Maximizing the user experience with energy-based fair sharing in battery limited mobile systems

Wei, Jianguo; Juárez, E.; Garrido, M.J.; Pescador, F.

doi:10.1109/tce.2013.6626257

Cited by 15 publications

(5 citation statements)

References 18 publications

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“…Also the method includes minimizing the amount of resources required for the QoE provisioning. [6] deals with the design of a novel stream dispatcher. The responsibility of this dispatcher is to analyse the incoming requests and identify fluctuations.…”

Section: Related Workmentioning

confidence: 99%

Balancing Energy consumption and User experience for Multimedia Cloud Computing

David,

Palanisamy

2023

Preprint

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Multimedia services are usually expected to be delivered to the user at the level of Quality of Experience (QoE) which he expects. Hence the multimedia services have moved to a cloud-based environment. A typical cloud-based environment consists of a highly distributed range of servers. There is an urgent need to make these servers highly energy efficient. On the other hand, there exists a trade-off between the energy consumption and the Quality of Experience offered. We have contributed to develop a methodology to guarantee the expected level of QoE, with the minimization of the energy consumed by the various Multimedia data centers by formulating an optimization objective and thereby making optimal decisions. The methodology is self-adaptive and works according to the dynamic changes in the multimedia cloud environment. The approach has been implemented practically and carefully analysed.

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Section: Related Workmentioning

confidence: 99%

Balancing Energy consumption and User experience for Multimedia Cloud Computing

David,

Palanisamy

2023

Preprint

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“…ere are various evaluation methods of user experience. According to [20][21][22], guided by some references of the objective evaluation method, this paper analyses the parameters affecting user experience in cloud computing services and then defines the user experience model. ere are four parameters in the model (availability, SLA violation ratio, run time, and price).…”

Section: User Experience Modulementioning

confidence: 99%

Energy-Saving Virtual Machine Placement Method for User Experience in Cloud Environment

Pang

Kuangdi

Wang

et al. 2020

Mathematical Problems in Engineering

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Green computing focuses on the energy consumption to minimize costs and adverse environmental impacts in data centers. Improving the utilization of host computers is one of the main green cloud computing strategies to reduce energy consumption, but the high utilization of the host CPU can affect user experience, reduce the quality of service, and even lead to service-level agreement (SLA) violations. In addition, the ant colony algorithm performs well in finding suitable computing resources in unknown networks. In this paper, an energy-saving virtual machine placement method (UE-ACO) is proposed based on the improved ant colony algorithm to reduce the energy consumption and satisfy users’ experience, which achieves the balance between energy consumption and user experience in data centers. We improve the pheromone and heuristic factors of the traditional ant colony algorithm, which can guarantee that the improved algorithm can jump out of the local optimum and enter the global optimal, avoiding the premature maturity of the algorithm. Experimental results show that compared to the traditional ant colony algorithm, min-min algorithm, and round-robin algorithm, the proposed algorithm UE-ACO can save up to 20%, 24%, and 30% of energy consumption while satisfying user experience.

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“…Publication [1] presents a model for user typical activity session duration, and use it to compare various power management strategies. Work in [15] proposes a scheduling algorithm for energy-based fair queuing, aiming at optimizing activity and idle periods for user comfort. Such techniques achieve better energy efficiency only if user's requirements are not too strong, as they target application behavior.…”

Section: Related Workmentioning

confidence: 99%

BLAST: Battery Lifetime-constrained Adaptation with Selected Target in Mobile Devices

Mercati

Hanumaiah

Kulkarni

et al. 2015

Proceedings of the 12th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services

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Mobile devices today contain many power hungry subsystems and execute different applications. Standard power management is not aware of the desired battery lifetime and has no visibility into which applications are executing. However, power consumption is strongly dependent on which applications are executed. In this work, we propose a novel power characterization strategy for mobile devices called application-dependent power states (AP-states). Based on that, we formulate a management problem to improve performance under battery lifetime constraints, and we implement the management framework on a real Android device. We call our framework BLAST: Battery Lifetime-constrained Adaptation with Selected Target. The goal of such framework is to maximize performance while letting the device battery to last at least for a certain required lifetime, and only requires the user to select the desired target lifetime. The implementation does not require OS modifications and can be ported and installed to any Android device. We experimentally verify that our strategy can still meets user experience requirements with a selected target battery lifetime extension of at least 25%.

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Maximizing the user experience with energy-based fair sharing in battery limited mobile systems

Cited by 15 publications

References 18 publications

Balancing Energy consumption and User experience for Multimedia Cloud Computing

Balancing Energy consumption and User experience for Multimedia Cloud Computing

Energy-Saving Virtual Machine Placement Method for User Experience in Cloud Environment

BLAST: Battery Lifetime-constrained Adaptation with Selected Target in Mobile Devices

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