Opportunistic traffic scheduling in cellular data networks

Paul, Utpal; Buddhikot, Milind M.; Das, Samir R.

doi:10.1109/dyspan.2012.6478157

Cited by 9 publications

(18 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Now, we consider another application, opportunistic traffic scheduling in the context of cellular data network [29]. The idea here is to reduce the peak-traffic load and reduce the peak-to-average load ratios in cellular data networks by using a higher-layer priority-based scheduling.…”

Section: B Application 2: Opportunistic Traffic Schedulingmentioning

confidence: 99%

“…Finally, we analyze how much accuracy is important. To this end, we study two different resource management applications -(i) energy savings [18] and (ii) opportunistic [29]. scheduling.…”

Section: B Estimating Base Station Loads and Applicationsmentioning

confidence: 99%

“…One of the approaches to schedule the low priority flows in [29] uses load prediction. In this approach, base stations' loads and subscribers' mobility are predicted using appropriate models.…”

Section: B Application 2: Opportunistic Traffic Schedulingmentioning

confidence: 99%

“…High priority flows, on the other hand, are served immediately as is normal in today's network. In the specific technique we consider [29] a notion of 'deadline' is used for the low-priority flows to aid in scheduling. Note that essentially the technique moves low priority flows temporally and also spatially (if the user moves) from peak to off-peak periods.…”

Section: B Application 2: Opportunistic Traffic Schedulingmentioning

confidence: 99%

“…It is not always clear what type of accuracy is appropriate unless it is tied to an application that makes direct use of the measurement data. Thus, we also study the applicability of our proposed model for two applications in cellular networks -(i) energy savings [18] and (ii) opportunistic scheduling [29].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Learning probabilistic models of cellular network traffic with applications to resource management

Paul

Ortiz

Das

et al. 2014

2014 IEEE International Symposium on Dynamic Spectrum Access Networks (DYSPAN)

Self Cite

View full text Add to dashboard Cite

Given the exponential increase in broadband cellular traffic it is imperative that scalable traffic measurement and monitoring techniques be developed to aid various resource management methods. In this paper, we use a machine learning technique to learn the underlying conditional dependence and independence structure in the base station traffic loads to show how such probabilistic models can be used to reduce the traffic monitoring efforts. The broad goal is to exploit the model to develop a spatial sampling technique that estimates the loads on all the base stations based on actual measurements only on a small subset of base stations. We take special care to develop a sparse model that focuses on capturing only key dependences. Using trace data collected in a network of 400 base stations we show the effectiveness of this approach in reducing the monitoring effort. To understand the tradeoff between the accuracy and monitoring complexity better, we also study the use of this modeling approach on real applications. Two applications are studied -energy saving and opportunistic scheduling. They show that load estimation via such modeling is quite effective in reducing the monitoring burden.

show abstract

Section: B Application 2: Opportunistic Traffic Schedulingmentioning

confidence: 99%

“…Finally, we analyze how much accuracy is important. To this end, we study two different resource management applications -(i) energy savings [18] and (ii) opportunistic [29]. scheduling.…”

Section: B Estimating Base Station Loads and Applicationsmentioning

confidence: 99%

Section: B Application 2: Opportunistic Traffic Schedulingmentioning

confidence: 99%

Section: B Application 2: Opportunistic Traffic Schedulingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Learning probabilistic models of cellular network traffic with applications to resource management

Paul

Ortiz

Das

et al. 2014

2014 IEEE International Symposium on Dynamic Spectrum Access Networks (DYSPAN)

Self Cite

View full text Add to dashboard Cite

show abstract

AI-assisted traffic matrix prediction using GA-enabled deep ensemble learning for hybrid SDN

Etengu

Tan

Chuah

et al. 2023

Computer Communications

View full text Add to dashboard Cite

Async: De-congestion and yield management in cellular data networks

Gabale

Devi

Kokku

et al. 2013

2013 21st IEEE International Conference on Network Protocols (ICNP)

View full text Add to dashboard Cite

We design and implement a novel system called Async, which enables a mobile network operator (MNO) to efficiently manage the growth of mobile data by leveraging the delay-elastic nature of certain applications and the price-sensitive nature of certain users. Specifically, Async introduces an alternate "asynchronous" content-delivery paradigm for heavy content (e.g., videos), and facilitates an MNO to negotiate with users a delay in delivery in exchange for appropriate incentives. The MNO uses the negotiated delays to actively manage Async flows to reduce congestion and improve the quality-of-experience (QoE) of both delayed and regular flows. We show that in comparison to stateof-the-art, Async's network-based flow management enhances QoE for more than 30% of the regular flows, with up to 60% improvement in per-flow QoE metric, while still meeting the negotiated delivery times of 95% of the delayed flows. Async also lowers the delivery times of delayed flows by ∼67% and significantly increases robustness to traffic unpredictability. Our design is robust to disconnections and does not require any modifications to existing network infrastructure and protocols. Our prototype deployment (using Apache's mod proxy and an Android app) on live networks confirms Async's efficacy in meeting EDTs for diverse deployment scenarios.

show abstract

Opportunistic traffic scheduling in cellular data networks

Cited by 9 publications

References 14 publications

Learning probabilistic models of cellular network traffic with applications to resource management

Learning probabilistic models of cellular network traffic with applications to resource management

AI-assisted traffic matrix prediction using GA-enabled deep ensemble learning for hybrid SDN

Async: De-congestion and yield management in cellular data networks

Contact Info

Product

Resources

About