Mobile Computation Offloading Research Articles

Wireless and Service Allocation for Mobile Computation Offloading With Task Deadlines

Wireless and Service Allocation for Mobile Computation Offloading With Task Deadlines

Hybrid learning of predictive mobile-edge computation offloading under differently-aged network states

By offloading computationally demanding applications to edge servers, mobile edge computing (MEC) can alleviate the stringent hardware requirements and save energy consumption of resource-restrained devices. Mobile edge computation offloading (MECO, i.e., optimizing computation offloading and resource allocation) is critical to the performance of MEC. However, the existing study typically assumed the availability of all the network states for the upcoming time slot, not applicable in practical MECO with differently-aged network states (e.g., fast-changing practical wireless channels and instantly observable local task arrivals). This paper proposes a novel hybrid learning approach that optimizes the instantaneous local processing and predictive computation offloading decisions. We establish a new hybrid learning approach for instantaneous local processing and predictive computation offloading decisions by integrating the learning techniques of stochastic gradient descent (SGD) and online convex optimization (OCO). By decomposing the primal problems, the proposed approach can be implemented in a decentralized manner. We prove that the optimality loss resulting from the differently-aged network states can diminish with the decreasing stepsizes of SGD and OCO. Simulation results validate the asymptotic optimality and superiority of the proposed hybrid learning approach, as compared to state-of-the-art (e.g., based on OCO techniques).

Indoor Scene Recognition Mechanism Based on Direction-Driven Convolutional Neural Networks.

Indoor location-based services constitute an important part of our daily lives, providing position and direction information about people or objects in indoor spaces. These systems can be useful in security and monitoring applications that target specific areas such as rooms. Vision-based scene recognition is the task of accurately identifying a room category from a given image. Despite years of research in this field, scene recognition remains an open problem due to the different and complex places in the real world. Indoor environments are relatively complicated because of layout variability, object and decoration complexity, and multiscale and viewpoint changes. In this paper, we propose a room-level indoor localization system based on deep learning and built-in smartphone sensors combining visual information with smartphone magnetic heading. The user can be room-level localized while simply capturing an image with a smartphone. The presented indoor scene recognition system is based on direction-driven convolutional neural networks (CNNs) and therefore contains multiple CNNs, each tailored for a particular range of indoor orientations. We present particular weighted fusion strategies that improve system performance by properly combining the outputs from different CNN models. To meet users' needs and overcome smartphone limitations, we propose a hybrid computing strategy based on mobile computation offloading compatible with the proposed system architecture. The implementation of the scene recognition system is split between the user's smartphone and a server, which aids in meeting the computational requirements of CNNs. Several experimental analysis were conducted, including to assess performance and provide a stability analysis. The results obtained on a real dataset show the relevance of the proposed approach for localization, as well as the interest in model partitioning in hybrid mobile computation offloading. Our extensive evaluation demonstrates an increase in accuracy compared to traditional CNN scene recognition, indicating the effectiveness and robustness of our approach.

Deep Reinforcement Learning-Based Cloud-Edge Collaborative Mobile Computation Offloading in Industrial Networks

With the rapid development of mobile industrial applications and due to the limited coverage of static edge servers, traditional edge computing technology has great limitations in dynamic environmental applications. This paper proposes a deep reinforcement learning-based cloud-edge collaborative mobile computation offloading mechanism for satisfying the dynamic service requirements in industrial networks. Specifically, a three-layer network model of digital twins and a decentralized network of task resources are first constructed to handle the mobility of user terminals and the relevance of tasks. Then, based on the comprehensive consideration of mobility, associated tasks, computing resources and offloading decisions, an optimization problem is formulated to minimize the weighted sum of the execution delay and energy consumption of all tasks for all users. Additionally, a deep reinforcement learning-based cloud-edge collaborative mobile computation offloading (DRL-CCMCO) algorithm is proposed to solve this optimization problem. Based on the differences in each edge cloud, this algorithm sets the priority of the shared experience pool and selects the most effective experience samples to complete better learning and training. It also utilizes a distributed learning method to learn the probability of an approximate reward distribution and optimizes network parameters through cloud-edge collaboration to achieve faster optimal offloading decision. Finally, a large number of simulation results show that the proposed algorithm has the characteristics of fast convergence and high stability, and it can obtain the optimal offloading decision with the lowest total cost.

Work Allocating Strategy Using a Powerful Prioritized Tasks in Mobile Cloud Computing Atmosphere

Abstract: In recent times, users necessitate and expect more demanding criteria to perform computational in-depth applications on their mobile devices. Based on the mobile device limitations such as processing power and battery life, Mobile Cloud Computing (MCC) is turned to be a more attractive choice to influence these drawbacks as a mobile computation can be provided to the cloud, which is coined as Mobile computation deceive. Prevailing researches on mobile computation offloading determines offloading mobile computation to single cloud. Moreover, in real time environment, computation service can be offered by multiple clouds for every computation services. Therefore, a novel and an interesting research crisis in mobile computation offloading begins with, how to choose a computation service for every tasks of mobile computation like computation time, energy consumption and cost of using these computation services. This is also termed as multi-site computation offloading in mobile cloud computation. In this examination deceive computation to diverse cloudlets/data centres with respect to task scheduling is formulated for examination. So, a Searching algorithm known as Accelerated Cuckoo Search Algorithm based job splittingis designed to attain higher data transmission rate in the MCC. The results of the certain method outperform the prevailing methods in terms of effectual job splitting; transmission speed, Bandwidth used, execution time of a job, transmission value, through put value, buffering overhead and reduced waiting time. The simulation was carried out in Clouds environment for good output. Keywords: Computation Deceive, Mobile Cloud Computing, Scheduling, Searching Algorithm, WorkSplitting.

Secure Computation Offloading in Blockchain Based IoT Networks With Deep Reinforcement Learning

For current and future Internet of Things (IoT) networks, mobile edge-cloud computation offloading (MECCO) has been regarded as a promising means to support delay-sensitive IoT applications. However, offloading mobile tasks to the cloud gives rise to new security issues due to malicious mobile devices (MDs). How to implement offloading to alleviate computation burdens at MDs while guaranteeing high security in mobile edge cloud is a challenging problem. In this paper, we investigate simultaneously the security and computation offloading problems in a multi-user MECCO system with blockchain. First, to improve the offloading security, we propose a trustworthy access control mechanism using blockchain, which can protect cloud resources against illegal offloading behaviours. Then, to tackle the computation management of the authorized MDs, we formulate a computation offloading problem by jointly optimizing the offloading decisions, the allocation of computing resource and radio bandwidth, and smart contract usage. This optimization problem aims to minimize the long-term system costs of latency, energy consumption and smart contract fee among all MDs. To solve the proposed offloading problem, we develop an advanced deep reinforcement learning algorithm using a double-dueling Q-network. Evaluation results from real experiments and numerical simulations demonstrate the significant advantages of our scheme over the existing approaches.

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.

Preemptive Mobile Computation Offloading With Hard Deadlines and Concurrent Local Execution

This paper considers Preemptive Mobile Computation Offloading when concurrent local execution (CLE) is used to guarantee task execution time constraints. By allowing simultaneous local and remote execution, CLE ensures that job deadlines are always satisfied in the face of unforeseen wireless channel conditions. In the preemptive offloading case, at the start of each time slot, a decision is made to either continue or temporarily interrupt the offload. This mechanism allows the system to adapt when channel conditions change. The paper considers the case for homogeneous Markovian wireless channels. Using Markovian decision process stopping theory, an online energy-optimal computation offloading algorithm is formulated for preemptive offloading, referred to as Optimal Preemptive Offloading (OPO). Since the computational complexity of OPO can be prohibitive, the paper introduces three computationally efficient techniques motivated by OPO: 1) water-filling; 2) water-filling with scheduling; and 3) generalized water-filling. For each method, two variations are considered. The first (Equ) uses the equilibrium channel state probabilities in offloading decision calculations, and the second (Exp) uses Markovian transition matrix exponentiation. This results in six algorithms with a wide variety of energy performance and computational complexity. Performance of the algorithms is compared that shows the tradeoffs between complexity and mobile energy saving performance.

Retracted] Machine Learning‐Based Offloading Strategy for Lightweight User Mobile Edge Computing Tasks

This paper presents an in‐depth study and analysis of offloading strategies for lightweight user mobile edge computing tasks using a machine learning approach. Firstly, a scheme for multiuser frequency division multiplexing approach in mobile edge computing offloading is proposed, and a mixed‐integer nonlinear optimization model for energy consumption minimization is developed. Then, based on the analysis of the concave‐convex properties of this optimization model, this paper uses variable relaxation and nonconvex optimization theory to transform the problem into a convex optimization problem. Subsequently, two optimization algorithms are designed: for the relaxation optimization problem, an iterative optimization algorithm based on the Lagrange dual method is designed; based on the branch‐and‐bound integer programming method, the iterative optimization algorithm is used as the basic algorithm for each step of the operation, and a global optimization algorithm is designed for transmitting power allocation, computational offloading strategy, dynamic adjustment of local computing power, and receiving energy channel selection strategy. Finally, the simulation results verify that the scheduling strategy of the frequency division technique proposed in this paper has good energy consumption minimization performance in mobile edge computation offloading. Our model is highly efficient and has a high degree of accuracy. The anomaly detection method based on a decision tree combined with deep learning proposed in this paper, unlike traditional IoT attack detection methods, overcomes the drawbacks of rule‐based security detection methods and enables them to adapt to both established and unknown hostile environments. Experimental results show that the attack detection system based on the model achieves good detection results in the detection of multiple attacks.

Two-Layer Stackelberg Game-Based Offloading Strategy for Mobile Edge Computing Enhanced FiWi Access Networks

Carbon footprints of both computation and communications infrastructures attract growing public attention, raising concerns about global warming and motivating research efforts in developing energy-efficient solutions. Since Fiber-Wireless (FiWi) access networks have succeeded in interconnecting our digitalized world and mobile/multi-access edge computing (MEC) further removes unnecessary data transmission from optical backbone networks, MEC enhanced FiWi emerges as the win-win architecture for resource-hungry and delay-sensitive mobile apps. However, the assumption of unlimited and free resources offered by FiWi infrastructure is impractical, and energy-efficient offloading mechanisms for MEC enhanced FiWi still remain underexplored, conflicting with globally advocated green communications. This motivates us to design the Two-layer Stackelberg Game based Offloading (TSGO) strategy, including the FiWi layer and mobile edge computation offloading (MeCo) layer subgames capturing bandwidth allocation and offloading decision respectively. Then the backward induction and an iterative algorithm are exploited to acquire the Nash equilibrium of the two-layer Stackelberg game. Furthermore, we propose three energy efficiency benchmarks from both user-side and system-wide perspectives to evaluate mechanisms proposed for, but not limited to, FiWi, MEC, or a combination thereof. Simulation results validate the proposed TSGO strategy, which echoes the profound notion of green communications.

Collaborative Mobile Computation Offloading to Vehicle-Based Cloudlets

This paper investigates collaborative computation offloading in a vehicular network. Although there are increasingly more smart vehicles on roads, a significant number of legacy vehicles that are not equipped with powerful computing devices are expected to exist for a long time. When mobile devices located in these legacy vehicles require computation offloading, they can offload the tasks to nearby smart vehicles that are available to serve as cloudlet servers. Due to high mobility of the vehicles, multiple tasks of an application may have to be offloaded to different vehicle-based cloudlets. The offloading problem is formulated as a Markov decision process (MDP) by considering the randomness of the vehicle moving speeds and wireless channel conditions. The objective is to minimize the average completion time of the application. The complexity for solving the problem directly, however, is prohibitively high due to the large size of the state space and state transition probability matrix. The problem is solved by exploring the special structure of the state space, which helps reduce the computational complexity. A heuristic solution, namely, site-by-site and task-by-task (SSTT), is then proposed that makes the offloading decisions for individual tasks with much lower complexity. Simulation results show that the proposed SSTT solution not only achieves much lower average completion time, compared to executing all tasks locally and using distance-based offloading decisions, but also significantly reduces the energy consumption of the mobile device.

Energy-Efficient and Delay-Fair Mobile Computation Offloading

Mobile edge computing has been regarded as a new paradigm to achieve mobile computation offloading (MCO), which enables resource-limited mobile devices (MDs) to offload part or all of computation-intensive applications to more powerful computing entities. In this work we study MCO for data partitioned oriented applications. This type of services are usually delay-elastic, but shorter processing delay helps improve the user experience. We consider energy consumption of MDs and latency fairness of the applications. The latency fairness is achieved by allowing multiple offloading MDs to share one channel and allocating a fraction of the channel time for each application, and minimizing total energy consumption of the MDs is achieved by jointly optimizing the offloading ratio, channel assignments, and channel time allocations. For a special case that assigns at most one MD to each channel, a closed-form offloading ratio is derived and optimal channel assignment is obtained through transforming the problem into a weighted bipartite matching. The general problem is then solved by embedding the branch-and-bound iterations with successive convex approximation that solves a series of geometric programming problems. To reduce the complexity, a two-layer recursive method is then proposed that finds the channel assignments, offloading ratio and channel time allocation for each MD. Simulation results demonstrate that the proposed method can achieve low energy consumption and high latency fairness; and much lower energy consumption can be achieved with a slight decrease in latency fairness.

Energy-efficient computation offloading strategy with tasks scheduling in edge computing

In mobile edge computing systems, the energy consumption and execution delay can be reduced dramatically by mobile edge computation offloading (MECO) . However, due to the limited computing capacity of edge cloud, an energy-efficient offloading strategy plays a significant role. In this paper, the offloading decision problem for multi-device edge computing systems based on time-division multiple access is studied. The scheduling of offloading devices at the edge cloud is considered when modelling the edge computing system. Then, the offloading decision problem is formulated as an energy consumption minimization problem with the constraint of latency tolerance. It is a mixed integer programming problem of NP-hardness. To address the problem, a Dynamic Programming-based Energy Saving Offloading (DPESO) algorithm is designed to obtain the offloading strategy including the offloading option, offloading sequence and transmission power. First, the MECO with infinite edge cloud capacity is solved by device classification and transmission power decision. Then, we sort and adjust the offloading devices to meet the latency tolerance for the MECO with finite edge cloud capacity. Finally, simulation results demonstrate that the DPESO algorithm achieves better energy efficiency than the baseline strategies and has good scalability.

Optimal multi-part mobile computation offloading with hard deadline constraints

This paper considers mobile computation offloading when concurrent local execution (CLE) is used to enforce task execution time constraints. This mechanism can ensure that hard task deadlines are satisfied regardless of any randomness induced by the wireless channel, network or cloud servers. In this type of system, mobile device energy may be reduced by segmenting a task upload into multiple parts, rather than doing a conventional contiguous task upload. The paper uses this mechanism to adapt to changes in channel conditions during the offload. Unlike the contiguous task offload case however, the upload initiation times of each part must be determined dynamically. This is done while ensuring that hard task deadlines are always satisfied. In this paper, the multi-part computation offloading case is considered. In multi-part offloading, the task to be offloaded is partitioned into K upload parts before any offload initiation decisions are made. In this case, current channel state information is incorporated into the offload decisions, and the system must always satisfy a hard task execution time constraint using concurrent local execution. The paper considers the case for Markovian wireless channels. A provably energy-optimal online computation offloading algorithm (MuliOpt) is introduced for multi-part offloading. MultiOpt is shown to be optimal using Markovian decision process stopping theory. Since the computational complexity of MultiOpt can be significant, simpler and more computationally efficient heuristics, which also respect the hard task execution deadline, may be used. The paper introduces two such heuristics, the Immediate Offloading, and Multi Threshold algorithms. The mobile energy use of MultiOpt is compared to these heuristics, as well as to local execution without offloading and an offline energy bound. Simulation results show that MultiOpt performs significantly better when compared to the proposed heuristics, as well as when K increases.

Whale Optimization Algorithm With Applications to Resource Allocation in Wireless Networks

Resource allocation plays a pivotal role in improving the performance of wireless and communication networks. However, the optimization of resource allocation is typically formulated as a mixed-integer non-linear programming (MINLP) problem, which is non-convex and NP-hard by nature. Usually, solving such a problem is challenging and requires specific methods due to the major shortcomings of the traditional approaches, such as exponential computation complexity of global optimization, no performance optimality guarantee of heuristic schemes, and large training time and generating a standard dataset of machine learning based approaches. Whale optimization algorithm (WOA) has recently gained the attention of the research community as an efficient method to solve a variety of optimization problems. As an alternative to the existing methods, our main goal in this article is to study the applicability of WOA to solve resource allocation problems in wireless networks. First, we present the fundamental backgrounds and the binary version of the WOA as well as introducing a penalty method to handle optimization constraints. Then, we demonstrate three examples of WOA to resource allocation in wireless networks, including power allocation for energy-and-spectral efficiency tradeoff in wireless interference networks, power allocation for secure throughput maximization, and mobile edge computation offloading. Lastly, we present the adoption of WOA to solve a variety of potential resource allocation problems in 5G wireless networks and beyond.

Joint Computation Offloading and Radio Resource Allocations in Small-Cell Wireless Cellular Networks

Mobile computation offloading (MCO) is a technique that can help reduce energy consumption of mobile devices (MDs) by offloading their tasks to more powerful devices for execution. In MCO, the offloading decision for a task depends on availability of both communication and computation resource. In small cell cellular networks, cloudlet servers are usually co-located with the small base stations (SBSs). As a result, offloading decisions of the MDs are coupled with SBS associations, while strong overlapping coverage between the SBSs can result in complicated interference conditions in wireless transmissions that affect the offloading performance. In this paper, offloading decisions and SBS associations are jointly optimized with transmission power and channel assignments in a small cell cellular network. The objective is to minimize the total energy consumption of all MDs, subject to task’s latency constraints. The problem is first formulated as a mixed binary nonlinear programming problem, then transformed and solved using the general bender decomposition (GBD). A heuristic solution is proposed that recursively allows more MDs to make offloading decisions based on the current transmission conditions. Compared to using GBD, this solution results in much lower worst-case complexity, while achieving good energy performance for a wide range of system parameters.

A Multi-User Mobile Computation Offloading and Transmission Scheduling Mechanism for Delay-Sensitive Applications

In this paper, a mobile edge computing framework with multi-user computation offloading and transmission scheduling for delay-sensitive applications is studied. In the considered model, computation tasks are generated randomly at mobile users along the time. For each task, the mobile user can choose to either process it locally or offload it via the uplink transmission to the edge for cloud computing. To efficiently manage the system, the network regulator is required to employ a network-wide optimal scheme for computation offloading and transmission scheduling while guaranteeing that all mobile users would like to follow (as they may naturally behave strategically for benefiting themselves). By considering tradeoffs between local and edge computing, wireless features and noncooperative game interactions among mobile users, we formulate a mechanism design problem to jointly determine a computation offloading scheme , a transmission scheduling discipline , and a pricing rule . A queueing model is built to analytically describe the packet-level network dynamics. Based on this, we propose a novel mechanism, which can maximize the network social welfare (i.e., the network-wide performance), while achieving a game equilibrium among strategic mobile users. Theoretical and simulation results examine the performance of our proposed mechanism, and demonstrate its superiority over the counterparts.

Offloading Optimization and Bottleneck Analysis for Mobile Cloud Computing

Mobile cloud computing systems, or simply mobile clouds, have attracted tremendous attention because they allow mobile devices with limited computational resources to offload complex computations. However, due to the channel uncertainty and the complexity of a computation task, mobile computation offloading may suffer from poor outage performance that the offloaded task cannot be completed within the desired delay constraint. Thus, how to efficiently identify and overcome the outage bottleneck, which could be used to optimize resource allocation schemes and improve the system performance effectively is an open problem. In this paper, we shall develop a unified framework that minimizes the overall outage probability in various mobile computation offloading scenarios. More specifically, the outage bottleneck is defined and identified by adopting asymptotic analysis, without any need of the accurate outage probabilities in both transmissions and computations. To overcome the outage bottleneck, resource pairing, matching, and allocation policies are investigated. Both theoretical analysis and numerical results show that the outage bottleneck relies on not only the availability of spectrum and computation resources but also the probability distributions of computation complexities of the computation tasks.

Joint Job Partitioning and Collaborative Computation Offloading for Internet of Things

Advances in Internet of Things (IoT) bring massive intelligent applications, many of which are computation intensive and time sensitive. With limited resources of IoT devices, mobile computation offloading can be exploited to offload part of the applications to nearby devices that have more powerful computing resources, thereby speeding up the applications and reducing the energy consumption. In this paper, we consider application partitioning and collaborative computation offloading in IoT networks, in order to meet the completion deadline of the applications while minimizing the overall energy consumption. The problem is formulated as a binary integer linear programming problem, which is transformed into a weighted bipartite matching problem and then solved by the centralized Kuhn-Munkres algorithm. To fit the large-scale IoT scenarios, three distributed algorithms are then introduced from different perspectives. The first one is referred to as the noncooperative matching (NCM) algorithm, where each node makes offloading decision based on its own interest in minimizing energy consumption. Afterward, an asynchronous greedy matching (AGM) algorithm is developed by considering the mutual interest of the requestor and collaborator pairs in terms of their energy consumptions. Finally, a maximum differential energy matching (MDEM) algorithm is devised by relaxing the network stability requirement, which can further benefit the energy efficiency for all network nodes. Theoretical analysis and simulation results demonstrate that both the NCM and AGM algorithms guarantee the network stability and improve the energy saving compared with entirely local execution, while the MDEM algorithm can further achieve near-optimal energy consumption at the expense of higher implementation overheads.

Mobile Computation Offloading for Application Throughput Fairness and Energy Efficiency

Mobile computation offloading is emerging as a promising technology to enhance the computation power of mobile devices by borrowing processing resources from the cloud. However, using cloud resource is a double-edged sword because of the potentially enormous network energy consumption of mobile devices. In this paper, we study the mobile device resource management problem for application throughput fairness and energy efficiency in computation offloading environment. Our problem seeks to optimize task arrival rates, scheduling for local processing and offloading, CPU clock speed, and network interface selection, so as to maximize the energy-utility efficiency defined as achievable utility per unit energy consumption. The efficiency metric has a fractional form that is hard to deal with in general. To address this difficulty, we modify a general Lyapunov optimization technique and derive a series of short-term problems that change over time with respect to an unknown objective parameter. Then, we derive an offloading algorithm and prove that the algorithm maximizes the long-term energy-utility efficiency. Trace-driven simulations demonstrate that our algorithm achieves high-energy efficiency while maintaining throughput fairness among applications running on a mobile device.