Auto-Scaling Web Applications in Clouds: A Taxonomy and Survey <s> Workload Prediction Data Source. <s> Event detection on Twitter has become a promising research direction due to Twitter’s popularity, up-to-date feature, free writing style and so on. Unfortunately, it’s a challenge to analyze Twitter dataset for event detection, since the informal expressions of short messages comprise many abbreviations, Internet buzzwords, spelling mistakes, meaningless contents etc. Previous techniques proposed for Twitter event detection mainly focus on clustering bursty words related to the events, while ignoring that these words may not be easily interpreted to clear event descriptions. In this paper, we propose a General and Event-related Aspects Model (GEAM), a new topic model for event detection from Twitter that associates General topics and Event-related Aspects with events. We then introduce a collapsed Gibbs sampling algorithm to estimate the word distributions of General topics and Event-related Aspects in GEAM. Our experiments based on over 7 million tweets demonstrate that GEAM outperforms the state-of-the-art topic model in terms of both Precision and DERate (measuring Duplicated Events Rate detected). Particularly, GEAM can get better event representation by providing a 4-tuple (Time, Locations, Entities, Keywords) structure of the detected events. We show that GEAM not only can be used to effectively detect events but also can be used to analyze event trends. <s> BIB001 </s> Auto-Scaling Web Applications in Clouds: A Taxonomy and Survey <s> Workload Prediction Data Source. <s> One of the biggest advantages of cloud infrastructures is the elasticity. Cloud services are monitored and based on the resource utilization and performance load, they get scaled up or down, by provision or de-provision of cloud resources. The goal is to guarantee the customers an acceptable performance with a minimum of resources. Such Quality of Service (QoS) characteristics are stated in a contract, called Service Level Agreement (SLA) negotiated between customer and provider. The approach of this paper shows that with additional imprecise information (e.g. expected daytime/week- time performance) modeled with fuzzy logic and used in a behavior, load and performance prediction model, the up and down scaling mechanism of a cloud service can be optimized. Evaluation results confirm, that using this approach, SLA violation can be minimized. <s> BIB002
It is necessary to study the past workload history to understand the characteristics of the workload, including its intensity and mix during each time frame, to predict it. Besides workload history, individual applications can rely on available information from other aspects to predict request bursts that are impossible to be derived from past workload data alone, such as weather information for an outdoor application, and political events for a news feed application. However, the relevant parameters are application specific and thus this feature is hard to integrate into a general-purpose auto-scaler. Besides, it is also challenging to devise a prediction algorithm with real-time accuracy for resource provisioning, because there are too many parameters in the model and errors can quickly accumulate. The work by Frey et al. BIB002 considers multiple outside parameters in an auto-scaler. Their approach integrates all the prediction information into a fuzzy controller. Though it is challenging to predict the exact workload intensity using outside information, it is viable to in a timely manner detect events that may affect incoming workload intensity through social media and other channels BIB001 . Since this is a broad topic itself, we focus on prediction algorithms only based on workload history.