An efficient algorithm for dynamic task offloading using cloudlets in mobile cloud computing

It is a visible fact that the growth of mobile devices is enormous. More computations are required to be carried out for various applications in these mobile devices. But the drawback of the mobile devices is less computation power and low available energy. The mobile cloud computing helps in resolving these issues by integrating the mobile devices with cloud technology. Again, the issue is increased in the latency as the task and data to be offloaded to the cloud environment uses WAN. Hence, to decrease the latency, this paper proposes cloudlet‐based dynamic task offloading (CDTO) algorithm where the task can be executed in device environment, cloudlet environment, cloud server environment, and integrated environment. The proposed algorithm, CDTO, is tested in terms of energy consumption and completion time.     

Multi-objective optimization for task offloading based on network calculus in fog environments

With the widespread application of wireless communication technology and continuous improvements to Internet of Things (IoT) technology, fog computing architecture composed of edge, fog, and cloud layers have become a research hotspot. This architecture uses Fog Nodes (FNs) close to users to implement certain cloud functions while compensating for cloud disadvantages. However, because of the limited computing and storage capabilities of a single FN, it is necessary to offload tasks to multiple cooperating FNs for task completion. To effectively and quickly realize task offloading, we use network calculus theory to establish an overall performance model for task offloading in a fog computing environment and propose a Globally Optimal Multi-objective Optimization algorithm for Task Offloading (GOMOTO) based on the performance model. The results show that the proposed model and algorithm can effectively reduce the total delay and total energy consumption of the system and improve the network Quality of Service (QoS).     

Cuckoo: flexible compute‐intensive task offloading in mobile cloud computing

Mobile cloud computing (MCC) is an emerging technology to facilitate complex application execution on mobile devices. Mobile users are motivated to implement various tasks using their mobile devices for great flexibility and portability. However, such advantages are challenged by the limited battery life of mobile devices. This paper presents Cuckoo, a scheme of flexible compute‐intensive task offloading in MCC for energy saving. Cuckoo seeks to balance the key design goals: maximize energy saving (technical feasibility) and minimize the impact on user experience with limited cost for offloading (realistic feasibility). Specifically, using a combination of static analysis and dynamic profiling, compute‐intensive tasks are fine‐grained marked from mobile application codes offline. According to the network transmission technologies supported in mobile devices and the runtime network conditions, adopting “task‐bundled” strategy online offloads these tasks to MCC. In the task‐hosted stage, we propose a skyline‐based online resource scheduling strategy to satisfy the realistic feasibility of MCC. In addition, we adopt resource reservation to reduce the extra energy consumption caused by the task multi‐offloading phenomenon. Further, we evaluate the performance of Cuckoo using real‐life data sets on our MCC testbed. Our extensive experiments demonstrate that Cuckoo is able to balance energy consumption and execution performance. Copyright © 2016 John Wiley & Sons, Ltd.     

Q-learning-based task offloading strategy for satellite edge computing

In this paper, we study the task offloading optimization problem in satellite edge computing environments to reduce the whole communication latency and energy consumption so as to enhance the offloading success rate. A three-tier machine learning framework consisting of collaborative edge devices, edge data centers, and cloud data centers has been proposed to ensure an efficient task execution. To accomplish this goal, we also propose a Q-learning-based reinforcement learning offloading strategy in which both the time-sensitive constraints and data requirements of the computation-intensive tasks are taken into account. It enables various types of tasks to select the most suitable satellite nodes for the computing deployment. Simulation results show that our algorithm outperforms other baseline algorithms in terms of latency, energy consumption, and successful execution efficiency.     

A novel data size-aware offloading technique for resource provisioning in mobile cloud computing

Mobile device users are involved in social networking, gaming, learning, and even some office work, so the end users expect mobile devices with high-response computing capacities, storage, and high battery power consumption. The data-intensive applications, such as text search, online gaming, and face recognition usage, have tremendously increased. With such high complex applications, there are many issues in mobile devices, namely, fast battery draining, limited power, low storage capacity, and increased energy consumption. The novelty of this work is to strike a balance between time and energy consumption of mobile devices while using data-intensive applications by finding the optimal offloading decisions. This paper proposes a novel efficient Data Size-Aware Offloading Model (DSAOM) for data-intensive applications and to predict the appropriate resource provider for dynamic resource allocation in mobile cloud computing. Based on the data size, the tasks are separated and gradually allocated to the appropriate resource providers for execution. The task is placed into the appropriate resource provider by considering the availability services in the fog nodes or the cloud. The tasks are split into smaller portions for execution in the neighbor fog nodes. To execute the task in the remote side, the offloading decision is made by using the min-cut algorithm by considering the monetary cost of the mobile device. This proposed system achieves low-latency time 13.2% and low response time 14.1% and minimizes 24% of the energy consumption over the existing model. Finally, according to experimental findings, this framework efficiently lowers energy use and improves performance for data-intensive demanding application activities, and the task offloading strategy is effective for intensive offloading requests.     

基于模拟退火机制的车辆用户移动边缘计算任务卸载新方法

在车联网(IOV)环境中,如果将车辆的计算任务都放置在云平台执行,无法满足对于信息处理的实时性,考虑移动边缘计算技术以及任务卸载策略,将用户的计算任务卸载到靠近设备边缘的服务器去执行。但是在密集的环境下,如果所有的任务都卸载到附近的边缘服务器去执行,同样会给边缘服务器带来巨大的负载。该文提出基于模拟退火机制的车辆用户移动边缘计算任务卸载新方法,通过定义用户的任务计算卸载效用,综合考虑时耗和能耗,结合模拟退火机制,根据当前道路的密集程度对系统卸载效用进行优化,改变用户的卸载决策,选择在本地执行或者卸载到边缘服务器上执行,使得在给定的环境下的所有用户都能得到满足低时延高质量的服务。仿真结果表明,该算法在减少用户任务计算时间的同时降低了能量消耗。     

Deep reinforcement learning-based joint task offloading and bandwidth allocation for multi-user mobile edge computing

The rapid growth of mobile internet services has yielded a variety of computation-intensive applications such as virtual/augmented reality. Mobile Edge Computing (MEC), which enables mobile terminals to offload computation tasks to servers located at the edge of the cellular networks, has been considered as an efficient approach to relieve the heavy computational burdens and realize an efficient computation offloading. Driven by the consequent requirement for proper resource allocations for computation offloading via MEC, in this paper, we propose a Deep-Q Network (DQN) based task offloading and resource allocation algorithm for the MEC. Specifically, we consider a MEC system in which every mobile terminal has multiple tasks offloaded to the edge server and design a joint task offloading decision and bandwidth allocation optimization to minimize the overall offloading cost in terms of energy cost, computation cost, and delay cost. Although the proposed optimization problem is a mixed integer nonlinear programming in nature, we exploit an emerging DQN technique to solve it. Extensive numerical results show that our proposed DQN-based approach can achieve the near-optimal performance.     

Research on adaptive dual task offloading decision algorithm for parking space recommendation service

In order to improve the efficiency of tasks processing and reduce the energy consumption of new energy vehicle (NEV), an adaptive dual task offloading decision-making scheme for Internet of vehicles is proposed based on information-assisted service of road side units (RSUs) and task offloading theory. Taking the roadside parking space recommendation service as the specific application Scenario, the task offloading model is built and a hierarchical self-organizing network model is constructed, which utilizes the computing power sharing among nodes, RSUs and mobile edge computing (MEC) servers. The task scheduling is performed through the adaptive task offloading decision algorithm, which helps to realize the available parking space recommendation service which is energy-saving and environmental-friendly. Compared with these traditional task offloading decisions, the proposed scheme takes less time and less energy in the whole process of tasks. Simulation results testified the effectiveness of the proposed scheme.     

Data offloading and task allocation for cloudlet-assisted ad hoc mobile clouds

Nowadays, although the data processing capabilities of the modern mobile devices are developed in a fast speed, the resources are still limited in terms of processing capacity and battery lifetime. Some applications, in particular the computationally intensive ones, such as multimedia and gaming, often require more computational resources than a mobile device can afford. One way to address such a problem is that the mobile device can offload those tasks to the centralized cloud with data centers, the nearby cloudlet or ad hoc mobile cloud. In this paper, we propose a data offloading and task allocation scheme for a cloudlet-assisted ad hoc mobile cloud in which the master device (MD) who has computational tasks can access resources from nearby slave devices (SDs) or the cloudlet, instead of the centralized cloud, to share the workload, in order to reduce the energy consumption and computational cost. A two-stage Stackelberg game is then formulated where the SDs determine the amount of data execution units that they are willing to provide, while the MD who has the data and tasks to offload sets the price strategies for different SDs accordingly. By using the backward induction method, the Stackelberg equilibrium is derived. Extensive simulations are conducted to demonstrate the effectiveness of the proposed scheme.     

Energy-efficient task offloading strategy in mobile edge computing for resource-intensive mobile applications

Mobile Edge Computing (MEC) has been considered a promising solution that can address capacity and performance challenges in legacy systems such as Mobile Cloud Computing (MCC). In particular, such challenges include intolerable delay, congestion in the core network, insufficient Quality of Experience (QoE), high cost of resource utility, such as energy and bandwidth. The aforementioned challenges originate from limited resources in mobile devices, the multi-hop connection between end-users and the cloud, high pressure from computation-intensive and delay-critical applications. Considering the limited resource setting at the MEC, improving the efficiency of task offloading in terms of both energy and delay in MEC applications is an important and urgent problem to be solved. In this paper, the key objective is to propose a task offloading scheme that minimizes the overall energy consumption along with satisfying capacity and delay requirements. Thus, we propose a MEC-assisted energy-efficient task offloading scheme that leverages the cooperative MEC framework. To achieve energy efficiency, we propose a novel hybrid approach established based on Particle Swarm Optimization (PSO) and Grey Wolf Optimizer (GWO) to solve the optimization problem. The proposed approach considers efficient resource allocation such as sub-carriers, power, and bandwidth for offloading to guarantee minimum energy consumption. The simulation results demonstrate that the proposed strategy is computational-efficient compared to benchmark methods. Moreover, it improves energy utilization, energy gain, response delay, and offloading utility.     

Hierarchical task offloading in heterogeneous cellular network:modeling and optimization

To improve the efficiency of computation offloading,a hierarchical task offloading framework based on device-to-device (D2D) communication,mobile edge computing and cloud computing was proposed,in which cooperative D2D relay technology was introduced to assist users to access remote computing resources.Considering the negative effects of uplink channel congestion,limited edge computing capability,D2D reuse interference and backhaul delay of cloud computing in the multi-user scenario of the proposed framework,a game theory based offloading scheduling and load balancing scheme was developed by fully taking advantage of the computing and communication resources in the proposed framework.The simulation results demonstrate that the proposed scheme can effectively reduce end-to-end delay and offloading energy consumption,and also has good stability even when the network resources are limited.     

A bandwidth allocation and energy-optimal transmission rate scheduling scheme in multi-services wireless networks

The energy-saving of mobile devices during their application offloading process has always been the research hotspot in the field of mobile cloud computing (MCC). In this paper, we focus on the scenario where multiple mobile devices with MCC and non-MCC services coexist. A bandwidth allocation and the corresponding transmission rate scheduling schemes are proposed with the objectives of simultaneously maximizing the overall system throughput and minimizing the energy consumption of individual mobile device with MCC service. To allocate the bandwidth to all mobile devices, two different algorithms are proposed, i.e., 0–1 integer programming algorithm and Lagrange dual algorithm. The transmission rate scheduling scheme for mobile device with MCC service is presented based on reverse order iteration method. The numerical results suggest that energy consumed by individual mobile device with MCC service can be remarkably saved while the overall system throughput can also be maximized. Moreover, the results show that 0–1 integer programming algorithm can get greater system throughput but has higher computational complexity, which means the algorithm is more suitable for small-scale systems, whereas Lagrange dual algorithm can achieve a good balance between the performance and computational complexity.     

基于拉格朗日的计算迁移能耗优化策略

随着移动网络技术的发展和智能终端的普及应用,移动边缘计算已成为云计算的一个重要应用。计算迁移策略已成为移动边缘计算服务的关键问题之一。以移动终端总的计算时间和移动终端能耗最小化为目标,将移动终端的计算迁移资源划分问题建模为一个凸优化问题,运用拉格朗日乘子法进行求解,提出基于阈值的迁移优化策略模型。仿真实验表明,本迁移优化策略模型能有效平衡本地计算和迁移计算之间的关系,为移动边缘计算中执行计算密集型应用提供保障。     

OKRA: optimal task and resource allocation for energy minimization in mobile edge computing systems

To cope with the computational and energy constraints of mobile devices, Mobile Edge Computing (MEC) has recently emerged as a new paradigm that provides IT and cloud-computing services at mobile network edge in close proximity to mobile devices. This paper investigates the energy consumption problem for mobile devices in a multi-user MEC system with different types of computation tasks, random task arrivals, and unpredictable channel conditions. By jointly considering computation task scheduling, CPU frequency scaling, transmit power allocation and subcarrier bandwidth assignment, we formulate it as a stochastic optimization problem aiming at minimizing the power consumption of mobile devices and to maintain the long-term stability of task queues. By leveraging the Lyapunov optimization technique, we propose an online control algorithm (OKRA) to solve the formulation. We prove that this algorithm is able to provide deterministic worst-case latency guarantee for latency-sensitive computation tasks, and balance a desirable tradeoff between power consumption and system stability by appropriately tuning the control parameter. Extensive simulations are carried out to verify the theoretical analysis, and illustrate the impacts of critical parameters to algorithm performance.

     

Multiuser computation offloading for edge-cloud collaboration using submodular optimization

A computation offloading scheme based on edge-cloud computing was proposed to improve the system utility of multiuser computation offloading.This scheme improved the system utility while considering the optimization of edge-cloud resources.In order to tackle the problems of computation offloading mode selection and edge-cloud resource allocation,a greedy algorithm based on submodular theory was developed by fully exploiting the computing and communication resources of cloud and edge.The simulation results demonstrate that the proposed scheme effectively reduces the delay and energy consumption of computing tasks.Additionally,when computing tasks are offloaded to edge and cloud from devices,the proposed scheme still maintains stable system utilities under ultra-limited resources.     

车载边缘计算中任务卸载和服务缓存的联合智能优化

针对车载环境下有限的网络资源和大量用户需求之间的矛盾,提出了智能驱动的车载边缘计算网络架构,以实现网络资源的全面协同和智能管理.基于该架构,设计了任务卸载和服务缓存的联合优化机制,对用户任务卸载以及计算和缓存资源的调度进行了建模.鉴于车载网络的动态、随机和时变的特性,利用异步分布式强化学习算法,给出了最优的卸载决策和资...     

A genetic‐based decision algorithm for multisite computation offloading in mobile cloud computing

Mobile cloud computing is a promising approach to improve the mobile device's efficiency in terms of energy consumption and execution time. In this context, mobile devices can offload the computation‐intensive parts of their applications to powerful cloud servers. However, they should decide what computation‐intensive parts are appropriate for offloading to be beneficial instead of local execution on the mobile device. Moreover, in the real world, different types of clouds/servers with heterogeneous processing speeds are available that should be considered for offloading. Because making offloading decision in multisite context is an NP‐complete, obtaining an optimal solution is time consuming. Hence, we use a near optimal decision algorithm to find the best‐possible partitioning for offloading to multisite clouds/servers. We use a genetic algorithm and adjust it for multisite offloading problem. Also, genetic operators are modified to reduce the ineffective solutions and hence obtain the best‐possible solutions in a reasonable time. We evaluated the efficiency of the proposed method using graphs of real mobile applications in simulation experiments. The evaluation results demonstrate that our proposal outperforms other counterparts in terms of energy consumption, execution time, and weighted cost model.     

基于二次规划的任务卸载决策和资源分配方法

主要研究移动用户均有多个独立任务的多用户移动云计算系统,这些移动用户将任务卸载到云端时共享通信资源。如何对所有用户的任务卸载决策和通信资源分配进行联合优化,以便使所有用户的能耗、计算量和延时降到最低是目前研究的难点。将该问题建模为NP难度的非凸的具有二次约束的二次规划(QCQP)问题,提出一种高效的近似算法进行求解,通过单独的半正定松驰(SDR)处理后,确定二元卸载决策和通信资源最优分配。采用代表最小系统成本的性能下界作为性能基准进行仿真实验,结果表明,本文算法在多种参数配置下的性能均接近最优性能。     

Offloading framework for computation service in the edge cloud and core cloud: A case study for face recognition

A fast rate of progress has allowed the proliferation of smartphones and eased their extensive presence in people's daily life. However, low processing speed and limited battery capacity have hindered improvements in the smartphone's computational capabilities. Offloading computational tasks to the cloud could solve this problem by enabling users to access these services over the Internet. Edge cloud computing has been recognized as an emerging field within the cloud computing paradigm, where computation servers are situated at the edge of the Internet to reduce network delay and traffic. Nevertheless, offloading tasks to the cloud is not always beneficial due to variable network conditions and increased processing costs. In this paper, a deep reinforcement learning-based offloading framework has been presented that provides smartphones with the ability to make decisions for local processing in the smartphone or to offload processing tasks to the cloud (edge and/or core). Thus, a smartphone can minimize the combination of the processing time, energy consumption, and monetary cost and maximize the accuracy of face recognition as well. Simulation results under synthetic scenarios show that the proposed offloading framework can effectively adapt to the dynamic cloud computing and networking environment.     

基于元强化学习的自适应卸载方法

计算卸载是移动边缘网络中的一个关键问题,基于深度学习的算法为高效生成卸载策略提供了一种解决方法。但考虑到移动终端设备的动态性以及不同任务场景之间的转换,需要大量的训练数据和较长的训练时间重新训练神经网络模型,即这些方法对新环境的适应能力较弱。针对这些不足,提出了一种基于元强化学习(Meta Reinforcement Learning, MRL)的自适应卸载方法,先对外部模型进行预训练,处理具体任务时再基于外部模型训练内部模型。该方法能快速适应具有少量梯度更新的样本的新环境。仿真实验表明,该算法能够适应新的任务场景,效果良好。