Outsourcing Resource-Intensive Tasks from Mobile Apps to Clouds: Android and Aneka Integration

Justino, Tiago; Buyya, Rajkumar

doi:10.1109/ccem.2014.7015494

Cited by 11 publications

(5 citation statements)

References 11 publications

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“…Zheng Chang et al study resource allocation and data offloading for energy efficiency in wireless power transfer–enabled collaborative mobile clouds. Tiago Justino et al proposed a mobile cloud model to process resource‐intensive tasks to alleviate the mobile device load. Li Chunlin et al propose context‐based service provisioning to improve user experience in mobile cloud.…”

Section: Related Workmentioning

confidence: 99%

Energy‐aware cross‐layer resource allocation in mobile cloud

Liu

Luo

2017

Int J Communication

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Summary This paper proposed an energy‐aware cross‐layer mobile cloud resource allocation approach. In this paper, a hybrid cloud architecture is adopted for provisioning mobile service to mobile device users, which include nearby local cloud and remote public cloud. The computation‐intensive tasks can be processed by the remote public cloud, while the delay‐sensitive computation can be processed by the nearby local cloud. On the basis of the system context and mobile user preferences, the energy‐aware cross‐layer mobile cloud resource allocation approach can optimize the consumption of cloud resource and system performance. The cooperation and collaboration among local cloud agent, public cloud supplier, and mobile cloud user are regulated through the economic approach. The energy‐aware cross‐layer mobile cloud resource allocation is performed on the local cloud level and the public cloud level, which comprehensively considers the benefits of all participants. The energy‐aware cross‐layer mobile cloud resource allocation algorithm is proposed, which is evaluated in the experiment environment, and comparison results and analysis are discussed.

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Section: Related Workmentioning

confidence: 99%

Energy‐aware cross‐layer resource allocation in mobile cloud

Liu

Luo

2017

Int J Communication

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“…1,2,19 There are some related works that study load balancing in cloud environment. Justino et al 3 propose mobile cloud architecture to delegate resource-intensive mobile tasks in an asynchronous manner. That architecture is designed to provide transparent resource provisioning and job scheduling services.…”

Section: Related Workmentioning

confidence: 99%

QoS‐based resource allocation across local and public cloud for resource‐constrained mobile device

Liu

Luo

2016

Trans. Emerging Tel. Tech.

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This paper uses the hybrid cloud architecture, including the local cloud and the remote public cloud, to execute the mobile tasks with the objective of optimizing the resource allocation, while meeting the service‐level agreement requirement. Hybrid cloud is a cloud computing environment that uses a mix of on‐premises http://searchcloudcomputing.techtarget.com/definition/private-cloud and third‐party http://searchcloudcomputing.techtarget.com/definition/public-cloud services with orchestration between the 2 platforms. The mobile application could run in private cloud, but use http://searchcloudcomputing.techtarget.com/definition/cloud-bursting to access additional computing resources from a public cloud when computing demands spike. The proposed Quality of Service–based resource allocation optimization scheme across local and public cloud for resource‐constrained mobile device is formulated into a constrained optimization problem and further divided into 2 subproblems, ie, one for resource allocation for the local cloud and one for the remote public cloud, using the Lagrange method. The proposed Quality of Service–based resource allocation optimization algorithm across local and public cloud is proposed. Through the experiments, the performance of proposed algorithm is evaluated with other baseline algorithms. The results of the experiments show a performance improvement when compared to the approaches from the literature.

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“…Mobile cloud architecture, application model, time-constrained or real time Fig. 1: Conceptual overview of the Aura model task offloading scheduler and algorithm are proposed in [18], [19], [20], [21], [22]. Our work is complementary to the research on mobile computation offloading and can leverage the offloading or scheduling scheme on the mobile devices to determine optimal strategies for offloading tasks to IoT clouds.…”

Section: Related Workmentioning

confidence: 99%

Aura: An IoT Based Cloud Infrastructure for Localized Mobile Computation Outsourcing

Hasan

Hossain

Khan

2015

2015 3rd IEEE International Conference on Mobile Cloud Computing, Services, and Engineering

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Many applications require localized computation in order to ensure better performance, security, and lower costs. In recent years, the emergence of Internet-of-Things (IoT) devices has caused a paradigm shift in computing and communication.IoT devices are making our physical environment and infrastructures smarter, bringing pervasive computing to the mainstream. With billions of such devices slated to be deployed in the next five years, we have the opportunity to utilize these devices in converting our physical environment into interactive, smart, and intelligent computing infrastructures. In this paper, we present Aura -a highly localized IoT based cloud computing model. Aura allows clients to create ad hoc clouds using the IoT and other computing devices in the nearby physical environment, while providing the flexibility of cloud computing. Aura provides localized computation capability from untapped computing resources. Computations done on Aura are highly flexible, giving clients full control to start, stop, migrate, and restart computations in nearby devices as the clients move between different physical locations. To demonstrate the feasibility of Aura, we have ported a lightweight version of MapReduce to run on IoT devices, and evaluated its performance.

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Outsourcing Resource-Intensive Tasks from Mobile Apps to Clouds: Android and Aneka Integration

Cited by 11 publications

References 11 publications

Energy‐aware cross‐layer resource allocation in mobile cloud

Energy‐aware cross‐layer resource allocation in mobile cloud

QoS‐based resource allocation across local and public cloud for resource‐constrained mobile device

Aura: An IoT Based Cloud Infrastructure for Localized Mobile Computation Outsourcing

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