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.
After an exhaustive search, we have not been able to find any work that characterizes performance of hot spots in cellular networks using real data. The only exception could be the work by Nika et al. @cite_4 , where a data set with more than 700K users is analyzed to understand temporal characteristics of data hot spots. Predictability is assessed, but considering only hot spot repetitions at the exact time and location in the forthcoming week. However, we find that the focus of the work is not on performance, but on load, with hot spots being defined on the basis of relative daily cell traffic.
@cite_4
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Various graphical passwords have been proposed as an alternative to traditional alphanumeric passwords and Microsoft has applied a graphical scheme in the operating system Windows 8. As a new type of password scheme, potential security problems such as hot-spots may exist. In this paper, we study user choice in Windows 8 graphical password scheme by both lab and field studies and analyze the hot-spots caused by user choice. Our analysis shows that there are many significant hot-spots in the background image when users set their passwords using Microsoft’s guidance. Then, based on the data of field study, we conducted a simulated human-seeded attack to prove our conclusion. The success rate of 66.69 and 54.46 also provide strong proof of the hot-spots in Windows 8 graphical password scheme. Finally, we designed a simulated automated attack and obtained a success rate of 42.86 . Many wireless local area network (WLAN) performance estimations are done with the assumption of uniformly distributed stations (STAs). In practice, on the contrary STAs are distributed unevenly among access points (APs), causing hot-spots and under utilized APs in a wireless network. Considering a WLAN is made up of multiple APs, having some APs carrying excessive loads (i.e. hot-spots) degrades both the considered APs as well as the overall network performance. The system performance can be improved by associating incoming STAs effectively throughout the network, in a sense to balance the network load evenly between APs and relieve the hot-spot congestion. Currently employed user association method in IEEE 802.11 WLANs considers only the received signal strength of APs at STAs, and associates STAs to the closest (in signal strength sense) AP, ignoring its load and interference value.Novel user association algorithms are required for congestion relief and network performance improvement. In this work, a new distributed association algorithm taking into consideration not only the received signal strength of the APs at STAs but also AP loadings and interference is proposed. A new AP load calculation method acknowledging the interference between STAs and APs is presented. Our simulations demonstrate that the proposed algorithm can improve the overall system throughput performance more than 50 and offers a better load distribution across the network compared to conventional association algorithm. There is a need to increase the capacity of cellular networks to support the successful growth of mobile broadband usage. Network densification with HetNet (Heterogeneous Networks) is identified as a key method to meet future demands. In this paper one promising HetNet component is studied, the low power pico node. The 3GPP LTE (Long Term Evolution) pico features are described and the deployment aspects are investigated by means of simulations. It is found that with good deployment the capacity increase from low power nodes can be significantly improved. Capturing hot spot traffic has larger impact on performance than radio access features like range extension. Thus, knowing the hot spot cluster positions and ability to deploy the pico node near these positions is important. Also the hotspot positions relative the overlaying macro cells have impact. In the past, there have been several denial of service (DOS) attacks which exhaust some shared resource (e.g., physical memory, process table, file descriptors, TCP connections) of the targeted machine. Though these attacks have been addressed, it is important to continue to identify and address new attacks because DOS is one of most prominent methods used to cause significant financial loss. A recent paper shows how to prevent attacks that exploit the sharing of pipeline resources (e.g., shared trace cache) in SMT to degrade the performance of normal threads. In this paper, we show that power density can be exploited in SMT to launch a novel DOS attack, called heat stroke. Heat stroke repeatedly accesses a shared resource to create a hot spot at the resource. Current solutions to hot spots inevitably involve slowing down the pipeline to let the hot spot cool down. Consequently, heat stroke slows down the entire SMT pipeline and severely degrades normal threads. We present a solution to heat stroke by identifying the thread that causes the hot spot and selectively slowing down the malicious thread while minimally affecting normal threads.