Cloud Analytics for Wireless Metric Prediction - Framework and Performance

Sayeed, Zulfiquar; Liao, Qi; Faucher, Dave; Grinshpun, Ed; Sharma, Sameer

doi:10.1109/cloud.2015.135

Cited by 9 publications

(3 citation statements)

References 6 publications

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“…where C (b i ) is the average number of useful bits per PRB for bearer i [31]. The forecast of the UEs channel quality can be used to estimate the MCS [24]. Table 1 illustrates the average throughput given a variety of PRB rates along with the user's MCS.…”

Section: Objectivementioning

confidence: 99%

RAN Resource Usage Prediction for a 5G Slice Broker

Gutterman

Grinshpun

Sharma

et al. 2019

Proceedings of the Twentieth ACM International Symposium on Mobile Ad Hoc Networking and Computing

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Network slicing will allow 5G network operators to oer a diverse set of services over a shared physical infrastructure. We focus on supporting the operation of the Radio Access Network (RAN) slice broker, which maps slice requirements into allocation of Physical Resource Blocks (PRBs). We rst develop a new metric, REVA, based on the number of PRBs available to a single Very Active bearer. REVA is independent of channel conditions and allows easy derivation of an individual wireless link's throughput. In order for the slice broker to eciently utilize the RAN, there is a need for reliable and short term prediction of resource usage by a slice. To support such prediction, we construct an LTE testbed and develop custom additions to the scheduler. Using data collected from the testbed, we compute REVA and develop a realistic time series prediction model for REVA. Specically, we present the X-LSTM prediction model, based upon Long Short-Term Memory (LSTM) neural networks. Evaluated with data collected in the testbed, X-LSTM outperforms Autoregressive Integrated Moving Average Model (ARIMA) and LSTM neural networks by up to 31%. X-LSTM also achieves over 91% accuracy in predicting REVA. By using X-LSTM to predict future usage, a slice broker is more adept to provision a slice and reduce over-provisioning and SLA violation costs by more than 10% in comparison to LSTM and ARIMA. CCS CONCEPTS• Networks → Wireless access points, base stations and infrastructure; • Computing methodologies → Neural networks;

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Section: Objectivementioning

confidence: 99%

RAN Resource Usage Prediction for a 5G Slice Broker

Gutterman

Grinshpun

Sharma

et al. 2019

Proceedings of the Twentieth ACM International Symposium on Mobile Ad Hoc Networking and Computing

Self Cite

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“…More recently, the authors of [94] studied traffic prediction in cloud analytics and prove that optimizing the choice of metrics and parameters can lead to accurate prediction even under high latency. This prediction is exploited at the application/TCP layer to improve the performance of the application avoiding buffer overflows and/or congestion.…”

Section: Traffic Contextmentioning

confidence: 99%

“…, M is a continuous response, X i (z) is a functional predictor over the variable z, B(z) is the functional coefficient, B 0 is the intercept, and E i is the residual error. Functional regression methods are applied in [94] to predict traffic-related Long Term Evolution (LTE) metrics (e.g., throughput, modulation and coding scheme, and used resources) showing that cloud analytics of short-term LTE metrics is feasible. In [154], functional regression is used to study churn rate of mobile subscribers to maximize the carrier profitability.…”

Section: Regression Analysismentioning

confidence: 99%

A Survey of Anticipatory Mobile Networking: Context-Based Classification, Prediction Methodologies, and Optimization Techniques

Bui

Cesana

Hosseini³

et al. 2017

IEEE Commun. Surv. Tutorials

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218

137

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A growing trend for information technology is to not just react to changes, but anticipate them as much as possible. This paradigm made modern solutions, such as recommendation systems, a ubiquitous presence in today's digital transactions. Anticipatory networking extends the idea to communication technologies by studying patterns and periodicity in human behavior and network dynamics to optimize network performance. This survey collects and analyzes recent papers leveraging context information to forecast the evolution of network conditions and, in turn, to improve network performance. In particular, we identify the main prediction and optimization tools adopted in this body of work and link them with objectives and constraints of the typical applications and scenarios. Finally, we consider open challenges and research directions to make anticipatory networking part of next generation networks.

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