Pingin' in the rain

Schulman, Aaron; Spring, Neil

doi:10.1145/2068816.2068819

Cited by 67 publications

(49 citation statements)

References 5 publications

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“…Impact of lightning on faults in the network and suggestions for mitigation of their effects were also a subject of research [19]. Similarly, a research on how severe weather (e.g., thunderstorms) can cause loss of Internet connection for residential users is presented in [20]. To determine the effect of weather conditions on connectivity, the authors designed and implemented a measurement tool called ThunderPing.…”

Section: Related Workmentioning

confidence: 99%

A Multivariate Approach to Predicting Quantity of Failures in Broadband Networks Based on a Recurrent Neural Network

2015

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In this paper, we present a multivariate recurrent neural network model for short-time prediction of the number of failures that are expected to be reported by users of a broadband telecommunication network. An accurate prediction of the expected number of reported failures is becoming increasingly important to service providers. It enables proactive actions and improves the decision-making process, operational network maintenance, and workforce allocation. Our previous studies have shown that the recursive neural network is flexible enough to approximate the dynamics of the failure reporting process. Development of the model is based on long-term monitoring of failure-reporting processes and experience gained through fault management related to the network of one of the leading Croatian telecom providers (T-HT). Many factors, both in the network and outside the network, influence the time series representing failure reporting. The model encompasses the most important predictor variables and their logical and temporal dependencies. Predictor variables represent internal factors such as profiles of past and current quantities of failures as well as external factors like weather forecasts or announced activities (scheduled maintenance) in the network. External factors have a strong effect on fault occurrence, which finally results in failures reported by users. These factors are quantified and included as input variables to our model. The model is fitted to the data from different sources like an error-logging database, a troubleticket archive, announced settings logs and a meteo-data archive. the model is examined on simulation tests varying the prediction horizons. Assessment of the model's accuracy is made by comparing results obtained by prediction and the actual data. This research represents a real-world case study from telecom operations. The developed prediction model is scalable and adaptable so that other relevant input factors can be added as needed. Hence, the proposed prediction approach based on the model can be efficiently implemented as a functionality in real fault-management processes where a variety of available input data of different volumes exist.

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Section: Related Workmentioning

confidence: 99%

A Multivariate Approach to Predicting Quantity of Failures in Broadband Networks Based on a Recurrent Neural Network

2015

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“…Moreover, choosing optimal default lease time for is far from being an obvious task; short leases times lead to high volume of broadcast traffic, while long lease times can lead to exhaustion on the address pool space [7]. Ultimately, the session duration of a device therefore is not only influenced by the way IP pools and leases are configured, but also by human behavior (e.g., users deciding when to connect or disconnect from the network) as well as external-factors, such as network failures and power outages [21].…”

Section: A Ip Address Assignmentmentioning

confidence: 99%

“…As discussed in [21], "one ping is not enough". Whenever an ICMP packet reaches a router that does not know the MAC address of the destination, the ARP RFC [24] states that the router should drop the packet and then, send a ARP request instead, impacting our results.…”

Section: Number Of Probesmentioning

confidence: 99%

“…Another Internet-wide probing was carried out by anonymous authors in [12]. Since its validity questionable [13] (data was obtained by hacking users' CPEs), we only use their datasets to compare the visibility of the four large ISPs we have probed in Section V. Schulman et al [21] have employed ICMPbased measurements to detect network failures incurred by the weather.…”

Section: Related Workmentioning

confidence: 99%

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How dynamic is the ISPs address space? Towards internet-wide DHCP churn estimation

Moura

Gañán

Lone

et al. 2015

2015 IFIP Networking Conference (IFIP Networking)

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Abstract-IP address counts are typically used as a surrogate metric for the number of hosts in a network, as in the case of ISP rankings based on botnet infected addresses. However, due to effects of dynamic IP address allocation, such counts tend to overestimate the number of hosts, sometimes by an order of magnitude. In the literature, the rate at which hosts change IP addresses is referred to as DHCP churn. Churn rates vary significantly within and among ISP networks, and such variation poses a challenge to any research that relies upon IP addresses as a metric. We present the first attempt towards estimating ISP and Internet-wide DHCP churn rates, in order to better understand the relation between IP addresses and hosts, as well as allow us to correct data relying on IP addresses as a surrogate metric. We propose an scalable active measurement methodology and then validate it using ground truth data from a medium-sized ISP. Next, we build a statistical model to estimate DHCP churn rates and validate against the ground truth data of the same ISP, estimating correctly 72.3% of DHCP churn rates. Finally, we apply our measurement methodology to four major ISPs, triangulate the results to another Internet census, and discuss the next steps to more precisely estimate DHCP churn rates.

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“…In principle, our visualization methods can be applied to other sources of outage input, such as that from iPlane [17], Hubble [11], or localized methods [23]; such integration is future work.…”

Section: Data Sources: Detecting Outagesmentioning

confidence: 99%

Visualizing sparse internet events: network outages and route changes

2013

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To understand network behavior, researchers and enterprise network operators must interpret large amounts of network data. To understand and manage network events such as outages, route instability, and spam campaigns, they must interpret data that covers a range of networks and evolves over time. We propose a simple clustering algorithm that helps identify spatial clusters of network events based on correlations in event timing, producing 2-D visualizations. We show that these visualizations where they reveal the extent, timing, and dynamics of network outages such as January 2011 Egyptian change of government, and the March 2011 Japanese earthquake. We also show they reveal correlations in routing changes that are hidden from AS-path analysis.

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Pingin' in the rain

Cited by 67 publications

References 5 publications

A Multivariate Approach to Predicting Quantity of Failures in Broadband Networks Based on a Recurrent Neural Network

A Multivariate Approach to Predicting Quantity of Failures in Broadband Networks Based on a Recurrent Neural Network

How dynamic is the ISPs address space? Towards internet-wide DHCP churn estimation

Visualizing sparse internet events: network outages and route changes

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