text: 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.
  @cite_26: Real-time communication (RTC) applications such as VoIP, video conferencing, and online gaming are flourishing. To adapt and deliver good performance, these applications require accurate estimations of short-term network performance metrics, e.g., loss rate, one-way delay, and throughput. However, the wide variation in mobile cellular network performance makes running RTC applications on these networks problematic. To address this issue, various performance adaptation techniques have been proposed, but one common problem of such techniques is that they only adjust application behavior reactively after performance degradation is visible. Thus, proactive adaptation based on accurate short-term, fine-grained network performance prediction can be a preferred alternative that benefits RTC applications. In this study, we show that forecasting the short-term performance in cellular networks is possible in part due to the channel estimation scheme on the device and the radio resource scheduling algorithm at the base station. We develop a system interface called PROTEUS, which passively collects current network performance, such as throughput, loss, and one-way delay, and then uses regression trees to forecast future network performance. PROTEUS successfully predicts the occurrence of packet loss within a 0.5s time window for 98 of the time windows and the occurrence of long one-way delay for 97 of the time windows. We also demonstrate how PROTEUS can be integrated with RTC applications to significantly improve the perceptual quality. In particular, we increase the peak signal-to-noise ratio of a video conferencing application by up to 15dB and reduce the perceptual delay in a gaming application by up to 4s.
  @cite_10: The Network Weather Service is a generalizable and extensible facility designed to provide dynamic resource performance forecasts in metacomputing environments. In this paper, we outline its design and detail the predictive performance of the forecasts it generates. While the forecasting methods are general, we focus on their ability to predict the TCP IP end-to-end throughput and latency that is attainable by an application using systems located at different sites. Such network forecasts are needed both to support scheduling, and by the metacomputing software infrastructure to develop quality-of-service guarantees.
  @cite_33: Extremely slow, or straggler, tasks are a major performance bottleneck in map-reduce systems. Hadoop infrastructure makes an effort to both avoid them (through minimizing remote data accesses) and handle them in the runtime (through speculative execution). However, the mechanisms in place neither guarantee the avoidance of performance hotspots in task scheduling, nor provide any easy way to tune the timely detection of stragglers. We suggest a machine-learning approach to address these problems, and introduce a slowdown predictor - an oracle to forecast how much slower a task will run on a given node, compared to similar tasks. Slowdown predictors can be embedded in the map-reduce infrastructure to improve the agility and timeliness of scheduling decisions. We provide initial evaluation to demonstrate the viability of our approach, and discuss the use cases for the new paradigm.
  @cite_8: 
summary: In terms of forecasting, we find that, over the past years, there have been some attempts to predict network performance based on historical network measurements. The network weather service @cite_10 was one of the first systems to make predictions of TCP IP throughput and latency. Another example is the Proteus @cite_26 system, which uses regression trees to forecast short-term performance in cellular networks, with the objective to proactively adapt mobile application network patterns. Similarly, Sprout @cite_8 attempts to predict immediate network load in cellular networks to optimize TCP-layer parameters. Finally, in @cite_33 , the authors use gradient boosted trees to forecast hot spots within a data center, in order to avoid stranglers and, therefore, speed up map-reduce computation. In general, all these works focus on very short-term forecasts (in the order of seconds or, at most, minutes) and, consequently, they consider data sets spanning only very short time periods. Further approaches are either not applied to cellular networks or do not consider performance hot spots.