Mobile Demand Profiling for Cellular Cognitive Networking

Furno, Angelo; Naboulsi, Diala; Stanica, Razvan; Fiore, Marco

doi:10.1109/tmc.2016.2563429

Cited by 16 publications

(19 citation statements)

References 29 publications

(29 reference statements)

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“…For example, Barlacchi et al [34] released a large-scale Call Detail Records (CDR) dataset from Telecom Italia, containing two-months of calls, SMSs and network traffic data from the city of Milan and Trentino, Italy. Based on the dataset, Furno et al [35] proposed a data analytics framework to builds profiles of the city-wide traffic demand, and identifies unusual situations in network usages, aiming at facilitating the design and implementation of cellular cognitive networking. Cici et al [36] studied the decomposition of cell phone activity series, and connect the decomposed series to socio-economic activities, such as regular working patterns and opportunistic events [37].…”

Section: Mobile Data Analyticsmentioning

confidence: 99%

Deep mobile traffic forecast and complementary base station clustering for C-RAN optimization

Chen

Yang

Zhang

et al. 2018

Journal of Network and Computer Applications

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The increasingly growing data traffic has posed great challenges for mobile operators to increase their data processing capacity, which incurs a significant energy consumption and deployment cost. With the emergence of the Cloud Radio Access Network (C-RAN) architecture, the data processing units can now be centralized in data centers and shared among base stations. By mapping a cluster of base stations with complementary traffic patterns to a data processing unit, the processing unit can be fully utilized in different periods of time, and the required capacity to be deployed is expected to be smaller than the sum of capacities of single base stations. However, since the traffic patterns of base stations are highly dynamic in different time and locations, it is challenging to foresee and characterize the traffic patterns in advance to make optimal clustering schemes. In this paper, we address these issues by proposing a deep-learning-based C-RAN optimization framework. First, we exploit a Multivariate Long Short-Term Memory (MuLSTM) model to learn the temporal dependency and spatial correlation among base station traffic patterns, and make accurate traffic forecast for a future period of time. Afterwards, we build a weighted graph to model the complementarity of base stations according to their traffic patterns, and propose a Distance-Constrained Complementarity-Aware (DCCA) algorithm to find optimal base station clustering schemes with the objectives of optimizing capacity utility and deployment cost. We evaluate the performance of our framework using data in two months from real-world mobile networks in Milan and Trentino, Italy. Results show that our method effectively increases the average capacity utility to 83.4% and 76.7%, and reduces the overall deployment cost to 48.4% and 51.7% of the traditional RAN architecture in the two datasets, respectively, which consistently outperforms the state-of-the-art baseline methods.

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Section: Mobile Data Analyticsmentioning

confidence: 99%

Deep mobile traffic forecast and complementary base station clustering for C-RAN optimization

Chen

Yang

Zhang

et al. 2018

Journal of Network and Computer Applications

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“…The data analytics we adopt to drive our resource orchestration problem is inspired by the temporal classifier of mobile network traffic introduced by [37]. The classifier leverages an agglomerative hierarchical clustering with fine-tuned distance measures, and allows detecting long time periods during which the geographic distribution of the mobile traffic demand does not vary significantly.…”

Section: A Data Analyticsmentioning

confidence: 99%

“…Clearly, time needs to be discretized in order to obtain a finite set of time instants: we thus assume that each time instant refers in fact to a period of duration T . Phase 3: Following the methodology suggested in [37], we run two separate instances of the classifier on the average week, considering two distance metrics to compute the similarity of demands at diverse time instants. The first such metric is the difference of total traffic volumes, which tends to cluster together time instants with equivalent total demands.…”

Section: A Data Analyticsmentioning

confidence: 99%

Prescriptive Analytics for MEC Orchestration

Ceselli

Fiore

Furno

et al. 2018

2018 IFIP Networking Conference (IFIP Networking) and Workshops

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Orchestrating network and computing resources in Mobile Edge Computing (MEC) is an important item in the networking research agenda. In this paper, we propose a novel algorithmic approach to solve the problem of dynamically assigning base stations to MEC facilities, while taking into consideration multiple time-periods, and computing load switching and access latency costs. In particular, leveraging on an existing state of the art on mobile data analytics, we propose a methodology to integrate arbitrary time-period aggregation methods into a network optimization framework. We notably apply simple consecutive time period aggregation and agglomerative hierarchical clustering. Even if the aggregation and optimization methods represent techniques which are different in nature, and whose aim is partially overlapping, we show that they can be integrated in an efficient way. By simulation on real mobile cellular datasets, we show that, thanks to the clustering, we can scale with the number of time-periods considered, that our approach largely outperforms the case without time-period aggregations in terms of MEC access latency, and at which extent the use of clustering and time aggregation affects computing time and solution quality.

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“…For example, Barlacchi et al [10] released a large-scale Call Detail Records (CDR) dataset from Telecom Italia, containing twomonths of calls, SMSs and network traffic data from the city of Milan and the province of Trentino, Italy. Based on the dataset, Furno et al [25] proposed a data analytics framework to builds profiles of the city-wide traffic demand, and identifies unusual situations in network usages, aiming at facilitating the design and implementation of cellular cognitive networking. Cici et al [26] studied the decomposition of cell phone activity series, and connect the decomposed series to socio-economic activities such as regular working patterns and opportunistic social events [27].…”

Section: B Mobile Data Analyticsmentioning

confidence: 99%

Complementary base station clustering for cost-effective and energy-efficient cloud-RAN

Chen

Liu

Fan

et al. 2017

2017 IEEE SmartWorld, Ubiquitous Intelligence &Amp; Computing, Advanced &Amp; Trusted Computed, Scalable Computing &Amp; Commun

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Abstract-The fast growing mobile network data traffic poses great challenges for operators to increase their data processing capacity in base stations in an efficient manner. With the emergence of Cloud Radio Access Network (Cloud-RAN), the data processing units can now be centralized in a data center and shared among several base stations. By clustering base stations with complementary traffic patterns to the same data center, the deployment cost and energy consumption can be reduced. In this paper, we propose a two-phase framework to find optimal base station clustering schemes in a city-wide Cloud-RAN. First, we design a traffic profile for each base station, and propose an entropy-based metric to characterize the complementarity among base stations. Second, we build a graph model to represent the complementarity as link weight, and propose a distanceconstrained clustering algorithm to find optimal base station clustering schemes. We evaluate the performance of our framework using two months of real-world mobile network traffic data in Milan, Italy. Results show that our framework effectively reduces 12.88% of deployment cost and 9.45% of energy consumption compared with traditional architectures, and outperforms the baseline method.

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Mobile Demand Profiling for Cellular Cognitive Networking

Cited by 16 publications

References 29 publications

Deep mobile traffic forecast and complementary base station clustering for C-RAN optimization

Deep mobile traffic forecast and complementary base station clustering for C-RAN optimization

Prescriptive Analytics for MEC Orchestration

Complementary base station clustering for cost-effective and energy-efficient cloud-RAN

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