An Approach to Automatic Performance Prediction for Cloud-Enhanced Mobile Applications with Sparse Data

In mobile cloud computing (MCC), offloading compute-intensive parts of a mobile application onto the cloud is an attractive method to enhance application performance. To make good offloading decisions, history-based machinelearning techniques are proposed to predict application performance under various offloading schemes. However, the data sparsity problem is common in a realistic MCC scenario but is rarely the concern of existing work. In this paper, we employ a two-phase hybrid framework to predict performance for cloud-enhanced mobile applications, which is designed to be robust to the data sparsity. By training several multi-layer neural networks with historical execution records, the first phase automatically predicts some intermediate parameters for each execution of an application. The models learned by these neural networks can be shared among different applications, thus alleviating the data sparsity. Based on these predicted intermediate parameters and the application topology, the second phase deterministically calculates the estimated values of the performance metrics. The deterministic algorithm can partially guarantee the prediction accuracy of newly published applications even with no execution records. We evaluate our approach with a cloud-enhanced object recognition application and show that our approach can precisely predict the application performance and is robust to data sparsity.     

MobiByte: An Application Development Model for Mobile Cloud Computing

Mobile cloud computing presents an effective solution to overcome smartphone constraints, such as limited computational power, storage, and energy. As the traditional mobile application development models do not support computation offloading, mobile cloud computing requires novel application development models that can facilitate the development of cloud enabled mobile applications. This paper presents a mobile cloud application development model, named MobiByte, to enhance mobile device applications’ performance, energy efficiency, and execution support. MobiByte is a context-aware application model that uses multiple data offloading techniques to support a wide range of applications. The proposed model is validated using prototype applications and detailed results are presented. Moreover, MobiByte is compared with the most recent application models with a conclusion that it outperforms the existing application models in many aspects like energy efficiency, performance, generality, context awareness, and privacy.     

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。     

Energy-efficient multisite offloading policy using Markov decision process for mobile cloud computing

Mobile systems, such as smartphones, are becoming the primary platform of choice for a user’s computational needs. However, mobile devices still suffer from limited resources such as battery life and processor performance. To address these limitations, a popular approach used in mobile cloud computing is computation offloading, where resource-intensive mobile components are offloaded to more resourceful cloud servers. Prior studies in this area have focused on a form of offloading where only a single server is considered as the offloading site. Because there is now an environment where mobile devices can access multiple cloud providers, it is possible for mobiles to save more energy by offloading energy-intensive components to multiple cloud servers. The method proposed in this paper differentiates the data- and computation-intensive components of an application and performs a multisite offloading in a data and process-centric manner. In this paper, we present a novel model to describe the energy consumption of a multisite application execution and use a discrete time Markov chain (DTMC) to model fading wireless mobile channels. We adopt a Markov decision process (MDP) framework to formulate the multisite partitioning problem as a delay-constrained, least-cost shortest path problem on a state transition graph. Our proposed Energy-efficient Multisite Offloading Policy (EMOP) algorithm, built on a value iteration algorithm (VIA), finds the efficient solution to the multisite partitioning problem. Numerical simulations show that our algorithm considers the different capabilities of sites to distribute appropriate components such that there is a lower energy cost for data transfer from the mobile to the cloud. A multisite offloading execution using our proposed EMOP algorithm achieved a greater reduction on the energy consumption of mobiles when compared to a single site offloading execution.     

移动云环境面向多重服务选择的计算卸载算法

移动云计算可以通过计算卸载改善移动设备的能效和应用的执行延时。然而面对云端的多重服务选择时,计算卸载决策是NP问题。为了解决这一问题,提出一种遗传算法寻找计算卸载的最优应用分割决策解。遗传种群初始化中,算法联立预定义和随机染色体方法进行初始种群的生成,减少了无效染色体的发生比例。同时,算法为预定义的预留种群设计一种特定的基于汉明距离函数的适应度函数,更好地衡量了染色体间的差异。种群交叉中分别利用近亲交配与杂交繁育丰富了种群个体。算法通过修正的遗传操作减少了无效解的产生,以更合理的时间代价获得了应用分割的最优可行解。应用现实的移动应用任务图进行仿真实验评估了算法效率。评估结论表明,所设计的遗传算法在应用执行能耗、执行时间以及综合权重代价方面均优于对比算法。     

云边环境下基于博弈论的两阶段任务迁移策略

移动边缘计算（MEC）为计算密集型应用和资源受限的移动设备之间的冲突提供了有效解决办法,但大多关于MEC迁移的研究仅考虑移动设备与MEC服务器之间的资源分配,忽略了云计算中心的巨大计算资源。为了充分利用云和MEC资源,提出一种云边协作的任务迁移策略。首先,将云边服务器的任务迁移问题转化为博弈问题;然后,证明该博弈中纳什均衡（NE）的存在以及唯一性,并获得博弈问题的解决方案;最后,提出了一种基于博弈论的两阶段任务迁移算法来求解任务迁移问题,并通过性能指标对该算法的性能进行了评估。仿真结果表明,采用所提算法所产生的总开销分别比本地执行、云中心服务器执行和MEC服务器执行的总开销降低了72.8%、47.9%和2.65%,数值结果证实了所提策略可以实现更高的能源效率和更低的任务迁移开销,并且随着移动设备数量的增加可以很好地扩展规模。     

Optimization techniques to enable execution offloading for 3D video games

Nowadays, mobile devices are becoming the most popular computing device as their computing capabilities increase rapidly. However, it is still challenging to execute highly sophisticated applications such as 3D video games on mobile devices due to its constrained key computational resources. Execution offloading approaches have been proposed to resolve this problem by strengthening mobile devices with powerful cloud. Unfortunately, the existing offloading approaches are not suitable for 3D video games because of the unique execution characteristics of them. In this paper, we propose a streaming-based execution offloading framework to enable execution offloading for 3D video games. The experiments show that our framework successfully guarantees 20 frames per second for our benchmark.     

边缘计算中协作计算卸载与动态任务调度

在边缘计算场景中,通过将部分待执行任务卸载到边缘服务器执行能够达到降低移动设备的负载、提升移动应用性能和减少设备开销的目的.对于时延敏感任务,只有在截止期限内完成才具有实际意义.但是边缘服务器的资源往往有限,当同时接收来自多个设备的数据传输及处理任务时,可能造成任务长时间的排队等待,导致部分任务因超时而执行失败,因此无法兼顾多个设备的性能目标.鉴于此,在计算卸载的基础上优化边缘服务器端的任务调度顺序.一方面,将时延感知的任务调度建模为一个长期优化问题,并使用基于组合多臂赌博机的在线学习方法动态调整服务器的调度顺序.另一方面,由于不同的任务执行顺序会改变任务卸载性能提升程度,因而影响任务卸载决策的有效性.为了增加卸载策略的鲁棒性,采用了带有扰动回报的深度Q学习方法决定任务执行位置.仿真算例证明了该策略可在平衡多个用户目标的同时减少系统的整体开销.     

移动边缘计算中资源受限的串行任务卸载策略

云计算和移动互联网的不断融合，促进了移动云计算的产生和发展，但是其难以满足终端应用对带宽和延迟的需求.移动边缘计算在靠近用户的网络边缘提供计算和存储能力，通过计算卸载，将终端任务迁移至边缘服务器上面执行，能够有效降低应用延迟和节约终端能耗.然而，目前针对移动边缘环境任务卸载的主要工作大多考虑单个移动终端和边缘服务器资源无限的场景，这在实际应用中存在一定的局限性.因此，针对边缘服务器资源受限下的任务卸载问题，提出了一种面向多用户的串行任务动态卸载策略（multi-user serial task dynamic offloading strategy，简称MSTDOS）.该策略以应用的完成时间和移动终端的能量消耗作为评价指标，遵循先来先服务的原则，采用化学反应优化算法求解，充分考虑多用户请求对服务器资源的竞争关系，动态调整选择策略，为应用做出近似最优的卸载决策.仿真结果表明，MSTDOS策略比已有算法能够取得更好的应用性能.     

A study on virtual machine deployment for application outsourcing in mobile cloud computing

In mobile cloud computing, application offloading is implemented as a software level solution for augmenting computing potentials of smart mobile devices. VM is one of the prominent approaches for offloading computational load to cloud server nodes. A challenging aspect of such frameworks is the additional computing resources utilization in the deployment and management of VM on Smartphone. The deployment of Virtual Machine (VM) requires computing resources for VM creation and configuration. The management of VM includes computing resources utilization in the monitoring of VM in entire lifecycle and physical resources management for VM on Smartphone. The objective of this work is to ensure that VM deployment and management requires additional computing resources on mobile device for application offloading. This paper analyzes the impact of VM deployment and management on the execution time of application in different experiments. We investigate VM deployment and management for application processing in simulation environment by using CloudSim, which is a simulation toolkit that provides an extensible simulation framework to model the simulation of VM deployment and management for application processing in cloud-computing infrastructure. VM deployment and management in application processing is evaluated by analyzing VM deployment, the execution time of applications and total execution time of the simulation. The analysis concludes that VM deployment and management require additional resources on the computing host. Therefore, VM deployment is a heavyweight approach for process offloading on smart mobile devices.     

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。     

Pirax: framework for application piracy control in mobile cloud environment

Mobile cloud computing is an emerging technology that is gaining popularity as a means to extend the capabilities of resource-constrained mobile devices such as a smartphone. Mobile cloud computing requires specialized application development models that support computation offloading from a mobile device to the cloud. The computation offloading is performed by means of offloading application process, application component, entire application, or clone of the smartphone. The offloading of an entire application or clone of the smartphone to cloud may raise application piracy issues, which, unfortunately, have not been addressed in the existing literature. This paper presents a piracy control framework for mobile cloud environment, named Pirax, which prevents mobile applications from executing on unauthenticated devices and cloud resources. Pirax is formally verified using High Level Petri Nets, Satisfiability Modulo Theories Library and Z3 solver. Pirax is implemented on Android platform and analyzed from security and performance perspectives. The performance analysis results show that Pirax is lightweight and easy to integrate into existing mobile cloud application development models.     

Social-aware hybrid mobile offloading

Mobile offloading is a promising technique to aid the constrained resources of a mobile device. By offloading a computational task, a device can save energy and increase the performance of the mobile applications. Unfortunately, in existing offloading systems, the opportunistic moments to offload a task are often sporadic and short-lived. We overcome this problem by proposing a social-aware hybrid offloading system (HyMobi), which increases the spectrum of offloading opportunities. As a mobile device is always co-located to at least one source of network infrastructure throughout of the day, by merging cloudlet, device-to-device and remote cloud offloading, we increase the availability of offloading support. Integrating these systems is not trivial. In order to keep such coupling, a strong social catalyst is required to foster user’s participation and collaboration. Thus, we equip our system with an incentive mechanism based on credit and reputation, which exploits users’ social aspects to create offload communities. We evaluate our system under controlled and in-the-wild scenarios. With credit, it is possible for a device to create opportunistic moments based on user’s present need. As a result, we extended the widely used opportunistic model with a long-term perspective that significantly improves the offloading process and encourages unsupervised offloading adoption in the wild.     

Smart mobile computation offloading: Centralized selective and multi-objective approach

Although mobile devices have been considerably upgraded to more powerful terminals, yet their lightness feature still impose intrinsic limitations in their computation capability, storage capacity and battery lifetime. With the ability to release and augment the limited resources of mobile devices, mobile cloud computing has drawn significant research attention allowing computations to be offloaded and executed on remote resourceful infrastructure. Nevertheless, circumstances like mobility, latency, applications execution overload and mobile device state; any can affect the offloading decision, which might dictate local execution for some tasks and remote execution for others. We present in this article a novel system model for computations offloading which goes beyond existing works with smart centralized, selective, and optimized approach. The proposition consists of (1)hotspots selection mechanism to minimize the overhead of the offloading evaluation process yet without jeopardizing the discovery of the optimal processing environment of tasks, (2)a multi-objective optimization model that considers adaptable metrics crucial for minimizing device resource usage and augmenting its performance, and (3)a tailored centralized decision maker that uses genetics to intelligently find the optimal distribution of tasks. The scalability, overhead and performance of the proposed hotspots selection mechanism and hence its effect on the decision maker and tasks dissemination are evaluated. The results show its ability to notably reduce the evaluation cost while the decision maker was able in turn to maintain optimal dissemination of tasks. The model is also evaluated and the experiments prove its competency over existing models with execution speedup and significant reduction in the CPU usage, memory consumption and energy loss.     

移动云计算中能效感知的计算卸载机制研究

移动云计算可以将任务从移动设备计算卸载至云端以增强设备计算能力,而如何实现能效计算卸载机制是当前的主要挑战。为了解决该问题,以降低移动设备能耗和应用完成时间为目标,将计算卸载问题形式化为满足任务顺序与截止时间约束的能效代价最小化问题,并提出一种动态能效感知计算卸载算法。算法由三个子算法组成：计算卸载选择、时钟频率控制及传输功率分配。实验结果表明,通过局部计算时优化调整移动设备CPU时钟频率,以及云端计算时自适应分配传输功率,新算法可以有效降低应用执行能效代价,同时确保满足约束条件,提高执行效率。     

Application-aware cloudlet selection for computation offloading in multi-cloudlet environment

Latency- and power-aware offloading is a promising issue in the field of mobile cloud computing today. To provide latency-aware offloading, the concept of cloudlet has evolved. However, offloading an application to the most appropriate cloudlet is still a major challenge. This paper has proposed an application-aware cloudlet selection strategy for multi-cloudlet scenario. Different cloudlets are able to process different types of applications. When a request comes from a mobile device for offloading a task, the application type is verified first. According to the application type, the most suitable cloudlet is selected among multiple cloudlets present near the mobile device. By offloading computation using the proposed strategy, the energy consumption of mobile terminals can be reduced as well as latency in application execution can be decreased. Moreover, the proposed strategy can balance the load of the system by distributing the processes to be offloaded in various cloudlets. Consequently, the probability of putting all loads on a single cloudlet can be dealt for load balancing. The proposed algorithm is implemented in the mobile cloud computing laboratory of our university. In the experimental analyses, the sorting and searching processes, numerical operations, game and web service are considered as the tasks to be offloaded to the cloudlets based on the application type. The delays involved in offloading various applications to the cloudlets located at the university laboratory, using proposed algorithm are presented. The mathematical models of total power consumption and delay for the proposed strategy are also developed in this paper.     

基于光纤-无线网络的协同计算卸载算法

随着无源光网络的发展,光纤-无线网络能同时支持集中式云和边缘云计算技术,成为一种具有发展前景的网络结构。但是,现有的基于光纤-无线网络的任务协同计算卸载研究主要以最小化移动设备的能耗为目标,忽略了实时性高的任务的需求。针对实时性高的任务,提出了以最小化任务的总处理时间为目标的集中式云和边缘云协同计算卸载问题,并对其进行形式化描述。同时,通过将该问题归约为装箱问题,从而证明其为NP难解问题。提出一个启发式协同计算卸载算法,该算法通过比较不同卸载策略的任务处理时间,优先选择时间最短的任务卸载策略。同时,提出一个定制的遗传算法,获得一个更优的任务卸载策略。实验结果表明,与现有的算法相比,本文提出的启发式算法得到的任务卸载策略平均减少4.34%的任务总处理时间,而定制的遗传算法的卸载策略平均减少18.41%的任务总处理时间。同时,定制的遗传算法的卸载策略与本文提出的启发式算法相比平均减少14.49%的任务总处理时间。     

Case of ARM emulation optimization for offloading mechanisms in Mobile Cloud Computing

The Mobile Cloud Computing (MCC) paradigm depends on efficient offloading of computation from the resource constrained mobile device to the resource rich cloud server. The computational offloading is assisted by system virtualization, application virtualization, and process state migration. However, system and application virtualization techniques force unnecessary overhead on applications that require offloading to the cloud and applications that do not. Moreover, smartphones and cloud data centers are based on heterogeneous processor architectures, such as, ARM and x86. As a result, process migrated from a smartphone needs translation or emulation on the cloud server. Therefore, instruction emulation is a necessary criterion for a comprehensive MCC framework. In this paper, we evaluate the overhead of the system and application virtualization techniques and emulation frameworks that enable MCC offloading mechanisms. We find that the overhead of system and application virtualization can be as high as 4.51% and 55.18% respectively for the SciMark benchmark. Moreover, ARM to Intel device emulation overhead can be as high as 55.53%. We provide a proof of concept of emulation speedup by utilizing efficient Single Instruction, Multiple Data (SIMD) translations. We conclude that the overhead of virtualization and emulation techniques need to be reduced for efficient MCC offloading frameworks.     

Multiobjective computation offloading for workflow management in cloudlet‐based mobile cloud using NSGA‐II

Cloudlet is a novel computing paradigm, introduced to the mobile cloud service framework, which moves the computing resources closer to the mobile users, aiming to alleviate the communication delay between the mobile devices and the cloud platform and optimize the energy consumption for mobile devices. Currently, the mobile applications, modeled by the workflows, tend to be complicated and computation‐intensive. Such workflows are required to be offloaded to the cloudlet or the remote cloud platform for execution. However, it is still a key challenge to determine the offloading resolvent for the deadline‐constrained workflows in the cloudlet‐based mobile cloud, since a cloudlet often has limited resources. In this paper, a multiobjective computation offloading method, named MCO, is proposed to address the above challenge. Technically, an energy consumption model for the mobile devices is established in the cloudlet‐based mobile cloud. Then, a corresponding computation offloading method, by improving Nondominated Sorting Genetic Algorithm II, is designed to achieve the goal of energy saving for all the mobile device while satisfying the deadline constraints of the workflows. Finally, extensive experimental evaluations are conducted to demonstrate the efficiency and effectiveness of our proposed method.