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.     

Survey on computation offloading in mobile edge computing

Computation offloading in mobile edge computing would transfer the resource intensive computational tasks to the edge network.It can not only solve the shortage of mobile user equipment in resource storage,computation performance and energy efficiency,but also deal with the problem of resource occupation,high latency and network load compared to cloud computing.Firstly the architecture of MEC was introduce and a comparative analysis was made according to various deployment schemes.Then the key technologies of computation offloading was studied from three aspects of decision on computation offloading,allocation of computing resource within MEC and system implement of MEC.Based on the analysis of MEC deployment scheme in 5G,two optimization schemes on computation offloading was proposed in 5G MEC.Finally,the current challenges in the mobility management was summarized,interference management and security of computation offloading in MEC.     

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.     

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.     

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.     

移动边缘计算卸载技术综述

移动边缘计算(Mobile Edge Computing,MEC)将云服务器的计算资源扩展到更靠近用户一侧的网络边缘,使得用户可以将任务卸载到边缘服务器,从而克服原先云计算中将任务卸载到云服务器所带来的高时延问题。首先介绍了移动边缘计算的基本概念、基本框架和应用场景,然后围绕卸载决策、联合资源分配的卸载决策分别从单MEC服务器和多MEC服务器两种场景总结了任务卸载技术的研究现状,最后结合当前MEC卸载技术中存在的不足展望了未来MEC卸载技术的研究。     

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。     

Learning for Smart Edge: Cognitive Learning-Based Computation Offloading

With the development of intelligent applications, more and more intelligent applications are computation intensive, data intensive and delay sensitive. Compared with traditional cloud computing, edge computing can reduce communication delay by offloading computing tasks to edge cloud. Furthermore, with the complexity of computing scenarios in edge cloud, deep learning based on computation offloading scheme has attracted wide attention. However, all the learning-based offloading scheme does not consider the where and how to run the offloading scheme itself. Thus, in this paper, we consider the problem of running the learning-based computation offloading scheme for the first time and propose the learning for smart edge architecture. Then, we give the computation offloading optimization problem of mobile devices under multi-user and multi edge cloud scenarios. Furthermore, we propose cognitive learning-based computation offloading (CLCO) scheme for this problem. Finally, experimental results show that compared with other offloading schemes, the CLCO scheme has lower task duration and energy consumption.

     

Joint optimization of energy and delay for computation offloading in cloudlet-assisted mobile cloud computing

Wireless Networks - In the mobile cloud computing (MCC), although offloading requests to the distant central cloud or nearby cloudlet can reduce energy consumption at the mobile devices (MDs), it...     

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.     

基于终端能耗和系统时延最小化的边缘计算卸载及资源分配机制

通过移动边缘计算下移云端的应用功能和处理能力支撑计算密集或时延敏感任务的执行成为当前的发展趋势。但面对众多移动终端用户时,如何有效利用计算资源有限的边缘节点来保障终端用户服务质量(QoS)成为关键问题。为此,该文融合边缘云与远端云构建了一种分层的边缘云计算架构,以此架构为基础,以最小化移动设备能耗和任务执行时间为目标,将问题形式化描述为资源约束下的最小化能耗和时延加权和的凸优化问题,并提出基于乘子法的计算卸载及资源分配机制解决该问题。实验结果表明,在计算任务量很大的情况下,提出的计算卸载及资源分配机制能够有效降低移动终端能耗,并在任务执行时延方面较局部计算与计算卸载机制分别降低最高60%与10%,提高系统性能。     

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。     

A Distributed and Elastic Application Processing Model for Mobile Cloud Computing

The latest developments in mobile computing technology have increased the computing capabilities of smart mobile devices (SMDs). However, SMDs are still constrained by low bandwidth, processing potential, storage capacity, and battery lifetime. To overcome these problems, the rich resources and powerful computational cloud is tapped for enabling intensive applications on SMDs. In Mobile Cloud Computing (MCC), application processing services of computational clouds are leveraged for alleviating resource limitations in SMDs. The particular deficiency of distributed architecture and runtime partitioning of the elastic mobile application are the challenging aspects of current offloading models. To address these issues of traditional models for computational offloading in MCC, this paper proposes a novel distributed and elastic applications processing (DEAP) model for intensive applications in MCC. We present an analytical model to evaluate the proposed DEAP model, and test a prototype application in the real MCC environment to demonstrate the usefulness of DEAP model. Computational offloading using the DEAP model minimizes resources utilization on SMD in the distributed processing of intensive mobile applications. Evaluation indicates a reduction of 74.6% in the overhead of runtime application partitioning and a 66.6% reduction in the CPU utilization for the execution of the application on SMD.

     

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.     

Enabling intelligence in fog computing to achieve energy and latency reduction

Fog computing is an emerging architecture intended for alleviating the network burdens at the cloud and the core network by moving resource-intensive functionalities such as computation, communication, storage, and analytics closer to the End Users (EUs). In order to address the issues of energy efficiency and latency requirements for the time-critical Internet-of-Things (IoT) applications, fog computing systems could apply intelligence features in their operations to take advantage of the readily available data and computing resources. In this paper, we propose an approach that involves device-driven and human-driven intelligence as key enablers to reduce energy consumption and latency in fog computing via two case studies. The first one makes use of the machine learning to detect user behaviors and perform adaptive low-latency Medium Access Control (MAC)-layer scheduling among sensor devices. In the second case study on task offloading, we design an algorithm for an intelligent EU device to select its offloading decision in the presence of multiple fog nodes nearby, at the same time, minimize its own energy and latency objectives. Our results show a huge but untapped potential of intelligence in tackling the challenges of fog computing.     

Optimal Task Assignment in Mobile Cloud Computing by Queue Based Ant-Bee Algorithm

Wireless Personal Communications - Mobile cloud computing (MCC) broadens the mobile devices capability by offloading tasks to the ‘cloud’. Hence, offloading numerous tasks...     

A Novel Multi-Objective Efficient Offloading Decision Framework in Cloud Computing for Mobile Computing Applications

Wireless Personal Communications - Mobile cloud computing is the emerging paradigm to improve mobile device computation issues using cloud resources. Computation offloading is an efficient way of...     

Joint computation offloading and task caching for multi-user and multi-task MEC systems: reinforcement learning-based algorithms

Computation offloading at mobile edge computing (MEC) servers can mitigate the resource limitation and reduce the communication latency for mobile devices. Thereby, in this study, we proposed an offloading model for a multi-user MEC system with multi-task. In addition, a new caching concept is introduced for the computation tasks, where the application program and related code for the completed tasks are cached at the edge server. Furthermore, an efficient model of task offloading and caching integration is formulated as a nonlinear problem whose goal is to reduce the total overhead of time and energy. However, solving these types of problems is computationally prohibitive, especially for large-scale of mobile users. Thus, an equivalent form of reinforcement learning is created where the state spaces are defined based on all possible solutions and the actions are defined on the basis of movement between the different states. Afterwards, two effective Q-learning and Deep-Q-Network-based algorithms are proposed to derive the near-optimal solution for this problem. Finally, experimental evaluations verify that our proposed model can substantially minimize the mobile devices’ overhead by deploying computation offloading and task caching strategy reasonably.

     

Energy‐aware dynamic task offloading and collective task execution in mobile cloud computing

There is a good opportunity for enlightening the services of the mobile devices by introducing computational offloading using cloud technology. Offloading is a process for managing the complexity of the mobile environment by migrating computational load to the cloud. The mobile devices oblige the quick response for the offloading requests; it is dependent on network connectivity. The cloud services take long set‐up time irrespective of network connectivity. In this paper, new system architecture for the dynamic task offloading in the mobile cloud environment is proposed. The architecture includes the offloading algorithm that concentrates on energy consumption of the tasks both in the local and remote environment. The proposed algorithm formulates a collective task execution model for minimizing the energy consumption. The architecture concentrates on the network model by considering the task completion time in three different network scenarios. The experimental results show the efficiency of the suggested architecture in reducing the energy consumption and completion time of the tasks.     

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.