AndroidOff:Offloading android application based on cost estimation

Chen, Xing; Chen, Jiaqing; Liu, Bichun; Ma, Yun; Zhang, Ying; Zhong, Hao

doi:10.1016/j.jss.2019.110418

Cited by 14 publications

(4 citation statements)

References 37 publications

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“…The vehicle speed V n is v abs n = 60km/hour [41]. The relative speed among vehicles is set in the range of [ 10,20] m/s. The vertical distance from RSU to the road is set as e = 100m.…”

Section: Resultsmentioning

confidence: 99%

“…However, vehicle's data demands and computation requirements are also increasing day by day caused prompt interactive response in the computation offloading service by deploying computing nodes or servers, to fulfill users' demands for delay-sensitive tasks [9]. Computation offloading is a promising way of transferring some computation-intensive activities to nearby servers [10,11]. Moreover, VEC takes advantage of proximal vehicles, to reduce the edge servers' load.…”

Section: Introductionmentioning

confidence: 99%

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An efficient task offloading scheme in vehicular edge computing

et al. 2020

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Vehicular edge computing (VEC) is a promising paradigm to offload resource-intensive tasks at the network edge. Owing to time-sensitive and computation-intensive vehicular applications and high mobility scenarios, cost-efficient task offloading in the vehicular environment is still a challenging problem. In this paper, we study the partial task offloading problem in vehicular edge computing in an urban scenario. Where the vehicle computes some part of a task locally, and offload the remaining task to a nearby vehicle and to VEC server subject to the maximum tolerable delay and vehicle’s stay time. To make it cost-efficient, including the cost of the required communication and computing resources, we consider to fully exploit the vehicular available resources. We estimate the transmission rates for the vehicle to vehicle and vehicle to infrastructure communication based on practical assumptions. Moreover, we present a mobility-aware partial task offloading algorithm, taking into account the task allocation ratio among the three parts given by the communication environment conditions. Simulation results validate the efficient performance of the proposed scheme that not only enhances the exploitation of vehicular computation resources but also minimizes the overall system cost in comparison to baseline schemes.

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“…The vehicle speed V n is v abs n = 60km/hour [41]. The relative speed among vehicles is set in the range of [ 10,20] m/s. The vertical distance from RSU to the road is set as e = 100m.…”

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An efficient task offloading scheme in vehicular edge computing

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Finally, other approaches focus on offloading some part of a mobile application depending on its context and the consumed resources. For instance, AndroidOff [67], collects the execution costs of a subset of the methods and then uses static analysis to predict the execution costs of the remaining methods. Then, optimization algorithms decide when some methods should be offloaded to the cloud.…”

Section: Related Workmentioning

confidence: 99%

Early Evaluation of Mobile Applications’ Resource Consumption and Operating Costs

et al. 2020

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The explosive growth of the mobile application market in recent years has led to a large concomitant mobile software industry whose components are, in many cases, startups and small-size software providers. The success of these applications and the firms behind them depends on a subtle balance between different dimensions mainly affected by their architectural design, such as user satisfaction, resource consumption, operating costs, and timing. The present communication describes a framework with a specific set of practices for identifying the boundaries of different architectural designs-in this paper we apply it to estimate both the smartphone's resource consumption and the operating costs in the cloudand thus help in the architectural decision-making process. This will enable mobile software developers to predict at early stages which architectural design best suits their business model in accordance with the number of users and the expected use of the application and even provide an advance alert of when architectural choices will need to be reviewed, obviating the need for costly architectural redesign in further phases.

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“…In energy management system, accurately predict the energy use could assist energy manager make advisable strategies and schedule resource reasonably, this is the motivation of conducting this research, and the research data was acquired by the energy monitoring system. Another important reason for this study is the excessive energy consumption which caused large emissions of greenhouse gas has made huge impact on climate [5][6][7]. It was well known that more and more frequent and intense extreme weather events have occurred in recent decades which have profound effects on human society as well as on ecosystems [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Using improved support vector regression to predict the transmitted energy consumption data by distributed wireless sensor network

Guo

Gui

Chen

et al. 2020

J Wireless Com Network

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Massive energy consumption data of buildings was generated with the development of information technology, and the real-time energy consumption data was transmitted to energy consumption monitoring system by the distributed wireless sensor network (WSN). Accurately predicting the energy consumption is of importance for energy manager to make advisable decision and achieve the energy conservation. In recent years, considerable attention has been gained on predicting energy use of buildings in China. More and more predictive models appeared in recent years, but it is still a hard work to construct an accurate model to predict the energy consumption due to the complexity of the influencing factors. In this paper, 40 weather factors were considered into the research as input variables, and the electricity of supermarket which was acquired by the energy monitoring system was taken as the target variable. With the aim to seek the optimal subset, three feature selection (FS) algorithms were involved in the study, respectively: stepwise, least angle regression (Lars), and Boruta algorithms. In addition, three machine learning methods that include random forest (RF) regression, gradient boosting regression (GBR), and support vector regression (SVR) algorithms were utilized in this paper and combined with three feature selection (FS) algorithms, totally are nine hybrid models aimed to explore an improved model to get a higher prediction performance. The results indicate that the FS algorithm Boruta has relatively better performance because it could work well both on RF and SVR algorithms, the machine learning method SVR could get higher accuracy on small dataset compared with the RF and GBR algorithms, and the hybrid model called SVR-Boruta was chosen to be the proposed model in this paper. What is more, four evaluate indicators were selected to verify the model performance respectively are the mean absolute error (MAE), the mean squared error(MSE), the root mean squared error (RMSE), and the R-squared (R2), and the experiment results further verified the superiority of the recommended methodology.

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AndroidOff:Offloading android application based on cost estimation

Cited by 14 publications

References 37 publications

An efficient task offloading scheme in vehicular edge computing

An efficient task offloading scheme in vehicular edge computing

Early Evaluation of Mobile Applications’ Resource Consumption and Operating Costs

Using improved support vector regression to predict the transmitted energy consumption data by distributed wireless sensor network

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