Bartendr

Schulman, Aaron; Navda, Vishnu; Ramjee, Ramachandran; Spring, Neil; Deshpande, Pralhad; Grunewald, Calvin; Jain, Kamal; Padmanabhan, Venkata N.

doi:10.1145/1859995.1860006

Cited by 210 publications

(8 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The system takes advantage of bursty transmissions and it also uses prefetching to predict future data accesses and save energy by fetching them in advance. Bartendr [14] exploits the fact that transmissions are less costly when the received signal is strong by transmitting data in periods of good signal strength in cellular networks. Bartendr decides to transmit DTA data based on a fixed signal strength threshold and modulates the traffic of the streams to download more data when strong signal is available.…”

Section: Related Workmentioning

confidence: 99%

Software-related energy footprint of a wireless broadband module

Asplund

Thomasson

Vergara

et al. 2011

Proceedings of the 9th ACM International Symposium on Mobility Management and Wireless Access

View full text Add to dashboard Cite

Energy economy in mobile devices is becoming an increasingly important factor as the devices become more advanced and rich in features. A large part of the energy footprint of a mobile device comes from the wireless communication module, and even more so as the amount of traffic increases. In this paper we study the energy footprint of a mobile broadband hardware module, and how it is affected by software, by performing systematic power consumption measurements. We show that there are several cases where the software does not properly take into account the effect that data communication has on the power consumption. This opens up for potential energy savings by creating better applications that are aware of the energy characteristics of the communication layer.

show abstract

Section: Related Workmentioning

confidence: 99%

Software-related energy footprint of a wireless broadband module

Asplund

Thomasson

Vergara

et al. 2011

Proceedings of the 9th ACM International Symposium on Mobility Management and Wireless Access

View full text Add to dashboard Cite

show abstract

“…Among these techniques: offloading, cloud computing, and region decision for optimal task execution. Taking into consideration the impact of the applications workload on the energy consumption in smart phone, the researchers in Reference 27 developed energy‐aware scheduling algorithms for different application workload. The researchers in Reference 4 studied the impact of wireless signal strength on the consumed energy by the smart phone and suggested new power model based on that.…”

Section: Literature Reviewmentioning

confidence: 99%

Improving the impact of power efficiency in mobile cloud applications using cloudlet model

Tawalbeh

Aldwairi

2020

Concurrency and Computation

View full text Add to dashboard Cite

The applications and services of Information and Communication Technologies are becoming a very essential part of our daily life. In addition, the spread of advanced technologies including the cloud and mobile cloud computing (MCC), wireless communication, and smart devices made it easy to access the internet and utilize unlim-K E Y W O R D S cloud computing applications, cloudlets model, mobile cloud computing, performance improvement, power consumption, simulation 1 INTRODUCTION The spread of advanced technologies including the cloud and mobile cloud computing (MCC), wired and wireless communications, cellular networks, and smart mobile devices made it easy to access the internet and benefit from unlimited number of useful services and applications. For example, we use mobile applications to perform variety of tasks: at work, in hospitals, for educational purposes, commercial transactions, social networking,

show abstract

“…However, since such a network connectivity is provided through wireless (WIFI and cellular radios) the access could be 6 times more expensive (in terms of energy consumption and the use of processing resources), than a regular access when the network signal quality is quite strong, and may lead to data loss [4]. Accordingly, the network attributes have to be merged with the map physical properties when calculating optimal routes.…”

Section: Shortest Bounded Thresholdmentioning

confidence: 99%

Connectivity-optimal shortest paths using crowdsourced data

Hultman

Boudjadar

Asplund

2016

2016 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops)

View full text Add to dashboard Cite

Abstract-With the increasing dependency of ubiquitous connectivity for applications ranging from multimedia entertainment to intelligent transportation systems, having good signal coverage becomes vital. Therefore, route planners and navigation systems should take into account not only the physical distance, but also the characteristics and availability of the cellular network on the potential routes. In this paper we present a route planning tool that finds the connectivity-aware shortest paths based on crowdsourced data from OpenStreetMap and OpenSignal. The tool calculates optimal paths and allows physical distance to be traded against signal quality. The evaluation shows that a 15% increase of the physical path length can achieve an 8.7dBm improvement of worst-case signal strength.

show abstract

Bartendr

Cited by 210 publications

References 19 publications

Software-related energy footprint of a wireless broadband module

Software-related energy footprint of a wireless broadband module

Improving the impact of power efficiency in mobile cloud applications using cloudlet model

Connectivity-optimal shortest paths using crowdsourced data

Contact Info

Product

Resources

About