Mobile Cloud Computing Model Research Articles

An Incentive Mechanism for Computation Offloading in Satellite-Terrestrial Internet of Vehicles

With the development of satellite-terrestrial technology and the popularity of the Internet of Vehicles, how to improve the efficiency of mobile cloud computing (MCC) has become the next concern. However, the resource of cloudlets is not sufficient to perform large-scale computation tasks, or some applications designed to run on vehicles have more efficiency executed on vehicles than executed on cloudlets. Additionally, it is still challenging for platforms to motivate mobile vehicle owners to join in the process while the existing mechanisms cannot provide all the desired properties in cloudlet scenarios. To this end, we design a satellite-terrestrial IoV based on an incentive mechanism for computation offloading (IMCO) in mobile edge computing to motivate vehicle owners to perform computation offloading tasks so as to offload certain kinds of tasks to the mobile vehicles. By optimizing the MCC model, we integrate auction theory into the mechanism to ensure individual rationality, budget balance, system efficiency, and truthfulness for both sellers and buyers. Through rigorous theoretical analysis, we prove that our mechanism can achieve computational efficiency under the condition that all algorithm outputs be computed in polynomial time. Both theoretical derivations and numerical calculations prove that all the desired properties of the mechanism hold.

Improving the impact of power efficiency in mobile cloud applications using cloudlet model

SummaryThe applications and services of Information and Communication Technologies are becoming a very essential part of our daily life. In addition, the spread of advanced technologies including the cloud and mobile cloud computing (MCC), wireless communication, and smart devices made it easy to access the internet and utilize unlimited number of services. For example, we use mobile applications to carry out critical tasks in hospitals, education, finance, and many others. This wide useful usage makes the smart devices an essential component of our daily life. The limited processing capacity and battery lifetime of mobile devices are considered main challenges. This challenge is increased when executing intensive applications. The MCC is believed to overcome these limitations. There are many models in MCC and one efficient model is the cloudlet‐based computing. In this model, the mobile devices users communicate with the cloudlets using cheaper efficient technologies, and offload the job requests to be executed on the cloudlet rather than on the enterprise cloud or on the device itself. In this article, we investigated the cloudlet‐based MCC architecture, and more specifically, the cooperative cloudlets model. In this model, the applications that require intensive computations such as image processing are offloaded from the mobile device to the nearest cloudlet. If the task cannot be accomplished at this cloudlet, the cloudlets cooperate with each other to accomplish the user request and send the results back to the user. To demonstrate the efficiency of this cooperative cloudlet‐based MCC model, we conducted real experiments that execute selected applications such as: object code recognition, and array sorting to measure the delay and power consumption of the cloudlet‐based system. Moreover, suitable cloud/mobile cloud simulators such as CloudSim and MCCSim will be used to perform simulation experiments and obtain time and power results.

A Survey of Cloudlet-Based Mobile Augmentation Approaches for Resource Optimization

Mobile devices (MDs) face resource scarcity challenges owing to limited energy and computational resources. Mobile cloud computing (MCC) offers a resource-rich environment to MDs for offloading compute-intensive tasks encountering resource scarcity challenges. However, users are unable to exploit its full potential owing to challenges of distance, limited bandwidth, and seamless connectivity between the remote cloud (RC) and MDs in the conventional MCC model. The cloudlet-based solution is widely used to address these challenges. The response of the cloudlet-based solution is faster than the conventional mobile cloud-computing model, rendering it suitable for the Internet of Things (IoT) and Smart Cities (SC). However, with the increase in devices and workloads, the cloudlet-based solution has to deal with resource-scarcity challenges, thus, forwarding the requests to remote clouds. This study has been carried out to provide an insight into existing cloudlet-based mobile augmentation (CtMA) approaches and highlights the underlying limitations for resource optimization. Furthermore, numerous performance parameters have been identified and their detailed comparative analysis has been used to quantify the efficiency of CtMA approaches.

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

Mobile cloud computing (MCC) broadens the mobile devices capability by offloading tasks to the ‘cloud’. Hence, offloading numerous tasks simultaneously increases the ‘cloudlets’ load and augments the average completion duration of the offloaded tasks. To withstand this issue, we propose a hybrid Queue Ant Colony-Artificial Bee Colony Optimization (Ant-Bee) algorithm for optimal assignment of tasks in MCC environment. The proposed algorithm works on a two-way MCC model with offloading technique, that considers of both the ‘cloudlets’ and the public ‘cloud’. The ‘cloud’ and the ‘cloudlets’ are designed on the basis of queue model for the estimation of clients waiting time in the limitation of resources. The major concern of the proposed algorithm is to offload the tasks by identifying the accurate place preferably in a ‘cloud/cloudlet’. The ‘cloud/cloudlet’ is encompassed by a queue model with the end goal to minimize the drop rate by permitting the tasks to wait in the queue. It also aims for the optimal assignment of tasks to manage the ‘cloudlets’ load and to minimize the entire tasks average completion time. The performance of the proposed algorithm is analyzed with few Queue based conventional algorithms such as, “Round Robin”, “Weighted Round Robin” and “Random”. From the simulation result, it is analyzed that our proposed algorithm outperforms in the power consumption of the mobile devices, the average completion time of tasks, and drop rate. Also, to ensure the efficiency of our proposed hybrid QAnt-Bee algorithm, it is contrasted with the “HACAS” application scheduling algorithm, which fails to consider queue in the ‘cloudlets’.

A self-protecting agents based model for high-performance mobile-cloud computing

Mobile-cloud computing (MCC) allows devices with resource and battery limitations to achieve computation-intensive tasks in real-time. While this new paradigm of computing seems beneficial for real-time mobile computing, existing MCC models mainly rely on keeping full clones of program code at remote sites and unstandardized/uninteroperable environments, hampering wider adoption. Moreover, the security risks arising from offloading data and code to an untrusted platform and the computational overhead introduced by complex security mechanisms stand as deterrents for adoption of MCC at large. In this paper, we present a context-dependent computation-offloading model for MCC, which is based on application segments packed into autonomous agents. This approach only requires isolated execution containers in the cloud to provide a runtime environment for the agents, and minimal involvement of the mobile platform during the computation process. The agents in the proposed model are able to protect themselves from tampering using integrity-checkpointing and an authenticated encryption-based communication mechanism. Experiments with two mobile applications demonstrate the effectiveness of the approach for high-performance, secure MCC.

The Value of Cooperation: Minimizing User Costs in Multi-Broker Mobile Cloud Computing Networks

We study the problem of user cost minimization in mobile cloud computing (MCC) networks. We consider a MCC model where multiple brokers assign cloud resources to mobile users. The model is characterized by an heterogeneous cloud architecture (which includes a public cloud and a cloudlet) and by the heterogeneous pricing strategies of cloud service providers. In this setting, we investigate two classes of cloud reservation strategies, i.e., a competitive strategy, and a compete-then-cooperate strategy as a performance bound. We first study a purely competitive scenario where brokers compete to reserve computing resources from remote public clouds (which are affected by long delays) and from local cloudlets (which have limited computational resources but short delays). We provide theoretical results demonstrating the existence of disagreement points (i.e., the equilibrium reservation strategy that no broker has incentive to deviate unilaterally from) and convergence of the best-response strategies of the brokers to disagreement points. We then consider the scenario in which brokers agree to cooperate in exchange for a lower average cost of resources. We formulate a cooperative problem where the objective is to minimize the total average price of all brokers, under the constraint that no broker should pay a price higher than the disagreement price (i.e., the competitive price). We design new globally optimal solution algorithm to solve the resulting non-convex cooperative problem, based on a combination of the branch and bound framework and of advanced convex relaxation techniques . The resulting optimal solution provides a lower bound on the achievable user cost without complete collusion among brokers. Compared with pure competition, we found that (i) noticeable cooperative gains can be achieved over pure competition in markets with a few brokers only, and (ii) the cooperative gain is only marginal in crowded markets, i.e., with a high number of brokers, hence there is no clear incentive for brokers to cooperate.

Models for Multimedia Mobile Cloud in Smart Cities

Realizing efficient models that are capable of handling massive amounts of mobile multimedia data is an essential component of achieving the vision of smart future cities. In order for a model to be truly effective, it can not be based solely on improving the hardware of mobile devices. Instead, utilizing software solutions and tools provided by the emerging field of Mobile Cloud Computing (MCC) provides results that could not be otherwise possible. In MCC, parts of mobile applications are executed in remote servers that provide powerful computational resources. As such, the performance of mobile devices is significantly increased since the heavy computational load is being handled in the cloud. In this paper, we provide a comprehensive analysis and comparisons of current MCC models that are meant to increase mobile devices performance and utility. In particular, we provide a critical discussion related to the applicability of such models, and we compare these models using a devised set of criteria that affect the performance and reliability of effective Mobile Cloud Application models.

Development of a Secure Mobile Cloud Computing Model for Universities Services

Development of a Secure Mobile Cloud Computing Model for Universities Services

Mobile Cloud Computing Model and Big Data Analysis for Healthcare Applications

Mobile devices are increasingly becoming an indispensable part of people’s daily life, facilitating to perform a variety of useful tasks. Mobile cloud computing integrates mobile and cloud computing to expand their capabilities and benefits and overcomes their limitations, such as limited memory, CPU power, and battery life. Big data analytics technologies enable extracting value from data having four Vs: volume, variety, velocity, and veracity. This paper discusses networked healthcare and the role of mobile cloud computing and big data analytics in its enablement. The motivation and development of networked healthcare applications and systems is presented along with the adoption of cloud computing in healthcare. A cloudlet-based mobile cloud-computing infrastructure to be used for healthcare big data applications is described. The techniques, tools, and applications of big data analytics are reviewed. Conclusions are drawn concerning the design of networked healthcare systems using big data and mobile cloud-computing technologies. An outlook on networked healthcare is given.

A Swarm Intelligence Based Model for Mobile Cloud Computing

Mobile Computing (MC) provides multi services and a lot of advantages for millions of users across the world over the internet. Millions of business customers have leveraged cloud computing services through mobile devices to get what is called Mobile Cloud Computing (MCC). MCC aims at using cloud computing techniques for storage and processing of data on mobile devices, thereby reducing their limitations. This paper proposes architecture for a Swarm Intelligence Based Mobile Cloud Computing Model (SIBMCCM). A model that uses a proposed Parallel Particle Swarm Optimization (PPSO) algorithm to enhance the access time for the mobile cloud computing services which support different E Commerce models and to better secure the communication through the mobile cloud and the mobile commerce transactions.

Scalable Cloudlet-based Mobile Computing Model

Mobile Cloud Computing (MCC) has been introduced as a feasible solution to the inherited limitations of mobile computing. These limitations include battery lifetime, processing power and storage capacity. By using MCC, the processing and the storage of intensive mobile device jobs will take place in the cloud system and the results will be returned to the mobile device. This reduces the required power and time for completing such intensive jobs. However, connecting mobile devices with the cloud, suffers from the high network latency and the huge transmission power consumption especially when using 3G/LTE connections. On the other hand, multimedia applications are the most common applications in today's mobile devices; such applications require high computing resources. In this paper, a Cloudlet-based MCC system is introduced, aiming at reducing the power consumption and the network delay of multimedia applications while using MCC. The MCC concepts with the proposed Cloudlet framework are integrated and a new scalable framework for the MCC model is proposed. Our practical experimental results using multimedia applications show that using the proposed model reduces the power consumption of the mobile devices as well as reducing the communication latency when the mobile device requests a job to be performed remotely while satisfying the high quality of service requirements.