Computation Offloading System Research Articles

All-Pay Auction Based Profit Maximization in End-to-End Computation Offloading System

All-Pay Auction Based Profit Maximization in End-to-End Computation Offloading System

TOFDS: A Two-Stage Task Execution Method for Fake News in Digital Twin-Empowered Socio-Cyber World

Owing to the breakthrough in mobile wireless communication technologies, almost everyone has been immersed into social networks, while fake news and misinformation are also being pushed into people’s minds with astonishing speed and breadth. The rising disparity between limited computing resources and the exploding news size necessitates innovative solutions to handle the challenge posed by booming data volume and make it more likely to differentiate fake news. In response to the aforementioned dilemma, the social-aware computation offloading system is analyzed, where the digital twin (DT) paradigm is used to simulate tasks offloading and assess the associated costs. Next, to obtain the best offloading choice, we fully consider the social relationship constraints and further propose an online task execution method that includes two stages of cluster selection and computing offloading, named TOFDS. Specifically, it exploits the technologies from multiobjective optimization and deep reinforcement learning (DRL) and realizes the joint optimization of resource utilization, load balancing, service latency, and energy consumption. Eventually, the comparative experiments demonstrate that TOFDS performs well when dealing with fake news data and can adapt to changes in dataset size and service clusters.

Multi-Tier Hybrid Offloading for Computation-Aware IoT Applications in Civil Aircraft-Augmented SAGIN

Satellites and civil aircrafts (CAs) with computing ability are valuable access platforms, making it possible for Internet of Things (IoT) devices to offload their computation-intensive tasks in remote areas without network infrastructures. Unlike existing works mainly focused on the static scenarios or the interaction between any two types of local, edge and cloud nodes, we propose an innovative multi-tier hybrid parallel computation architecture in CA-augmented space-air-ground integrated networks (CAA-SAGIN). Specifically, devices perform local computing, CAs and satellites act as edge servers, and ground stations of satellite networks operate cloud computing. Aiming to minimize the weighted sum of end-to-end (E2E) delay and energy consumption, we formulate a partial computation offloading problem by jointly considering access strategy, transmit power, computing resource allocation, offloading ratio and delay tolerance. The platform selection exists both within and between layers, and there are inner- and inter-coupling relationships between communication and computing resources. The issue is solved by the proposed multi-tier partial task offloading (MPTO) algorithm. The original problem is firstly decomposed into primal and master subproblems by generalized benders decomposition (GBD) method, and parallel successive convex approximation (SCA) theory is utilized to transform the multi-variable NP-hard master problem into a convex one. Simulation results demonstrate the convergence and optimality of the MPTO algorithm and the advantages of this multi-tier hybrid computation offloading system. Also, the optimal tradeoff between E2E delay and energy consumption can be achieved by the MPTO algorithm.

Satisfaction-Maximized Secure Computation Offloading in Multi-Eavesdropper MEC Networks

In this paper, we consider a mobile edge computing (MEC)-based secure computation offloading system, and design a practical multi-eavesdropper model including two specific scenarios of non-colluding and colluding eavesdropping. Furthermore, we design a requirement satisfaction model by exploring practical variations in user request patterns for security provisioning, delay reduction and energy saving. Based on these, we propose a satisfaction-maximized secure computation offloading (SMax-SCO) scheme, and then formulate an optimization problem aiming at maximizing users’ requirement satisfactions subject to secrecy offloading rate, tolerable delay, task workload and maximum power constraints. Since the optimization problem is nonconvex, we present an efficient successive convex approximation (SCA)-based algorithm to obtain suboptimal solutions. We demonstrate that the proposed SMax-SCO scheme achieves a significant improvement in security performance and requirement satisfaction compared with existing schemes. Moreover, we conclude that SMax-SCO can resist eavesdropping attacks of multiple eavesdroppers and even colluding eavesdroppers.

Resource Configuration for Full-Duplex-Aided Multiple-Access Edge Computation Offloading

Multiple-access edge computation offloading (MECO) systems have been highlighted as a solution for extending the battery life and computation capability of mobile devices. However, in scenarios where information data and computation offloading (CO) data coexist, CO users and information users affect each other. It means that the communication resources for information data transmission are inevitably reduced by the communication resources allocated for CO in the conventional half-duplex (HD) based systems. Hence, improving the spectral efficiency of MECO systems in coexistence scenarios is essential, and we investigate a full-duplex (FD)-aided MECO (FD-MECO) system. A step-wise resource configuration is proposed to improve the performance of computation offloading under the information data rate constraint. The main idea is to improve spectral efficiency by maximizing the opportunity of operating in FD mode. By comparing all the communication phases in FD-MECO systems, the proposed resource configuration maximizes the amount of delivered CO data while guaranteeing the required information data rate. The simulation results show that FD-MECO systems with the proposed resource configuration always outperform HD-MECO systems.

Delay-Optimal Closed-Form Scheduling for Multi-Destination Computation Offloading

This letter studies a delay-optimal multi-destination computation offloading system, where the subtasks are transmitted to multiple Edge Computing Servers (ECSs) sequentially due to the limited communication resource, and different subtasks are computed in parallel at different ECSs. Consider the communication and computation capabilities, we jointly optimize the task assignment and the offloading scheduling for minimizing the total delay, which is the maximum completion time among all ECSs with their corresponding subtasks. We first consider a simplified case with equal task assignment, and then obtain the optimal offloading order according to the Lowest-Computation-First (LCF) rule. Under flexible task assignment, we derive the optimal offloading scheduling policy according to the Highest-Communication-First (HCF) offloading order with closed-form task assignment. Simulation results demonstrate the jointly optimized offloading scheduling policy reduces the delay by up to 62.4% compared with the non-scheduling offloading method.

Joint Mobility, Communication and Computation Optimization for UAVs in Air-Ground Cooperative Networks

Unmanned aerial vehicles (UAVs) play a significant role in various 5G or Beyond-5G (B5G)-enabled Internet-of-Things (IoT) applications. However, the UAV performance in an air-ground cooperative network is significantly affected by its mobility and air-to-ground (A2G) communication and computation behaviors. In this paper, a UAV-oriented computation offloading system is investigated, where the UAV desires to complete its onboard computation demands with the assistance of a ground edge-computing infrastructure, i.e., a road-side unit (RSU). The objective is to maximize the energy efficiency of the UAV. Specifically, a non-convex constrained optimal control problem is formulated to optimize the overall energy efficiency of UAV by jointly considering the coupled effects of UAV's longitudinal mobility, A2G communication, and computation dynamics. To address the coupled complexity and non-convexity of the original problem, a primal decomposition approach is developed to transform the problem into a convex subproblem and a primary problem, and then a closed-form optimal transmission power control is derived by solving the subproblem, which is dependent on mobility information. By embedding the closed-form optimal power control into the primary problem, a gradient projection-based iterative algorithm is proposed to obtain a joint optimal solution for both the longitudinal acceleration control and the power control, the feasibility and convergence of which is also theoretically proven. Extensive simulations have been conducted to validate the effectiveness of the proposed method in terms of constraint satisfaction and convergence speed, and comparative results also demonstrate that it can outperform other benchmark methods in terms of global energy efficiency.

Intelligent Offloading for NOMA-Assisted MEC via Dual Connectivity

Multiaccess edge computing (MEC), which brings computing capability close to the user equipment (UE) within a radio access network (RAN), is a promising technique to meet the challenges of explosive growth in edge data traffic and the number of connected devices. Nevertheless, MEC offloading failures, i.e., interruption of task offloading due to UE mobility and limited battery capacity, are still an important issue. In this article, we aim to minimize the total energy consumption in dual connectivity (DC)- and nonorthogonal multiple access (NOMA)-assisted computation offloading systems. This problem is formulated as a mixed-integer nonlinear programming (MINLP) problem with nonconvex constraints, which is a nondeterministic polynomial-time (NP)-hard and perplexing problem. To address this problem, we propose an iterative optimization algorithm to obtain a suboptimal time scheduling and task allocation solution. Specifically, we solve the nonconvex constrained MINLP problems by exploiting the successive convex approximation (SCA) algorithm and the nonlinear optimization algorithm with the mesh adaptive direct search (MADS) (NOMAD). Furthermore, a deep learning-based intelligent offloading scheme is introduced, called a cyclic branch network, to fully utilize data traffic and the computing capability on the network edge. Numerical results show that the proposed intelligent offloading scheme has comparable performance and dramatically reduces the inference time compared with the traditional optimization algorithm.

A Socially-Aware Hybrid Computation Offloading Framework for Multi-Access Edge Computing

Computation offloading manages resource-intensive and mobile collaborative applications (MCA) on mobile devices where much processing is replicated with multiple users in the same environment. In this article, we propose a novel hybrid multicast-based task execution framework for multi-access edge computing (MEC), where a crowd of mobile devices at the network edge leverage network-assisted device-to-device (D2D) collaboration for wireless distributed computing (MDC) and outcome sharing. The framework is socially aware in order to build effective D2D links. A key objective of this framework is to achieve an energy-efficient task assignment policy for mobile users. Specifically, we first introduce the socially aware hybrid computation offloading (SAHCO) system model, which combines of MEC offloading and D2D offloading in detail. Then, we formulate the energy-efficient task assignment problem by taking into account the necessary constraints. We next propose a Monte Carlo Tree Search based algorithm, named, TA-MCTS for the task assignment problem. Simulation results show that compared to four alternative benchmark solutions in literature, our proposal can reduce energy consumption up to 45.37 percent.

Computation offloading for mobile sign language video encoding

Mobile sign language video encoding is a kind of application that has high computation complexity. When using this application on battery powered mobile devices, there is conflict between computation complexity of encoder and energy limitation of mobile devices. In this paper, we propose a novel computation offloading method for mobile sign language video encoding. The method utilises sufficient cloud resources for Regions Of Interest (ROI) based sign language video encoding, which is the most computation-intensive part of the whole computation offloading system. With the method, a mobile device only consumes a small portion of energy to perform other parts of the offloading system. The theoretical analysis and experimental results both demonstrate that the proposed method can reduce the processing time and energy consumption of mobile devices.

Computational Offloading for Energy Constrained Devices in Multi-Hop Cooperative Networks

As the number of mobile devices that natively support ad hoc communication protocols increase, large ad hoc networks can be created not only to facilitate communication among the mobile devices, but also to assist devices that are executing computationally intensive applications. Prior work has developed computation offloading systems for mobile devices, but this work has focused exclusively on offloading to single hop neighbors, due in part to the practical challenges of setting up multi-hop networks using existing ad hoc communication protocols. However, limiting the offloading of computation to one-hop neighbors inherently restricts the number of devices that can participate in the distributed computation. By presenting a heuristic, aimed at avoiding partitioning the network, as well as an iterative task assignment algorithm that can optimize the assignment of computational tasks to devices in a multi-hop cooperative network, we are able to evaluate the effect of computational offloading in multi-hop networks. Experimental results, obtained from an implementation on Android devices, are integrated with an analytical model that enables the evaluation of system performance under a variety of conditions. These experimental and analytic results demonstrate the benefit of enabling computation offloading to all devices in a multi-hop cooperative network.

Joint Access and Resource Allocation in Ultradense mmWave NOMA Networks With Mobile Edge Computing

This article considers a two-tier heterogeneous network consisting of conventional sub-6-GHz macrocells along with millimeter-wave (mmWave) small cells, where mobile devices (MDs) can connect to either macrocell or small cells opportunistically via the nonorthogonal multiple access (NOMA) protocol. We employ the queuing theory in our network model to conduct an assessment on the execution delay, energy consumption and the total cost of offloading tasks in a mobile-edge computation offloading (MECO) system. The main goal is to design an energy-efficient MECO decision algorithm in an ultradense Internet of Thing (UD-IoT) network to analyze the tradeoff between execution delay and energy consumption. The proposed scheme jointly optimizes the communication and computation resource management, subject to the energy and delay constraints. Due to the mixed-integer nonlinear problem (MINLP) for resource allocation and computation offloading, an iterative algorithm along with the successive convex approximation (SCA) is proposed to achieve the optimum local frequency scheduling, power allocation, and computation offloading. The superior performance of the proposed MECO algorithm in our UD-IoT network is verified by the extensive numerical results.

Stochastic Control of Computation Offloading to a Helper With a Dynamically Loaded CPU

Due to densification of wireless networks, there exist abundance of idling computation resources at (network) edge helpers (e.g., base stations and handheld computers). These resources can be scavenged by offloading heavy computation tasks from small Internet-of-Things (IoT) devices (e.g., sensors and wearable computing devices) in proximity, thereby overcoming their limitations and lengthening their battery lives. However, unlike dedicated servers, the spare resources offered by edge helpers are random and intermittent . Thus, it is essential to intelligently control a user (IoT device) the amounts of data for offloading and local computing so as to ensure that a computation task can be finished in time-consuming minimum energy. In this paper, we design energy-efficient control policies in a computation offloading system with a random channel and a helper with a dynamically loaded CPU (due to the primary service). Specifically, the policy adopted by the helper aims at determining the sizes of offloaded and locally computed data for a given task in different slots such that the total energy consumption for transmission and local CPU is minimized under a task-deadline constraint. As the result, the polices endow an offloading user robustness against channel-and-helper randomness besides balancing offloading and local computing. By modeling the channel and helper CPU as Markov chains, the problem of offloading control is converted into a Markov decision process. Though dynamic programming (DP) for numerically solving the problem does not yield the optimal policies in closed form, we leverage the procedure to quantify the optimal policy structure and apply the result to design optimal or sub-optimal policies. For three cases ranging from zero, small to large helper buffers, the low complexity of the policies overcomes the “curse of dimensionality” in DP arising from joint consideration of channel, helper CPU, and buffer states.

An Optimal Pricing Scheme for the Energy-Efficient Mobile Edge Computation Offloading With OFDMA

The mobile edge computation offloading (MECO) system has emerged as a promising technology to address several problematic issues in cloud computing such as data throughput, capacity, and security. In this letter, we consider a single-cloud-multi-user MECO system with orthogonal frequency-division multiple access and propose an optimal pricing scheme to consider each mobile user's (MU) need for resources. The proposed model was formulated as a single-leader-multi-user Stackelberg game model, and the Stackelberg equilibrium was provided to optimize the utility of the MUs and the edge cloud. Here, the utility of the MU implies the energy efficiency and payment for using the computation capacity of the edge cloud, and the utility of the edge cloud implies the revenue for serving computation offloading. The optimal strategies of the MUs are uniquely given by closed-form expressions, and that of the edge cloud is given by gradient descent methods. Some numerical examples are provided to verify our approach.

A Hitchhiker's Guide to Computation Offloading: Opinions from Practitioners

Due to the increasing usage and capabilities of smart devices, mobile application developers build a large number of resource intensive applications, such as WAR applications. Even with the rapid development in hardware technology, the computing capability and battery capacity on wearable devices and smartphones still cannot meet the application demands with heavy computations and high battery drain. Pervasive computing addresses this problem by migrating applications to the resource providers external to mobile devices. The profitability of this method heavily depends on how to implement it and when to use it. Although there are many computation offloading systems proposed in the literature, there is no practical manual that addresses all the implementation complexities on the way to building a general offloading system. In this article, we review developments in the field of pervasive computing on computation offloading. We use this literature review together with our own experience and provide designers with some detailed guidelines to gain a deep insight into the implementation challenges of a computation offloading system. The guidelines empower the reader to choose between the variety of solutions in the literature for developing any offloading system with the consideration of their own system architecture and available facilities. Finally, we evaluate our general offloading system on Android devices with two real-time applications.