Anatomizing application performance differences on smartphones

Huang, Junxian; Xu, Qiang; Tiwana, Birjodh; Mao, Z. Morley; Zhang, Ming; Bahl, Paramvir

doi:10.1145/1814433.1814452

Cited by 238 publications

(151 citation statements)

References 10 publications

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“…As such, the bulk of our dataset 2 is biased toward locations where Google employees live and work. Speedometer collected the following measurements from 2011-10 to 2013-2 (17 months): 6.6M ping RTTs to www.google.com (each sample consists of 10 consecutive probes), 1.7M HTTP GETs to measure TCP throughput 3 All users consented to participate in the measurement study; the anonymization process is explained in the dataset's README file. Because of anonymization, the number of users who participated in data collection is unknown.…”

Section: Methodology and Datasetmentioning

confidence: 99%

“…Most closely related to our work is [1] and [3]. Both projects gather active measurements from apps running on mobile devices; however, they all rely on user-generated tests.…”

Section: Related Workmentioning

confidence: 99%

“…For instance, carriers enforce different policies depending on the traffic types or geographic/social characteristics of different locations such as population [1,2], causing user perceived performance to differ from advertised performance for access technologies. Other environmental factors have a significant impact on performance, including device model [3], mobility [4], network load [2], packet size [5,6] and MAC-layer scheduling [4].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Mobile Network Performance from User Devices: A Longitudinal, Multidimensional Analysis

Nikravesh

Choffnes

Katz-Bassett

et al. 2014

Passive and Active Measurement

Self Cite

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Abstract. In the cellular environment, operators, researchers and end users have poor visibility into network performance for devices. Improving visibility is challenging because this performance depends factors that include carrier, access technology, signal strength, geographic location and time. Addressing this requires longitudinal, continuous and large-scale measurements from a diverse set of mobile devices and networks.This paper takes a first look at cellular network performance from this perspective, using 17 months of data collected from devices located throughout the world. We show that (i) there is significant variance in key performance metrics both within and across carriers; (ii) this variance is at best only partially explained by regional and time-of-day patterns; (iii) the stability of network performance varies substantially among carriers. Further, we use the dataset to diagnose the causes behind observed performance problems and identify additional measurements that will improve our ability to reason about mobile network behavior.

show abstract

Section: Methodology and Datasetmentioning

confidence: 99%

“…Most closely related to our work is [1] and [3]. Both projects gather active measurements from apps running on mobile devices; however, they all rely on user-generated tests.…”

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Mobile Network Performance from User Devices: A Longitudinal, Multidimensional Analysis

Nikravesh

Choffnes

Katz-Bassett

et al. 2014

Passive and Active Measurement

Self Cite

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“…Mobile data usage has outgrown available bandwidth in 3G/HSPA network, resulting in severe congestion in the cellular network in some cases 2 . A popular approach to reduce such congestion is to offload data traffic to the WiFi network whenever possible 3 .…”

Section: Related Workmentioning

confidence: 99%

“…In addition, the much shorter RTT for direct mobile-to-mobile transfer allows significantly higher throughput compared to transferring large amount of data over the Internet through the 3G/HSPA network, where the median ping latency has been observed to be almost 200ms [2].…”

Section: Introductionmentioning

confidence: 99%

Mobile-to-Mobile Video Recommendation

Seshadri

Chan

Ooi

2013

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Abstract. Mobile device users can now easily capture and socially share video clips in a timely manner by uploading them wirelessly to a server. When attending crowded events, however, timely sharing of videos becomes difficult due to choking bandwidth in the network infrastructure, preventing like-minded attendees from easily sharing videos with each other through a server. One solution to alleviate this problem is to use direct device-to-device communication to share videos among nearby attendees. Contact capacity between two devices, however, is limited, and thus a recommendation algorithm is needed to select and transmit only videos of potential interest to an attendee. In this paper, we address the question: which video clip should be transmitted to which user. We proposed an video transmission scheduling algorithm, called CoFiGel, that runs in a distributed manner and aims to improve both the prediction coverage and precision of the recommendation algorithm. At each device, CoFiGel transmits the video that would increase the estimated number of positive user-video ratings the most if this video is transferred to the destination device. We evaluated CoFiGel using real-world traces and show that substantial improvement can be achieved compared to baseline schemes that do not consider rating or contact history.

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References

2017

Green Mobile Networks

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Anatomizing application performance differences on smartphones

Cited by 238 publications

References 10 publications

Mobile Network Performance from User Devices: A Longitudinal, Multidimensional Analysis

Mobile Network Performance from User Devices: A Longitudinal, Multidimensional Analysis

Mobile-to-Mobile Video Recommendation

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