Hybrid cloud load prediction model for LMS applications based on class activity patterns

Maneewongvatana, Songrit; Maneewongvatana, Suthathip

doi:10.1109/icawst.2013.6765450

Cited by 1 publication

(1 citation statement)

References 11 publications

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“…They present the greedy‐based 2‐level algorithm, which schedules a workflow to minimize the cost and deadline. Songrit Maneewongvatana and Suthathip Maneewongvatana present hybrid cloud load prediction model for learning management system applications based on class activity patterns. Huber Flores et al present a generic middleware implementation model to handle process‐intensive hybrid cloud services from mobile devices or phones.…”

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%