared with offloading to edge nodes or backbone nodes of the network alone, the proposed mixed offloading model can satisfy more than 100 task requests with low energy consumption and low delay. It can be seen that the computation offloading decision proposed in this paper can effectively reduce the delay and energy consumption during the task computation offloading in the Space–Air–Ground Integrated Network environment, and can select the optimal offloading sites to execute the tasks according to the characteristics of the task itself.

Computation Offloading in LEO Satellite Networks With Hybrid Cloud and Edge Computing

Low earth orbit (LEO) satellite networks can break through geographical restrictions and achieve global wireless coverage, which is an indispensable choice for future mobile communication systems. In this article, we present a hybrid cloud and edge computing LEO satellite (CECLS) network with a three-tier computation architecture, which can provide ground users with heterogeneous computation resources and enable ground users to obtain computation services around the world. With the CECLS architecture, we investigate the computation offloading decisions to minimize the sum energy consumption of ground users, while satisfying the constraints in terms of the coverage time and the computation capability of each LEO satellite. The considered problem leads to a discrete and nonconvex since the objective function and constraints contain binary variables, which makes it difficult to solve. To address this challenging problem, we convert the original nonconvex problem into a linear programming problem by using the binary variables relaxation method. Then, we propose a distributed algorithm by leveraging the alternating direction method of multipliers (ADMMs) to approximate the optimal solution with low computational complexity. Simulation results show that the proposed algorithm can effectively reduce the total energy consumption of ground users.

Computation offloading through mobile vehicles in IoT-edge-cloud network

With the developing of Internet of Things (IoT) and mobile edge computing (MEC), more and more sensing devices are widely deployed in the smart city. These sensing devices generate various kinds of tasks, which need to be sent to cloud to process. Usually, the sensing devices do not equip with wireless modules, because it is neither economical nor energy saving. Thus, it is a challenging problem to find a way to offload tasks for sensing devices. However, many vehicles are moving around the city, which can communicate with sensing devices in an effective and low-cost way. In this paper, we propose a computation offloading scheme through mobile vehicles in IoT-edge-cloud network. The sensing devices generate tasks and transmit the tasks to vehicles, then the vehicles decide to compute the tasks in the local vehicle, MEC server or cloud center. The computation offloading decision is made based on the utility function of the energy consumption and transmission delay, and the deep reinforcement learning technique is adopted to make decisions. Our proposed method can make full use of the existing infrastructures to implement the task offloading of sensing devices, the experimental results show that our proposed solution can achieve the maximum reward and decrease delay.

Efficient and Secure Multi-User Multi-Task Computation Offloading for Mobile-Edge Computing in Mobile IoT Networks

Mobile edge computing (MEC) is a new paradigm to alleviate resource limitations of mobile IoT networks through computation offloading with low latency. This article presents an efficient and secure multi-user multi-task computation offloading model with guaranteed performance in latency, energy, and security for mobile-edge computing. It does not only investigate offloading strategy but also considers resource allocation, compression and security issues. Firstly, to guarantee efficient utilization of the shared resource in multi-user scenarios, radio and computation resources are jointly addressed. In addition, JPEG and MPEG4 compression algorithms are used to reduce the transfer overhead. To fulfill security requirements, a security layer is introduced to protect the transmitted data from cyber-attacks. Furthermore, an integrated model of resource allocation, compression, and security is formulated as an integer nonlinear problem with the objective of minimizing the weighted sum of energy under a latency constraint. As this problem is considered as NP-hard, linearization and relaxation approaches are applied to transform the problem into a convex one. Finally, an efficient offloading algorithm is designed with detailed processes to make the computation offloading decision for computation tasks of mobile users. Simulation results show that our model not only saves about 46% of system overhead consumption in comparison with local execution but also scale well for large-scale IoT networks.

Incentive UAV-Enabled Mobile Edge Computing Based on Microwave Power Transmission

With the increasing requirements of the flexible and delay-sensitive computing services, UAV has attracted widespread attention for assisting mobile edge computing (MEC) system. However, due to the limited battery capacity, the UAV cannot always fly and serve the users in the air. To address this issue, a UAV-enabled MEC system based on microwave power transmission is proposed in this paper. In this system, the UAV periodically and continually flies over the users' area to provide computing services and will be charged over the microwave station. Considering profit-centric MEC service provider, the UAV will charge fees from users for the computation services. Then, an optimization problem is formulated to maximize the service utility of the UAV by finding the optimal UAV's trajectory, computation offloading decisions and offloading duration. Since the proposed optimization problem is non-convex, the original problem is decomposed into three sub-problems and a three-stage alternative algorithm is presented to solve them iteratively. The numerical results show that the proposed offloading method can achieve better performance than other baseline methods.