Energy-aware task scheduling in mobile cloud computing

The limited energy supply, computing, storage and transmission capabilities of mobile devices pose a number of challenges for improving the quality of service (QoS) of various mobile applications, which has stimulated the emergence of many enhanced mobile computing paradigms, such as mobile cloud computing (MCC), fog computing, mobile edge computing (MEC), etc. The mobile devices need to partition mobile applications into related tasks and decide which tasks should be offloaded to remote computing facilities provided by cloud computing, fog nodes etc. It is very important yet tough to decide which tasks to be uploaded and where they are scheduled since this could greatly impact the applications’ timeliness and mobile devices’ lifetime. In this paper, we model the task scheduling problem at the end-user mobile device as an energy consumption optimization problem, while taking into account task dependency, data transmission and other constraint conditions such as task deadline and cost. We further present several heuristic algorithms to solve it. A series of simulation experiments are conducted to evaluate the performance of the algorithms and the results show that our proposed algorithms outperform the state-of-the-art algorithms in energy efficiency as well as response time.     

Mobile Cloud Middleware

Mobile Cloud Computing (MCC) is arising as a prominent research area that is seeking to bring the massive advantages of the cloud to the constrained smartphones. Mobile devices are looking towards cloud-aware techniques, driven by their growing interest to provide ubiquitous PC-like functionality to mobile users. These functionalities mainly target at increasing storage and computational capabilities. Smartphones may integrate those functionalities from different cloud levels, in a service oriented manner within the mobile applications, so that a mobile task can be delegated by direct invocation of a service. However, developing these kind of mobile cloud applications requires to integrate and consider multiple aspects of the clouds, such as resource-intensive processing, programmatically provisioning of resources (Web APIs) and cloud intercommunication. To overcome these issues, we have developed a Mobile Cloud Middleware (MCM) framework, which addresses the issues of interoperability across multiple clouds, asynchronous delegation of mobile tasks and dynamic allocation of cloud infrastructure. MCM also fosters the integration and orchestration of mobile tasks delegated with minimal data transfer. A prototype of MCM is developed and several applications are demonstrated in different domains. To verify the scalability of MCM, load tests are also performed on the hybrid cloud resources. The detailed performance analysis of the middleware framework shows that MCM improves the quality of service for mobiles and helps in maintaining soft-real time responses for mobile cloud applications.     

Adaptive Service Management in Mobile Cloud Computing by Means of Supervised and Reinforcement Learning

Since the concept of merging the capabilities of mobile devices and cloud computing is becoming increasingly popular, an important question is how to optimally schedule services/tasks between the device and the cloud. The main objective of this article is to investigate the possibilities for using machine learning on mobile devices in order to manage the execution of services within the framework of Mobile Cloud Computing. In this study, an agent-based architecture with learning possibilities is proposed to solve this problem. Two learning strategies are considered: supervised and reinforcement learning. The solution proposed leverages, among other things, knowledge about mobile device resources, network connection possibilities and device power consumption, as a result of which a decision is made with regard to the place where the task in question is to be executed. By employing machine learning techniques, the agent working on a mobile device gains experience in determining the optimal place for the execution of a given type of task. The research conducted allowed for the verification of the solution proposed in the domain of multimedia file conversion and demonstrated its usefulness in reducing the time required for task execution. Using the experience gathered as a result of subsequent series of tests, the agent became more efficient in assigning the task of multimedia file conversion to either the mobile device or cloud computing resources.     

Efficient application scheduling in mobile cloud computing based on MAX–MIN ant system

Combining the advantages of mobile computing and cloud computing, Mobile Cloud Computing (MCC) greatly enriches the types of applications on mobile devices and enhances the quality of service of the applications. Under various circumstances, researchers have put forward several MCC architectures. However, it still remains a challenging task of how to design a reasonable mobile cloud model with efficient application processing structure for some particular environment. This paper firstly presents a Hybrid Local Mobile Cloud Model (HLMCM) with detailed application scheduling structure. Secondly, a scheduling algorithm for HLMCM based on MAX–MIN Ant System is put forward. Finally, the effectiveness and suitability of our proposed algorithms are evaluated through a series of simulation experiments.     

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.     

基于延迟敏感应用的边云协同方案

智能设备存在着存储能力以及计算能力不足的问题,导致无法满足计算密集型和延迟敏感型应用的服务质量要求。边缘计算和云计算被认为是解决智能设备局限性的有效方法。为了有效利用边云资源,并在延迟和服务失败概率方面提供良好的服务质量,首先提出了一种三层计算系统框架,然后考虑到边缘服务器的异构性和任务的延迟敏感性,在边缘层提出了一种高效的资源调度策略。三层计算系统框架可以根据应用程序的延迟敏感性提供计算资源和传输时延,保证了边缘资源的有效利用以及任务的实时性。仿真结果验证了所提资源调度策略的有效性,并表明该调度算法优于现有传统方法。     

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.     

DaaS: Cloud-based mobile Web service discovery

The proliferation of smartphones and the recent advancement in ubiquitous wireless access have made mobile Web services more possible than ever before. However, finding relevant Web services that can match requests and fit user context remains a major concern. The challenges facing Web service discovery are further magnified by the stringent constraints of mobile devices and the inherit complexity of wireless heterogeneous networks. Cloud computing, with its flexible design and theoretically unlimited computing resources, is a viable approach to bootstrapping Web service discovery. The cloud can build bridges between mobile devices, as a convenient ubiquitous interface, and a backbone infrastructure with abundant computing resources. This paper introduces “Discovery as a service (Daas)”, a novel cloud-based discovery framework that addresses the core components of mobile Web service discovery. The DaaS framework lays the foundation of efficient mobile Web service discovery that takes into consideration user preferences and context. The experimental validation and performance evaluation demonstrate that DaaS can effectively rank relevant services according to the various user context and preferences, in addition to enhancing the precision of the discovered services. The prototype also shows that Web service clustering for discovery significantly improves the overall response time, while the cloud maintains scalability according to prespecified performance criteria.     

Joint optimization method for task scheduling time and energy consumption in mobile cloud computing environment

An unheard of growth in mobile data traffic has drawn attention from academia and industry. Mobile cloud computing is an emerging computing paradigm combining cloud computing and mobile networks to alleviate resource-constrained limitations of mobile devices, which can greatly improve network quality of service and efficiency to make good use of available network resource. Mobile cloud computing not only inherits the advantages of strong computing capacity and massive storage of cloud computing, but also overcomes the time and geographical restrictions, bringing benefits for mobile users to offload complex computation to powerful cloud servers for execution anytime and anywhere. To this end, an optimal task workflow scheduling scheme is proposed for the mobile devices, based on the dynamic voltage and frequency scaling technique and the whale optimization algorithm. Through considering three factors: task execution position, task execution sequence, and operating voltage and frequency of mobile devices, this study makes a tradeoff between performance and energy consumption by solving the joint optimization for task completion time and energy consumption simultaneously. Finally, a series of extensive simulation results has demonstrated and verified the scheme has distinguished performance in terms of efficiency and operational cost, providing feasible solutions to similar optimization problems of mobile cloud computing.     

基于ILS-PSO算法的移动云计算DAG图的任务调度研究与应用

移动云计算已经深入到人们工作和生活的各个方面,同时也对移动设备的续航时间、计算能力,存储容量和安全性提出了更高的要求。移动云计算网络中的移动设备由于资源有限、通信受限,无法满足复杂应用的要求。为了解决移动云计算环境下复杂应用的有效使用问题,对移动设备网络和DAG任务图进行深入研究,将复杂应用分解成多个不相交的集合分配给移动设备并行执行,满足移动设备电池容量的约束下,提出了粒子群优化(PSO)算法求解最优调度方案的方法,并且应用迭代局部搜索(ILS)策略,保证了全局和局部搜索的平衡。     

面向边缘设备的高能效深度学习任务调度策略

近年来,深度学习在图像和自然语言处理等诸多领域表现出色,与深度学习相关的各类移动应用发展迅速,但由于移动网络状态的不稳定性及网络带宽的限制,基于云计算的深度模型任务可能出现较大响应延迟,严重影响用户体验.与此同时,深度模型对设备的计算及存储能力有较高的要求,无法直接在资源受限的移动设备中进行部署.因此,亟须设计一种新的计算模式,使得基于深度模型的移动应用能够满足用户对快速响应、低能耗及高准确率的期望.本文提出一种面向边缘设备的深度模型分类任务调度策略,该策略通过协同移动设备与边缘服务器,充分利用智能移动终端的便捷性和边缘服务器强大的计算能力,综合考虑分类任务的复杂度和用户期望,完成深度模型在移动设备和边缘服务器中的动态部署,并对推理任务进行动态调度,从而提升任务执行效率,降低深度学习模型推理开销.本文以基于卷积神经网络的图像识别应用为例,实验结果表明,在移动环境中,相比于准确率最高的深度模型,本文提出的高能效调度策略的推理能耗可降低93.2%、推理时间降低91.6%,同时准确率提升3.88%.     

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.     

SNA based QoS and reliability in fog and cloud framework

Fog and Cloud computing are ubiquitous computing paradigms based on the concepts of utility and grid computing. Cloud service providers permit flexible and dynamic access to virtualized computing resources on pay-per-use basis to the end users. The users having mobile device will like to process maximum number of applications locally by defining fog layer to provide infrastructure for storage and processing of applications. In case demands for resources are not being satisfied by fog layer of mobile device then job is transferred to cloud for processing. Due to large number of jobs and limited resources, fog is prone to deadlock at very large scale. Therefore, Quality of Service (QoS) and reliability are important aspects for heterogeneous fog and cloud framework. In this paper, Social Network Analysis (SNA) technique is used to detect deadlock for resources in fog layer of mobile device. A new concept of free space fog is proposed which helps to remove deadlock by collecting available free resource from all allocated jobs. A set of rules are proposed for a deadlock manager to increase the utilization of resources in fog layer and decrease the response time of request in case deadlock is detected by the system. Two different clouds (public cloud and virtual private cloud) apart from fog layer and free space fog are used to manage deadlock effectively. Selection among them is being done by assigning priorities to the requests and providing resources accordingly from fog and cloud. Therefore, QoS as well as reliability to users can be provided using proposed framework. Cloudsim is used to evaluate resource utilization using Resource Pool Manager (RPM). The results show the effectiveness of proposed technique.     

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.     

A Cloud-Manager-Based Re-Encryption Scheme for Mobile Users in Cloud Environment: a Hybrid Approach

Cloud computing is an emerging computing paradigm that offers on-demand, flexible, and elastic computational and storage services for the end-users. The small and medium-sized business organization having limited budget can enjoy the scalable services of the cloud. However, the migration of the organizational data on the cloud raises security and privacy issues. To keep the data confidential, the data should be encrypted using such cryptography method that provides fine-grained and efficient access for uploaded data without affecting the scalability of the system. In mobile cloud computing environment, the selected scheme should be computationally secure and must have capability for offloading computational intensive security operations on the cloud in a trusted mode due to the resource constraint mobile devices. The existing manager-based re-encryption and cloud-based re-encryption schemes are computationally secured and capable to offload the computationally intensive data access operations on the trusted entity/cloud. Despite the offloading of the data access operations in manager-based re-encryption and cloud-based re-encryption schemes, the mobile user still performs computationally intensive paring-based encryption and decryption operations using limited capabilities of mobile device. In this paper, we proposed Cloud-Manager-based Re-encryption Scheme (CMReS) that combines the characteristics of manager-based re-encryption and cloud-based re-encryption for providing the better security services with minimum processing burden on the mobile device. The experimental results indicate that the proposed cloud-manager-based re-encryption scheme shows significant improvement in turnaround time, energy consumption, and resources utilization on the mobile device as compared to existing re-encryption schemes.     

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.     

Towards an autonomic performance management approach for a cloud broker environment using a decomposition–coordination based methodology

Efficient resource allocation of computational resources to services is one of the predominant challenges in a cloud computing environment. Furthermore, the advent of cloud brokerage and federated cloud computing systems increases the complexity of cloud resource management. Cloud brokers are considered third party organizations that work as intermediaries between the service providers and the cloud providers. Cloud brokers rent different types of cloud resources from a number of cloud providers and sublet these resources to the requesting service providers. In this paper, an autonomic performance management approach is introduced that provides dynamic resource allocation capabilities for deploying a set of services over a federated cloud computing infrastructure by considering the availability as well as the demand of the cloud computing resources. A distributed control based approach is used for providing autonomic computing features to the proposed framework via a feedback-based control loop. This distributed control based approach is developed using one of the decomposition–coordination methodologies, named interaction balance, for interactive bidding of cloud computing resources. The primary goals of the proposed approach are to maintain the service level agreements, maximize the profit, and minimize the operating cost for the service providers and the cloud broker. The application of interaction balance methodology and prioritization of profit maximization for the cloud broker and the service providers during resource allocation are novel contributions of the proposed approach.     

RETRACTED ARTICLE: Simulator considering modeling and performance evaluation for high-performance computing of collaborative-based mobile cloud infrastructure

In recent years, as IT technology has progressed, mobile devices have been created that enable various manual tasks to be automated and portable. A variety of mobile devices has computing, storage, and Internet capabilities and can handle many tasks. When miniaturized mobile devices perform tasks that require a large amount of computing resources due to limited computing and storage, there is a delay in operation and a non-operation state. Therefore, collaborative-based mobile cloud infrastructure (MCI) research is being conducted to provide computing services composed of mobile devices. Computation off-loading studies have been conducted for MCI’s high-performance computing, but it is difficult to build various mobile infrastructures and verify algorithm performance. In addition, performance verification is performed in a predetermined MCI environment or is carried out through small-scale test equipment. This causes waste of time, cost, and manpower for constructing the environment. Various studies have been conducted for this purpose, but there is a difficulty in performance verification and analysis since only the results are displayed or outputted in text form. In this paper, we propose a mobile cloud infrastructure simulator (MCIS) for computing off-loading, resource management, mobile deployment, and mobile information for MCI. MCIS enables user tasks, resource allocation methods, and various mobile device performance settings. In addition, visualization of the operating state makes it easy to analyze the performance of the user, and it is possible to grasp the problems that occur during operation.

     

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.