A Mobility-Aware Optimal Resource Allocation Architecture for Big Data Task Execution on Mobile Cloud in Smart Cities

Enayet, Asma; Razzaque, Md. Abdur; Hassan, Mohammad Mehedi; Alamri, Atif; Fortino, Giancarlo

doi:10.1109/mcom.2018.1700293

Cited by 65 publications

(23 citation statements)

References 14 publications

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“…A 2-tier edge computing architecture has been proposed and the capacity provisioning problems have been solved by stochastic ordering and optimization problems. Enayet et al [26] have presented Mobi-Het, a mobility-aware resource allocation architecture, to execute tasks at remote cloudlets with high efficiency and reliability. In existing works of resource provisioning for MEC, dynamics of mobile requests have not been taken into consideration, which can incur overprovisioning of edge hosts and low resource utilization.…”

Section: Related Workmentioning

confidence: 99%

Cost-Efficient Resource Provisioning for Dynamic Requests in Cloud Assisted Mobile Edge Computing

Wang

Zhang

et al. 2021

IEEE Trans. Cloud Comput.

114

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Mobile edge computing is emerging as a new computing paradigm that provides enhanced experience to mobile users via low latency connections and augmented computation capacity. As the amount of user requests is time-varying, while the computation capacity of edge hosts is limited, the Cloud Assisted Mobile Edge (CAME) computing framework is introduced to improve the scalability of the edge platform. By outsourcing mobile requests to clouds with various types of instances, the CAME framework can accommodate dynamic mobile requests with diverse quality of service requirements. In order to provide guaranteed services at minimal system cost, the edge resource provisioning and cloud outsourcing of the CAME framework should be carefully designed in a cost-efficient manner. Specifically, two fundamental problems should be answered: (1) What is the optimal edge computation capacity configuration? (2) What types of cloud instances should be tenanted and what is the amount of each type? To solve these issues, we formulate the resource provisioning in CAME framework as an optimization problem. By exploiting the piecewise convex property of this problem, the Optimal Resource Provisioning (ORP) algorithms with different instances are developed, so as to optimize the computation capacity of edge hosts and meanwhile dynamically adjust the cloud tenancy strategy. The proposed algorithms are proved to be with polynomial computational complexity. To evaluate the performance of the ORP algorithms, extensive simulations and experiments are conducted based on both widely-used traffic models and Google cluster usage tracelogs, respectively. It is shown that the proposed ORP algorithms outperform the local-first and cloud-first benchmark algorithms in system flexibility and cost-efficiency.

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Section: Related Workmentioning

confidence: 99%

Cost-Efficient Resource Provisioning for Dynamic Requests in Cloud Assisted Mobile Edge Computing

Wang

Zhang

et al. 2021

IEEE Trans. Cloud Comput.

114

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“…Enayet et al [70] have chipped away at, the shrewd city idea, which includes numerous controls, for instance, brilliant human services, keen transportation, and savvy network, has turned out to be famous on account of its capacity to enhance urban residents' personal satisfaction. In any case, most administrations in these regions of shrewd urban areas have progressed toward becoming information driven, in this way producing huge information that require consistent ongoing access, sharing, putting away, preparing, and examination anyplace whenever for clever basic leadership to enhance expectations for everyday comforts.…”

Section: E Review Between 2015 and 2018mentioning

confidence: 99%

Mobility Management Challenges and Solutions in Mobile Cloud Computing System for Next Generation Networks

Pallavi¹,

Jagan²,

Thirumala³

2020

IJACSA

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As of late, there is a dynamic improvement in the field of mobile computing, and mobile cloud computing (MCC) has been familiar with a potential development for portable administrations. Likewise, the mobile phones and their applications have high framework in the administration at any point and grow more rapidly. Again, MCC is depended upon to deliver on a very basic level increasingly inventive with multi applications. Moreover, Mobile handling incorporates versatile equipments, portable correspondence, cell programming, and right now there are various compact cloud applications. Versatility the board for supporting the area following and area based assistance is a significant issue of savvy city by giving the way to the smooth transportation of individuals and products. The mobility is valuable to contribute the development in both open and private transportation frameworks for keen urban communities. As the information is distributed computing and getting to it with cell phones all the exchanges experience the system so it is powerless against assault. For keeping the utilization of this fundamental apparatus of steady in this development world we are giving a portion of the answers for these difficulties to address in the field of MCC. In this paper, the main challenges faced by the enterprises and their corresponding solutions are discussed with the mobile cloud computing.

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“…Enayet et al [41] designed an optimized mobile cloud offloading scheme for big data applications in smart cities. They analyzed the influence of user mobility, connection stability and cloud resource heterogeneousness on mobile cloud offloading procedures.…”

Section: A Multi-objective Optimization Of MCCmentioning

confidence: 99%

An Overview of Mobile Cloud Computing for Pervasive Healthcare

Wang

Jin

2019

IEEE Access

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Mobile devices, along with wearable sensors, allow patients to access healthcare services from anywhere at any time. The longstanding constraints of computational capability and storage space on mobile devices can be alleviated by outsourcing computation-or data-intensive tasks to remote cloud centers. Thus, mobile cloud computing (MCC) has been recognized as a promising approach to provide pervasive healthcare services to people in their daily life. As the development and adoption of MCC techniques in healthcare, new optimization strategies have been explored and studied to help mobile cloud healthcare services to be deployed in a more effective and efficient manner. In this survey, we demonstrate how MCC techniques have been extensively deployed in various healthcare applications and, specifically, describe the general architecture and design considerations one should take into account while designing an MCC for healthcare scenarios. Given a large number of factors that may affect the performance of the MCC and even result in catastrophic consequences in healthcare, this paper presents the state-of-the-art optimization methods on the MCC for meeting the diverse priorities and achieving the optimal tradeoff among multiple objectives. Finally, the security and privacy issues of the MCC in healthcare are also discussed. INDEX TERMS Mobile cloud computing, pervasive healthcare. optimization, offloading, security and privacy. NOMENCLATURE

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A Mobility-Aware Optimal Resource Allocation Architecture for Big Data Task Execution on Mobile Cloud in Smart Cities

Cited by 65 publications

References 14 publications

Cost-Efficient Resource Provisioning for Dynamic Requests in Cloud Assisted Mobile Edge Computing

Cost-Efficient Resource Provisioning for Dynamic Requests in Cloud Assisted Mobile Edge Computing

Mobility Management Challenges and Solutions in Mobile Cloud Computing System for Next Generation Networks

An Overview of Mobile Cloud Computing for Pervasive Healthcare

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