TOP: Tail Optimization Protocol For Cellular Radio Resource Allocation

Qian, Feng; Wang, Zhaoguang; Gerber, Alexandre; Mao, Z. Morley; Sen, Subhabrata; Spatscheck, Oliver

doi:10.1109/icnp.2010.5762777

Cited by 119 publications

(110 citation statements)

References 15 publications

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“…Thus if the Tail Time is handled carefully it can be used to avoid unnecessary overhead in terms of Energy Consumption and Network Signaling [10] [14]. Authors in [4] observed that Multiple Applications requesting for connection can increase signaling load considerably.…”

Section: Duplicate Content Downloadmentioning

confidence: 99%

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Analyzing Network and Energy Efficiency of Android Smartphone Applications

Garg¹,

Gupta²

2015

IJCA

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Mobile phones and tablets can be considered as the first incarnation of the post-PC era. Their explosive adoption rates lead to the emergence of a variety of Applications. Most attractive feature of these applications is that they are available for a variety of OS. Many of these applications, for example VoIP clients, stay active in the background. In the background, they may not communicate large amounts of data. However, their regular bursts of activity can lead to large signaling overheads, wastage of radio resources, and draining of a phone's battery. Signaling overheads can lead to service outage by overloading the radio network controller of the 3G network. Thus in addition to aiding user, these convenience applications can pose several threats to the user handset and the Network. In this Work, We study the various Problems that persist affecting the user handset and Network and the solutions that exist for dealing with these problems. We attempt to classify the Problem and Solutions into various categories and list all the available solutions that exist for a given problem. General Terms

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Section: Duplicate Content Downloadmentioning

confidence: 99%

“…Authors in [14] have designed a tool TOP (Tail Optimization Protocol). It requires no change in cellular infrastructure and only minimal changes in smartphone applications.…”

Section: Dealing With Tail Timementioning

confidence: 99%

Analyzing Network and Energy Efficiency of Android Smartphone Applications

Garg¹,

Gupta²

2015

IJCA

View full text Add to dashboard Cite

show abstract

“…In [7] and [9], different values for tail time has been suggested and proven to reduce the energy consumption. In [11,17,18] dynamic assignment of tail time based on usage pattern has been proposed. In particular [18] relies on prediction and dynamically terminates the tail time when it does not foresee an upcoming activity.…”

Section: Related Workmentioning

confidence: 99%

“…In [11,17,18] dynamic assignment of tail time based on usage pattern has been proposed. In particular [18] relies on prediction and dynamically terminates the tail time when it does not foresee an upcoming activity. Similarly [3] proposes data mining approaches to detect end of communication spurts to invoke fast dormancy with higher accuracy.…”

Section: Related Workmentioning

confidence: 99%

Energy Efficient Scheduling for Mobile Push Notifications

Acer

Mashhadi

Forlivesi

et al. 2015

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

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Push notifications are small and succinct messages used by mobile applications to inform users of new events and updates. These notifications are pushed to the user devices by a set of dedicated notification servers (e.g., Apple Push Notification Server, Google Cloud Messaging Server, etc.) as they arrive from the content providers of the mobile applications. However, due to their intrinsic small size and sporadic nature, the transfer of these messages is not power efficient, especially on cellular networks. To address this, we propose a network centric scheduling mechanism that delays the delivery of these messages as appropriate by sensing and predicting users' cellular network activities. A trace based evaluation with 60 users' cellular network logs of 30 days shows that we can reduce the energy consumption of mobile devices by 10% for an average delay of 150 seconds in notification delivery. As a network based system that does not require any modifications to user devices, scheduling push notifications opens up interesting opportunities for mobile operators to provide value added and differentiating services, especially considering the sharp rise of non-critical push notification messages.

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“…To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. IMC'12, November [14][15][16]2012 kinds. Cellular networks are typically characterized by limited radio resources and significant device power consumption for network communications.…”

Section: Introductionmentioning

confidence: 99%

Screen-off traffic characterization and optimization in 3G/4G networks

Huang

Qian

Mao

et al. 2012

Proceedings of the 2012 Internet Measurement Conference

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Today's cellular systems operate under diverse resource constraints: limited frequency spectrum, network processing capability, and handset battery life. We consider a novel and important factor, handset screen status, i.e., whether the screen is on or off, which was ignored by previous approaches for optimizing cellular resource utilization. Based on analyzing real smartphone traffic collected from 20 users over five months, we find that off-screen traffic accounts for 58.5% of the total radio energy consumption although their traffic volume contribution is much smaller. Such unexpected results are attributed to the unique cellular resource management policy that is not well understood by developers, leading to cellularunfriendly mobile apps. We then make a further step by proposing screen-aware optimization, by leveraging the key observation that screen-off traffic is much more delay-tolerant than its screenon counterpart due to a lack of user interaction. Our proposal can better balance the key tradeoffs in cellular networks. It saves up to 60.92% of the network energy and reduces signaling and delay overhead by 25.33% and 30.59%, respectively.

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TOP: Tail Optimization Protocol For Cellular Radio Resource Allocation

Cited by 119 publications

References 15 publications

Analyzing Network and Energy Efficiency of Android Smartphone Applications

Analyzing Network and Energy Efficiency of Android Smartphone Applications

Energy Efficient Scheduling for Mobile Push Notifications

Screen-off traffic characterization and optimization in 3G/4G networks

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