Collaboration among mobile agents for efficient energy allocation in mobile grid

The use of mobile devices in grid environments may have two interaction aspects: devices are considered as users of grid resources or as grid resources providers. Due to the limitation constraints on energy and processing capacity of mobile devices, their integration into the Grid is difficult. In this paper, we investigate the cooperation among mobile devices to balance the energy consumption and computation workloads. Mobile devices can have different roles such as buyer devices and seller devices. In the mobile grid, the energies of mobile devices are uneven, energy-poor devices can exploit other devices with spare energy. Our model consists of two actors: A buyer device agent represents the benefits of mobile buyer device that intends to purchase energy from other devices. A seller device agent represents the profits of mobile seller device that is willing to sell spare energy to other devices. The objective of optimal energy allocation in mobile grid is to maximize the utility of the system without exceeding the energy capacity, expense budget and the deadline. A collaboration algorithm among mobile agents for efficient energy allocation is proposed. In the simulation, the performance evaluation of collaboration algorithm among mobile agents is conducted.     

Energy constrained resource allocation optimization for mobile grids

A mobile grid incorporates mobile devices into Grid systems. But mobile devices at present have severe limitations in terms of processing, memory capabilities and energy. Minimizing the energy usage in mobile devices poses significant challenges in mobile grids. This paper presents energy constrained resource allocation optimization for mobile grids. The goal of the paper is not only to reduce energy consumption, but also to improve the application utility in a mobile grid environment with a limited energy charge, ensuring battery lifetime and the deadlines of the grid applications. The application utility not only depends on its allocated resources including computation and communication resources, but also on the consumed energy, this leads to a coupled utility model, where the utilities are functions of allocated resources and consumed energy. Energy constrained resources allocation optimization is formulated as a utility optimization problem, which can be decomposed into two subproblems, the interaction between the two sub-problems is controlled through the use of a pricing variable. The paper proposes a price-based distributed energy constrained resources allocation optimization algorithm. In the simulation, the performance evaluation of our energy constrained resources allocation optimization algorithm is conducted.     

Controlling energy without compromising system performance in mobile grid environments

The challenges confronting in mobile grid systems are: limited CPU power, limited memory, small screen, short battery life, and intermittent disconnection. Considering all these limitations, this paper is targeted to control energy consumption without compromising system’s performance in mobile grid. In this paper, we focus on using the mobile devices on the mobile grid environment. Mobile devices can serve two important functions in mobile grid environment either as service consumer or as valuable service providers. The proposed approach is not only to reduce energy consumption, but also to improve system performance in mobile grid environment. Utility functions are used to express grid users’ requirements, resource providers’ benefit function and system’s objectives. Dynamic programming is used to optimize the total utility function of mobile grid. A distributed controlling energy algorithm in mobile grid environment is proposed which decomposes mobile grid system optimization problem into sub-problems. In order to verify the efficiency of the proposed algorithm, in the experiment, the performance evaluation of controlling energy algorithm is conducted.     

Joint contexts optimization for mobile grid

This paper presents joint contexts optimization in mobile grid. The paper describes device context information for context-aware services in the mobile device collaboration. The objective of the paper is to dynamically deliver services to mobile grid users according to current context of mobile grid environment. A utility function is used as objective function that expresses values for the current contexts. The optimization is carried out by the joint context parameter optimizer with respect to an objective function. A joint contexts optimization algorithm is proposed which decomposes mobile grid system optimization problem into sub-problems. In the experiment, the performance evaluation of joint contexts optimization algorithm is conducted.     

A resource aware software partitioning algorithm based on mobility constraints in pervasive grid environments

Pervasive computing suffers from resource limitations of mobile devices, while grid computing can utilize almost unlimited resources distributed in the whole Internet. The conjunction of such two paradigms generates a new promising one, called pervasive grid computing, where mobile users can use handheld devices to access abundant resources and services in the grid. In this paper, a novel software partitioning algorithm is presented, which is suitable for pervasive grid to optimally allocate software components between a mobile device and one or more servers, with the goal of saving the resources of mobile devices. The algorithm takes into account component mobility constraints to not only prevent violating execution requirements of the application, but also to fully exploit component mobility, replication and rebinding to conserve more resources as compared to previous works. Another distinguishing feature of the algorithm is its generality, which can be applied to minimize network bandwidth usage, response time and energy consumption, respectively or simultaneously. Extensive simulation results have demonstrated the validity and effectiveness of the proposed algorithm in various environments.     

基于移动云计算的计算迁移能效算法

移动云计算可以通过应用任务的计算迁移降低执行延时和改善移动设备能效,但面对多云站点选择时,迁移决策是NP问题。针对该问题,提出一种能效计算迁移算法。为了实现截止期限和预算约束下执行时间与代价的多目标优化,算法将优化过程分解为三步进行。首先根据用户对时间与代价参数的偏好,设计一种CTTPO算法对应用进行分割,生成迁移模块（云端站点执行）和非迁移模块（移动设备执行）;然后为了实现云端多站点间的迁移模块调度,设计一种基于教与学最优化方法的MTS算法,进而产生效率最优的应用调度解;最后设计一种基于动态电压缩放方法的ESM算法,通过多站点的性能缩放进一步降低应用执行能耗。通过两种随机应用结构图进行了仿真实验,实验结果证明,该算法在执行效率、执行代价以及执行能耗上要优于对比算法。     

具有负载均衡和蚁群优化的移动P2P路由策略

分析了移动P2P网络的移动节点设备资源更加短缺,网络更加动态多变,建立健壮的路由策略是非常重要的。通过研究移动P2P网络的特征,从路由发现、路由选择、路由保持三个方面入手,提出了一种新的具有负载均衡和蚁群优化的路由策略。利用蚁群算法理论来指导移动agent的全局搜索的路由发现工作,并且结合通用的能量消耗公式计算得到的节点剩余能量和节点业务执行等候队列长度来优化路由选择工作。从仿真实验可以看到,该路由策略在平均端到端的延迟、路由控制负载方面具有性能优势。同时,节约了节点的能量,延长了节点在网络中的生存时间。     

普适网格中移动设备的迁移策略

针对目前普适网格中移动设备在迁移过程中出现的任务执行的不连续性问题,提出了一种在网格环境中普适设备的迁移策略,实现了移动设备在所提交的网格任务不中止执行的情况下,能够根据当前的环境自动进行迁移。其中触发切换算法和目标选择算法使得普适设备在迁移过程中能够主动地选择有效的资源进行切换,任务的无缝迁移算法则达到了在迁移过程中普适设备提交的网格任务能够不间断执行的要求,并且通过理论与实验证明了该策略的有效性。     

Utility-based scheduling for grid computing under constraints of energy budget and deadline

With recent advances in computing and communication technologies enabling mobile devices more powerful, the scope of Grid computing has been broadened to include mobile and pervasive devices. Energy has become a critical resource in such devices. So, battery energy limitation is the main challenge towards enabling persistent mobile grid computing. In this paper, we address the problem of energy constrained scheduling scheme for the grid environment. There is a limited energy budget for grid applications. The paper investigates both energy minimization for mobile devices and grid utility optimization problem. We formalize energy aware scheduling using nonlinear optimization theory under constraints of energy budget and deadline. The paper also proposes distributed pricing based algorithm that is used to tradeoff energy and deadline to achieve a system wide optimization based on the preference of the grid user. The simulations reveal that the proposed energy constrained scheduling algorithms can obtain better performance than the previous approach that considers both energy consumption and deadline.     

A scheduling algorithm with dynamic properties in mobile grid

Mobile grid is a branch of grid computing that incorporates mobile devices into the grid infrastructure. It poses new challenges because mobile devices are typically resource-constrained and exhibit unique characteristics such as instability in network connections. New scheduling strategies are imperative in mobile grid to efficiently utilize the devices. This paper presents a scheduling algorithm that considers dynamic properties of mobile devices such as availability, reliability, maintainability, and usage pattern in mobile grid environments. In particular, usage patterns caused by voluntarily or involuntarily losing a connection, such as switching off the device or a network interruption could be important criteria for choosing the best resource to execute a job. The experimental results show that our scheduling algorithm provides superior performance in terms of execution time, as compared to the other methods that do not consider usage pattern. Throughout the experiments, we found it essential to consider usage pattern for improving performance in the mobile grid.     

Multi-device task offloading with time-constraints for energy efficiency in mobile cloud computing

Nowadays, in order to deal with the increasingly complex applications on mobile devices, mobile cloud offloading techniques have been studied extensively to meet the ever-increasing energy requirements. In this study, an offloading decision method is investigated to minimize the energy consumption of mobile device with an acceptable time delay and communication quality. In general, mobile devices can execute a sequence of tasks in parallel. In the proposed offloading decision method, only parts of the tasks are offloaded for task characteristics to save the energy of multi-devices. The issue of the offloading decision is formulated as an NP-hard 0–1 nonlinear integer programming problem with time deadline and transmission error rate constraints. Through decision-variable relaxation from the integer to the real domain, this problem can be transformed as a continuous convex optimization. Based on Lagrange duality and the Karush–Kuhn–Tucker condition, a solution with coupled terms is derived to determine the priority of tasks for offloading. Then, an iterative decoupling algorithm with high efficiency is proposed to obtain near-optimal offloading decisions for energy saving. Simulation results demonstrate that considerable energy can be saved via the proposed method in various mobile cloud scenarios.     

移动边缘计算中的端到端任务分配算法

为了提高移动应用程序的运行效率,移动边缘计算将部分任务从终端设备迁移到边缘云中计算来缩减应用程序的运行时间和终端设备的能耗。针对应用程序所需的总代价即能耗和时间两个目标进行了研究,提出一个移动边缘计算模型和基于贪心策略的快速算法（HGA）;构造了一个结合贪心策略的粒子群（HPSO）算法,进一步优化HGA的解。实验结果表明,与传统所有任务只在一个设备上执行和尽可能上传云端执行两种策略相比,提出的HGA总代价分别优化28.5%和9.1%;与HGA相比,HPSO算法总代价减少12.3%;即所提算法能有效减少系统的总代价,更加满足用户需求。     

网格资源发现中移动Agent路径优化的研究

在对动态网格资源发现模型研究的基础上,改进了移动Agent在网格环境中的动态巡游策略.分析和比较了路径优化算法,在遗传算法和蚁群算法融合的基础上,提出了基于Agent的网格资源发现机制中进行路径优化的新方法,解决移动Agent为完成用户指定的资源发现任务在网格动态环境中移动时的迁移路径问题.实验结果表明了该算法的可行性,适应网格的动态性,以及提高网格资源发现的效率.     

Qos-aware mobile service optimization in multi-access mobile edge computing environments

With the rapid development of mobile Internet technologies and various new service services such as virtual reality (VR) and augmented reality (AR), users’ demand for network quality of service (QoS) is getting higher and higher. To solve the problems of high load and low latency in-network services, this paper proposes a data caching strategy based on a multi-access mobile edge computing environment. Based on the MEC collaborative caching framework, an SDN controller is introduced into the MEC collaborative caching framework, a joint cache optimization mechanism based on data caching and computational migration is constructed, and the user-perceived time-lengthening problem in the data caching strategy is solved by a joint optimization algorithm based on an improved heuristic genetic algorithm and simulated annealing. Meanwhile, this paper proposes a multi-base station collaboration-based service optimization strategy to solve the problem of collaboration of computation and storage resources due to multiple mobile terminals and multiple smart base stations. For the problem that the application service demand in MEC server changes due to time, space, requests and other privacy, an application service optimization algorithm based on the Markov chain of service popularity is constructed, and a deep deterministic strategy (DDP) based on deep reinforcement learning is also used to minimize the average delay of computation tasks in the cluster while ensuring the energy consumption of MEC server, which improves the accuracy of application service cache updates in the system as well as reducing the complexity of service updates. The experimental results show that the proposed data caching algorithm weighs the cache space of user devices, the average transfer latency of acquiring data resources is effectively reduced, and the proposed service optimization algorithm can improve the quality of user experience.     

An efficient trust negotiation strategy towards the resource-limited mobile commerce environment

Automated trust negotiation (ATN) offers an attractivemeans for trust establishments, which establishesmutual trust among strangers wishing to share resources or conduct business, but it comes at the cost of non-trivial computation and communication overheads. The deployment of ATN strategies on a resource-constrained mobile device may lead to user-obstructive latency for operations. In this paper, we propose a trust negotiation strategy called trust target Petri nets negotiation strategy (TPNNS). It highly reduces the negotiation latency in the mobile device compared with other negotiation strategies, since it considers all the alternative responses at each step and chooses the best one. TPNNS supports cycle avoidance and employs skipped TPN which is a new approach presented in this paper. What is more, it is complete and ensures no irrelevant credentials are disclosed during the trust negotiation.     

基于在线双边拍卖的分层联邦学习激励机制

在分层联邦学习中,能量受限的移动设备参与模型训练会消耗自身资源.为了降低移动设备的能耗,文中在不超过分层联邦学习的最大容忍时间下,提出了移动设备能耗之和最小化问题.不同训练轮次的边缘服务器能够选择不同的移动设备,移动设备也能够为不同的边缘服务器并发训练模型,因此文中基于在线双边拍卖机制提出了ODAM-DS算法.基于最优...     

无线可充能传感器网络的锚点算法与移动设备调度研究

由于传感器的电池容量和存储容量有限,导致无法持续对传感器进行能量补充并收集传感器生成的感测数据。针对该问题,研究了周期性能量补充和数据收集问题,提出了一种用于充能和数据收集的方法,包括基于网格的算法(GBA)、基于支配集的算法(DSBA)和基于圆相交的算法(CIBA)。通过这三种方法或两两相结合的方法找到锚点集合,通过移动设备调度算法调度最小数量的移动设备来访问生成的锚点。仿真结果验证了所提方法的有效性。与联合能量数据采集(JEDA)算法、最小覆盖圆(SEC)算法相比,所提CIBA需要的移动设备数量最少,总移动距离也最短,具有良好的综合性能。     

移动云计算中的任务调度与计算迁移算法

随着互联网的发展,许多应用程序对计算机的计算能力和资源的需求越来越大,而移动设备具有有限的资源和计算能力,云计算迁移技术是解决计算密集型任务在移动端上顺利运行的主流方法。针对无线网络中联合调度和迁移的问题,提出了一个快速高效的启发式算法。算法将能够迁移的任务全部迁移到云端作为初始解,然后逐次计算可迁移任务在移动端运行的能耗节省量,依次将节省量最大的任务迁移到移动端。每迁移一个任务,该算法都会依据任务间的通信时间,及时更新各个任务的能耗节省量。为了进一步优化启发式算法得到的解,还构造了适用于此问题并以启发解为初始解的模拟退火算法,给出了相应的编码方法、目标函数、邻域解、温度参数以及算法终止准则。与无迁移、饱和迁移、随机迁移三类算法的对比实验结果表明,由启发式算法得出的解具有高效性,能给出使移动端能耗更小的解。     

分层学习的边缘计算资源调度粒子群优化算法

由于存在诸如CPU运算速度慢,电池容量低等问题,智能移动设备本身无法执行计算需求大的应用程序,需要借助边缘计算技术来降低程序对移动设备硬件的要求。然而将部分计算任务从移动设备传输给边缘服务器,会带来额外的传输能耗和服务器计算能耗。综合考虑影响移动设备和服务器,以及数据传输能耗值的四个因素,即移动设备的计算速度,下载数据功耗,数据卸载百分比和剩余网络带宽占,提出一种基于分层学习的粒子群算法,优化每台移动设备对于这四个参数的取值,更合理分配计算资源使得总能耗最小。对计算资源建模时,还考虑了最大能耗、计算周期、存储、带宽和延迟约束条件。与其他算法进行对比实验发现,通过分层学习优化的粒子群算法,能更快速地获得满足约束条件具有更低能耗的资源调度最优解。     

优化网络生命周期和最短化路径的WSN移动sink路径规划算法

为了缓解无线传感器网络（WSN）中传感器节点分布不均匀、传感器节点感知数据量不同而造成能耗不均衡、"热区"等问题,提出一种优化网络生命周期和最短化路径的WSN移动sink路径规划算法（MSPPA）。首先,通过监测区域网格化,在每个网格内分布若干个移动sink候选访问站点,sink在每个网格中选择一个站点停留收集网格中节点数据;然后,分析所有传感器节点的生命周期与sink站点选择的关系,建立权衡网络生命周期和sink移动路径的优化模型;最后,使用双链遗传算法规划移动sink遍历网格的顺序和选择每个网格中移动sink访问站点,得到移动sink节点遍历所有网格收集数据的路径。仿真结果显示,与已有的低功耗自适应分簇（LEACH）算法与基于移动sink节点与集合节点（RN）的优化LEACH分簇算法（MS-LEACH-RN）相比,MSPPA在网络生命周期方面提高了60%,且具有良好的能耗均衡性。实验结果表明,MSPPA能有效缓解能量不均衡、"热区"问题,延长网络生命周期。