Optimizing Energy Efficiency of Browsers in Energy-Aware Scheduling-enabled Mobile Devices

Choi, Yonghun; Park, Seonghoon; Cha, Hojung

doi:10.1145/3300061.3345449

Cited by 14 publications

(21 citation statements)

References 7 publications

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“…To study the relative importance of each component of the model, we exclude every major one and observe how it affects the performance of the scheduler. An overview of this ablation analysis is given in Table (13). We reach the following findings:…”

Section: Ablation Analysismentioning

confidence: 57%

“…• Without the exploration term, the model is unable to find possibly better scheduling decisions. Also, the domain knowledge term, i.e., the GOBIGraph based indicator term in (13) helps the model to use the deep surrogate model for exploration. Both these terms help improve the scheduling decisions and reach better QoS values.…”

Section: Ablation Analysismentioning

confidence: 99%

“…For instance, many industries, especially healthcare, robotics and smart-cities demand ultralow response times for SLA sensitive tasks [12]. Battery driven mobile edge computing nodes also have strict energy consumption demands [13], [14]. Applications deployed on a pay-per-use cloud service model aim at reducing the overall execution costs.…”

Section: Introductionmentioning

confidence: 99%

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MCDS: AI Augmented Workflow Scheduling in Mobile Edge Cloud Computing Systems

Tuli¹,

Casale²,

Jennings³

2021

Preprint

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Workflow scheduling is a long-studied problem in parallel and distributed computing (PDC), aiming to efficiently utilize compute resources to meet user's service requirements. Recently proposed scheduling methods leverage the low response times of edge computing platforms to optimize application Quality of Service (QoS). However, scheduling workflow applications in mobile edge-cloud systems is challenging due to computational heterogeneity, changing latencies of mobile devices and the volatile nature of workload resource requirements. To overcome these difficulties, it is essential, but at the same time challenging, to develop a long-sighted optimization scheme that efficiently models the QoS objectives. In this work, we propose MCDS: Monte Carlo Learning using Deep Surrogate Models to efficiently schedule workflow applications in mobile edge-cloud computing systems. MCDS is an Artificial Intelligence (AI) based scheduling approach that uses a tree-based search strategy and a deep neural network-based surrogate model to estimate the long-term QoS impact of immediate actions for robust optimization of scheduling decisions. Experiments on physical and simulated edge-cloud testbeds show that MCDS can improve over the state-of-the-art methods in terms of energy consumption, response time, SLA violations and cost by at least 6.13, 4.56, 45.09 and 30.71 percent respectively.

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Section: Ablation Analysismentioning

confidence: 57%

Section: Ablation Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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MCDS: AI Augmented Workflow Scheduling in Mobile Edge Cloud Computing Systems

Tuli¹,

Casale²,

Jennings³

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Specifically on a mobile environment, there are works analyzing the energy efficiency of code blocks [5,13], the energy impact of different virtual keyboards [25], monitoring how energy consumption evolves [9], how browser extensions affect browser performance [2], and analyzing the characteristics of web browsers which cause energy inefficiency in EAS enabled mobile devices [3].…”

Section: Related Workmentioning

confidence: 99%

Energy wars - Chrome vs. Firefox

Macedo

Aloisio

Gonçalves

et al. 2020

Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering Workshops

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This paper presents a preliminary study on the energy consumption of two popular web browsers. In order to properly measure the energy consumption of both environments, we simulate the usage of various applications, which the goal to mimic typical user interactions and usage.Our preliminary results show interesting findings based on observation, such as what type of interactions generate high peaks of energy consumption, and which browser is overall the most efficient. Our goal with this preliminary study is to show to users how very different the efficiency of web browsers can be, and may serve with advances in this area of study.

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“…Low Latency Overhead: For the real-time inference exe-cution on the energy constrained edge devices, latency overhead is also one of the crucial factors. Among the various forms of RL [74], such as Q-learning [12], TD-learning [60], and deep RL [67], Q-learning has an advantage for low latency overhead, as it finds the best action with a look-up table.…”

Section: Autoscalementioning

confidence: 99%

AutoScale: Optimizing Energy Efficiency of End-to-End Edge Inference under Stochastic Variance

Kim,

2020

Preprint

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Deep learning inference is increasingly run at the edge. As the programming and system stack support becomes mature, it enables acceleration opportunities within a mobile system, where the system performance envelope is scaled up with a plethora of programmable co-processors. Thus, intelligent services designed for mobile users can choose between running inference on the CPU or any of the co-processors on the mobile system, or exploiting connected systems, such as the cloud or a nearby, locally connected system. By doing so, the services can scale out the performance and increase the energy efficiency of edge mobile systems. This gives rise to a new challenge-deciding when inference should run where. Such execution scaling decision becomes more complicated with the stochastic nature of mobile-cloud execution, where signal strength variations of the wireless networks and resource interference can significantly affect real-time inference performance and system energy efficiency. To enable accurate, energy-efficient deep learning inference at the edge, this paper proposes AutoScale. AutoScale is an adaptive and light-weight execution scaling engine built upon the customdesigned reinforcement learning algorithm. It continuously learns and selects the most energy-efficient inference execution target by taking into account characteristics of neural networks and available systems in the collaborative cloudedge execution environment while adapting to the stochastic runtime variance. Real system implementation and evaluation, considering realistic execution scenarios, demonstrate an average of 9.8 and 1.6 times energy efficiency improvement for DNN edge inference over the baseline mobile CPU and cloud offloading, while meeting the real-time performance and accuracy requirement.

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Optimizing Energy Efficiency of Browsers in Energy-Aware Scheduling-enabled Mobile Devices

Cited by 14 publications

References 7 publications

MCDS: AI Augmented Workflow Scheduling in Mobile Edge Cloud Computing Systems

MCDS: AI Augmented Workflow Scheduling in Mobile Edge Cloud Computing Systems

Energy wars - Chrome vs. Firefox

AutoScale: Optimizing Energy Efficiency of End-to-End Edge Inference under Stochastic Variance

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