Mobile cloud computing: A survey and propose solution framework

Chalaemwongwan, Nutthakorn; Kurutach, Werasak

doi:10.1109/ecticon.2016.7561437

Cited by 20 publications

(7 citation statements)

References 14 publications

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“…Due to the limited computing, storage, and communication capacities of mobile terminals, the vehicle itself cannot be able to meet the QoS requirements of various applications. Although cloud computing has large computing power, the transmission delay between vehicles and cloud computing platform is significant [ 15 ]. Hence, it is not sufficient for the latency-sensitive applications.…”

Section: Related Workmentioning

confidence: 99%

Software-Defined Optimal Computation Task Scheduling in Vehicular Edge Networking

Peng

2021

Sensors

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With the development of smart vehicles and various vehicular applications, Vehicular Edge Computing (VEC) paradigm has attracted from academic and industry. Compared with the cloud computing platform, VEC has several new features, such as the higher network bandwidth and the lower transmission delay. Recently, vehicular computation-intensive task offloading has become a new research field for the vehicular edge computing networks. However, dynamic network topology and the bursty computation tasks offloading, which causes to the computation load unbalancing for the VEC networking. To solve this issue, this paper proposed an optimal control-based computing task scheduling algorithm. Then, we introduce software defined networking/OpenFlow framework to build a software-defined vehicular edge networking structure. The proposed algorithm can obtain global optimum results and achieve the load-balancing by the virtue of the global load status information. Besides, the proposed algorithm has strong adaptiveness in dynamic network environments by automatic parameter tuning. Experimental results show that the proposed algorithm can effectively improve the utilization of computation resources and meet the requirements of computation and transmission delay for various vehicular tasks.

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Section: Related Workmentioning

confidence: 99%

Software-Defined Optimal Computation Task Scheduling in Vehicular Edge Networking

Peng

2021

Sensors

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“…We are inspired by [39] to extend single-variable Zipf distribution (4) to generalized Zipf distribution (5). We generalize the distribution in (5) in order to model the popular data with a flexible user's format of the request. In order to accomplish this, we propose the following probability mass function (PMF) of flexible requests.…”

Section: A Flexible User: Idea and Implicationsmentioning

confidence: 99%

“…The above equation represents a PMF, since every entity is positive and sums up to one. It should be noted that if flexibility index is K = 1, the distribution in (5) reduces to the normal Zipf distribution in (4). In other words, the univariate distribution as defined in (4) corresponds to the special case where the subset size is equal to 1.…”

Section: A Flexible User: Idea and Implicationsmentioning

confidence: 99%

A New Framework for Mobile Edge Caching by Proposing Flexible User in Heterogeneous Cellular Networks

2020

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The bursting increase in requesting wireless data has caused several issues in network peaktraffic duration. This negatively results in significant data delivery delay imposed on users that can eventually impact the network's quality of service and users' quality of experience. In this research, regarding mobile edge caching as a potential solution to decrease such delay, we propose a new framework in which we introduce the concept of the flexible user where he requests for a set of multiple files from the library with a unique feature, e.g., 5 movies within comedy genre from the library in the peak-traffic duration. The satisfactory criterion for the flexible user is to receive any of the files within the requested set. This definition of the flexible user indicates a new concept which captures interesting scenarios. In order to model this concept, we generalize the conventional Zipf distribution to a multivariate one as the modeling method for popular data. We formulate the problem of finding the optimal cache data placement, which minimizes the average total delivery delay in the network while satisfying the helpers' cache size constraints. To this end, we derive the average delivery delay per user as well as the average total delivery delay in the network, according to the new generalized Zipf distribution. Finding the optimal solution is proved to be NP-Hard. We leverage on the problem property to propose an efficient approximation method, called greedy algorithm, which performs within a constant factor as good as the optimal solution. Afterwards, we propose an algorithm called speedy-greedy to significantly reduce the computational complexity of the greedy algorithm while achieving the same performance. Simulation results indicate that our proposed framework significantly decreases the average total delivery delay of the system model that can help the network maintain its quality of service in network peak-traffic duration. INDEX TERMS Mobile edge caching, data delivery and management, delivery delay, femto-caching.

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“…Particularly, in recent years, more kinds of basic functions (e.g., computation, storage, and network) are offered by cloud computing as the software services for elastic management and rapid service delivery with low cost, such as SDS (Software Defined Storage) [1], SDN (Software Defined Network) [2], and cloud-based mobile Apps. With the explosion of mobile applications and the support of cloud computing, mobile computing based on clouds provides a new and promising paradigm for delivering IT services more effectively and conveniently [3]. Moreover, services provided by different clouds and mobile terminals can be composed together to form a more powerful applications [4,5], for example, trip mode selection application composed by Positioning service, Walking Speed service, Bus Tracking service, and Arrival Estimate service [6].…”

Section: Introductionmentioning

confidence: 99%

Distributed Secure Service Composition with Declassification in Mobile Clouds

et al. 2017

Mobile Information Systems

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The regional and dynamic characteristics of mobile clouds pose a great challenge on information flow security during service composition. Although secure verification approaches based on standard noninterference provide a solid assurance on information flow security of composite service, too strict constraints on service components may cause the failure of composition procedure. In order to ensure the availability of composite service, we specify the declassification policies based on cryptographic operations to allow data to be legally declassified. And we propose the improved distributed secure service composition framework and approach, which can realize different cloud platforms in multiple domains, cooperate with each other to complete the declassification, and secure composition procedure. Through the experiment and evaluation, it is indicated that our approach provides a more reliable and efficient way for secure service composition in mobile clouds.

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Mobile cloud computing: A survey and propose solution framework

Cited by 20 publications

References 14 publications

Software-Defined Optimal Computation Task Scheduling in Vehicular Edge Networking

Software-Defined Optimal Computation Task Scheduling in Vehicular Edge Networking

A New Framework for Mobile Edge Caching by Proposing Flexible User in Heterogeneous Cellular Networks

Distributed Secure Service Composition with Declassification in Mobile Clouds

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