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.     

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 lightweight active service migration framework for computational offloading in mobile cloud computing

Cloud computing enables access to the widespread services and resources in cloud datacenters for mitigating resource limitations in low-potential client devices. Computational cloud is an attractive platform for computational offloading due to the attributes of scalability and availability of resources. Therefore, mobile cloud computing (MCC) leverages the application processing services of computational clouds for enabling computational-intensive and ubiquitous mobile applications on smart mobile devices (SMDs). Computational offloading frameworks focus on offloading intensive mobile applications at different granularity levels which involve resource-intensive mechanism of application profiling and partitioning at runtime. As a result, the energy consumption cost (ECC) and turnaround time of the application is increased. This paper proposes an active service migration (ASM) framework for computational offloading to cloud datacenters, which employs lightweight procedure for the deployment of runtime distributed platform. The proposed framework employs coarse granularity level and simple developmental and deployment procedures for computational offloading in MCC. ASM is evaluated by benchmarking prototype application on the Android devices in the real MCC environment. It is found that the turnaround time of the application reduces up to 45 % and ECC of the application reduces up to 33 % in ASM-based computational offloading as compared to traditional offloading techniques which shows the lightweight nature of the proposed framework for computational offloading.     

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.     

Towards Mobile Cloud Computing with Single Sign-on Access

The low computing power of mobile devices impedes the development of mobile applications with a heavy computing load. Mobile Cloud Computing (MCC) has emerged as the solution to this by connecting mobile devices with the “infinite” computing power of the Cloud. As mobile devices typically communicate over untrusted networks, it becomes necessary to secure the communications to avoid privacy-sensitive data breaches. This paper presents work on implementing MCC applications with secure communications. For that purpose, we built on COMPSs-Mobile, a redesigned implementation of the COMP Superscalar (COMPSs) framework aiming to MCC platorms. COMPSs-Mobile automatically exploits the parallelism inherent in an application and orchestrates its execution on loosely-coupled distributed environment. To avoid a vendor lock-in, this extension leverages on the Generic Security Services Application Program Interface (GSSAPI) (RFC2743) as a generic way to access security services to provide communications with authentication, secrecy and integrity. Besides, GSSAPI allows applications to take profit of more advanced features, such as Federated Identity or Single Sign-On, which the underlying security framework could provide. To validate the practicality of the proposal, we use Kerberos as the security services provider to implement SSO; however, applications do not authenticate themselves and require users to obtain and place the credentials beforehand. To evaluate the performance, we conducted some tests running an application on a smartphone offloading tasks to a private cloud. Our results show that the overhead of securing the communications is acceptable.     

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.     

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。     

Precise execution offloading for applications with dynamic behavior in mobile cloud computing

In order to accommodate the high demand for performance in smartphones, mobile cloud computing techniques, which aim to enhance a smartphone’s performance through utilizing powerful cloud servers, were suggested. Among such techniques, execution offloading, which migrates a thread between a mobile device and a server, is often employed. In such execution offloading techniques, it is typical to dynamically decide what code part is to be offloaded through decision making algorithms. In order to achieve optimal offloading performance, however, the gain and cost of offloading must be predicted accurately for such algorithms. Previous works did not try hard to do this because it is usually expensive to make an accurate prediction. Thus in this paper, we introduce novel techniques to automatically generate accurate and efficient method-wise performance predictors for mobile applications and empirically show they enhance the performance of offloading.     

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。     

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.     

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。     

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.     

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.     

Automatic offloading of mobile applications into the cloud by means of genetic programming

The limited battery life of modern mobile devices is one of the key problems limiting their use. Even if the offloading of computation onto cloud computing platforms can considerably extend battery duration, it is really hard not only to evaluate the cases where offloading guarantees real advantages on the basis of the requirements of the application in terms of data transfer, computing power needed, etc., but also to evaluate whether user requirements (i.e. the costs of using the cloud services, a determined QoS required, etc.) are satisfied. To this aim, this paper presents a framework for generating models to make automatic decisions on the offloading of mobile applications using a genetic programming (GP) approach. The GP system is designed using a taxonomy of the properties useful to the offloading process concerning the user, the network, the data and the application. The fitness function adopted permits different weights to be given to the four categories considered during the process of building the model. Experimental results, conducted on datasets representing different categories of mobile applications, permit the analysis of the behavior of our algorithm in different applicative contexts. Finally, a comparison with the state of the art of the classification algorithm establishes the goodness of the approach in modeling the offloading process.     

Investigation on runtime partitioning of elastic mobile applications for mobile cloud computing

The latest developments in mobile computing technology have increased the computing capabilities of smartphones in terms of storage capacity, features support such as multimodal connectivity, and support for customized user applications. Mobile devices are, however, still intrinsically limited by low bandwidth, computing power, and battery lifetime. Therefore, the computing power of computational clouds is tapped on demand basis for mitigating resources limitations in mobile devices. Mobile cloud computing (MCC) is believed to be able to leverage cloud application processing services for alleviating the computing limitations of smartphones. In MCC, application offloading is implemented as a significant software level solution for sharing the application processing load of smartphones. The challenging aspect of application offloading frameworks is the resources intensive mechanism of runtime profiling and partitioning of elastic mobile applications, which involves additional computing resources utilization on Smart Mobile Devices (SMDs). This paper investigates the overhead of runtime application partitioning on SMD by analyzing additional resources utilization on SMD in the mechanism of runtime application profiling and partitioning. We evaluate the mechanism of runtime application partitioning on SMDs in the SmartSim simulation environment and validate the overhead of runtime application profiling by running prototype application in the real mobile computing environment. Empirical results indicate that additional computing resources are utilized in runtime application profiling and partitioning. Hence, lightweight alternatives with optimal distributed deployment and management mechanism are mandatory for accessing application processing services of computational clouds.     

Executing mobile applications on the cloud: Framework and issues

Modern mobile devices, such as smartphones and tablets, have made many pervasive computing dreams come true. Still, many mobile applications do not perform well due to the shortage of resources for computation, data storage, network bandwidth, and battery capacity. While such applications can be re-designed with client–server models to benefit from cloud services, the users are no longer in full control of the application, which has become a serious concern for data security and privacy. In addition, the collaboration between a mobile device and a cloud server poses complex performance issues associated with the exchange of application state, synchronization of data, network condition, etc. In this work, a novel mobile cloud execution framework is proposed to execute mobile applications in a cloud-based virtualized execution environment controlled by mobile applications and users, with encryption and isolation to protect against eavesdropping from cloud providers. Under this framework, several efficient schemes have been developed to deal with technical issues for migrating applications and synchronizing data between execution environments. The communication issues are also addressed in the virtualization execution environment with probabilistic communication Quality-of-Service (QoS) technique to support timely application migration.     

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.     

Evaluation of Remote-I/O Support for a DSM-Based Computation Offloading Scheme

Computation offloading enables mobile devices to execute rich applications by using the abundant computing resources of powerful server systems. The distributed shared memory based (DSM-based) computation offloading approach is expected to be especially popular in the near future because it can dynamically migrate running threads to computing nodes and does not require any modifications of existing applications to do so. The current DSM-based computation offloading scheme, however, has focused on efficiently offloading computationally intensive applications and has not considered the significant performance degradation caused by processing the I/O requests issued by offloaded threads. Because most mobile applications are interactive and thus yield frequent I/O requests, efficient handling of I/O operations is critically important. In this paper, we quantitatively analyze the performance degradation caused by I/O processing in DSM-based computation offloading schemes using representative commodity applications. To remedy the performance degradation, we apply a remote I/O scheme based on remote device support to computation offloading. The proposed approach improves the execution time by up to 43.6% and saves up to 17.7% of energy consumption in comparison with the existing offloading schemes. Selective compression of the remote I/O scheme reduces the network traffic by up to 53.5%.     

An energy-efficient scheduling scheme for time-constrained tasks in local mobile clouds

Mobile Cloud Computing (MCC) enables mobile devices to use resource providers other than mobile devices themselves to host the execution of mobile applications. Various mobile cloud architectures and scheduling algorithms have been studied recently. However, how to utilize MCC to enable mobile devices to run complex real-time applications while keeping high energy efficiency remains a challenge. In this paper, firstly, we introduce the local mobile clouds formed by nearby mobile devices and give the mathematical models of the mobile devices and their applications. Secondly, we formulate the scheduling problem in local mobile clouds. After describing the resource discovery scheme and the adaptive, probabilistic scheduling algorithm, we finally validate the performance of the proposed algorithm by simulation experiments.     

C2OF2N: a low power cooperative code offloading method for femtolet-based fog network

Power and delay aware cloud service provisioning to mobile devices has become a promising domain today. This paper proposes and implements a cooperative offloading approach for indoor mobile cloud network. In the proposed work mobile devices register under femtolet which is a home base station with computation and data storage facilities. The resources of the mobile devices are collaborated in such a way that different mobile devices can execute different types of computations based on cooperative federation. The proposed offloading scheme is referred as cooperative code offloading in femtolet-based fog network. If none of the mobile device can execute the requested computation, then femtolet executes the computation. Use of femtolet provides the mobile devices voice call service as well as cloud service access. Femtolet is used as the fog device in our approach. The proposed model is simulated using Qualnet version 7. The simulation results demonstrate that the proposed scheme minimizes the energy by 15% and average delay up to 12% approximately than the existing scheme. Hence, the proposed model is referred as a low power offloading approach.