Robust assessment of changes in cellular networks

Mahimkar, Ajay; Ge, Zihui; Yates, Jennifer; Hristov, Chris; Cordaro, Vincent; Smith, Shane; Xu, Jing Song; Stockert, Mark

doi:10.1145/2535372.2535382

Cited by 26 publications

(7 citation statements)

References 26 publications

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“…The employed indicators correspond to 3G sectors, and have been selected based on both internal 1 We explicitly do not show any service performance numbers in the paper for proprietary reasons. However, our observations and inferences are still totally supported by the reported results.…”

Section: B From Key Performance Indicators To the Hot Spot Scorementioning

confidence: 99%

“…The performance of a cellular network is dynamic; it changes with time, affected by internal and external factors, including hardware and software failures, human behavior, weather conditions, and seasonal changes [1]. Operators make huge investments to accurately monitor and understand performance dynamics, as they are key to cellular networks' management, planning [2], and optimization [3].…”

Section: Introductionmentioning

confidence: 99%

“…Indeed, by knowing typical hot spot patterns and the regularities of a given sector, one could make a projection into the future and forecast its behavior. Being able to forecast hot spots would provide a number of advantages: (1) as investment plans are finalized weeks in advance, forecasting future demands would allow operators to optimize capex spending [2]; (2) short-term forecasting would allow operators to proactively troubleshoot their network [8]; (3) it would also allow to dynamically balance resources as an input to self-organizing networks [9].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Hot or Not? Forecasting Cellular Network Hot Spots Using Sector Performance Indicators

Serrà¹,

Leontiadis²,

Karatzoglou³

et al. 2017

2017 IEEE 33rd International Conference on Data Engineering (ICDE)

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Abstract-To manage and maintain large-scale cellular networks, operators need to know which sectors underperform at any given time. For this purpose, they use the so-called hot spot score, which is the result of a combination of multiple network measurements and reflects the instantaneous overall performance of individual sectors. While operators have a good understanding of the current performance of a network and its overall trend, forecasting the performance of each sector over time is a challenging task, as it is affected by both regular and non-regular events, triggered by human behavior and hardware failures. In this paper, we study the spatio-temporal patterns of the hot spot score and uncover its regularities. Based on our observations, we then explore the possibility to use recent measurements' history to predict future hot spots. To this end, we consider tree-based machine learning models, and study their performance as a function of time, amount of past data, and prediction horizon. Our results indicate that, compared to the best baseline, tree-based models can deliver up to 14% better forecasts for regular hot spots and 153% better forecasts for non-regular hot spots. The latter brings strong evidence that, for moderate horizons, forecasts can be made even for sectors exhibiting isolated, non-regular behavior. Overall, our work provides insight into the dynamics of cellular sectors and their predictability. It also paves the way for more proactive network operations with greater forecasting horizons.

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Section: B From Key Performance Indicators To the Hot Spot Scorementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Hot or Not? Forecasting Cellular Network Hot Spots Using Sector Performance Indicators

Serrà¹,

Leontiadis²,

Karatzoglou³

et al. 2017

2017 IEEE 33rd International Conference on Data Engineering (ICDE)

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“…• Impacts of configuration changes: InfPs would like to understand how configuration changes or software updates affect system performance [37,38]. This is a common theme that has emerged in our discussions with several service providers.…”

Section: Motivating Scenariosmentioning

confidence: 99%

Eona

Jiang¹,

Liu²,

Sekar³

et al. 2014

Proceedings of the 13th ACM Workshop on Hot Topics in Networks

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There is a growing recognition among researchers, industry practitioners, and service providers of the need to optimize user-perceived application experience. Network infrastructure owners (i.e., ISPs) have traditionally been left out of this equation, leading to repeated tussles between content providers and ISPs. In parallel, application providers have to deploy complex workarounds that reverse engineer the network's impact on application-level metrics. In this work, we make the case for EONA, a new network paradigm where application providers and network providers can collaborate meaningfully to improve application experience. We observe a confluence of technology trends that are enablers for EONA: the ability to collect large volumes of client-side application measurements, the emergence of novel "big data" platforms for real-time analytics, and new control plane capabilities for ISPs (e.g., SDN, IXPs, NFV). We highlight the challenges and opportunities in designing suitable EONA interfaces between infrastructure and application providers and EONA-enhanced control loops that leverage these interfaces to optimize user experience.

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“…For instance, AWS networks [1] [2] [3], use cellular terminals to enable connectivity with nearby base stations and transmit weather data to central repositories. In literature, much research has explored the stability of cellular up-links on indoor and outdoor environments [4] [5] [6] [7]. In this paper, we concentrate on an indoor environment, and focus on hardware link metric RSSI, to determine the robustness of up-links by considering the level of stability.…”

Section: Introductionmentioning

confidence: 99%

Benchmarking the Robustness of Cellular Up-Links in Automatic Weather Station Networks

Kondela¹,

Nungu²,

Matiko³

et al. 2018

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We present a problem for benchmarking the robustness of cellular up-links, in an automatic weather station (AWS) testbed. Based on the problem, we conduct a small-scale measurement study of robustness, where the AWS is equipped with four (4) cellular modems for weather data delivery. The effectiveness of up-links is challenging because of overlapping spatial-temporal factors such as the presence of good reflectors that lead to multi-path effects, interference, network load or other reasons. We argue that, there is a strong need for independent assessments of their robustness, to perform end-to-end network measurement. However, it is yet difficult to go from a particular measurement to an assessment of the entire network. We extensively measure the variability of Radio Signal Strength (RSSI) as link metric on the cellular modems. The RSSI is one of the important link metrics that can determine the robustness of received RF signals, and explore how they differed from one another at a particular location and instant time. We also apply the statistical analysis that quantifies the level of stability by considering the robustness, referring short-term variation, and determines good up-link in comparison to weak one. The results show that the robustness of cellular up-links exists for an unpredictable period of time and lower than one could hope. More than 50% of up-links are intermittent. Therefore, we plan to extend our work by exploring RSSI thresholds, to develop a classification scheme supporting a decision whether a link is either intermittent or not. This will help in normalizing the level of stability, to design the RSSI estimation metric for the robust routing protocol in weather data networks.

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Robust assessment of changes in cellular networks

Cited by 26 publications

References 26 publications

Hot or Not? Forecasting Cellular Network Hot Spots Using Sector Performance Indicators

Hot or Not? Forecasting Cellular Network Hot Spots Using Sector Performance Indicators

Eona

Benchmarking the Robustness of Cellular Up-Links in Automatic Weather Station Networks

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