Learning - based Adaptation Framework for Elastic Software Systems

Abstract

Adaptation is a concern for elastic software systems. Conventional methods like Brownout try to deactivate optional computation per request after decoupling the software into different components, to lower the workload when peaks occur. However, resource-intensive components are not always easy to isolate, and some software systems are even not separable. In this paper, we propose a new paradigm that provides each core and mandatory component a corresponding alternative component, with similar function but lower resource consumption, and use reinforcement learning in a feedback loop control for the selfadaptation process. We modified the widely used benchmark RU-BiS to make it weakly coupled and add alternative components. Experiments show that our framework dramatically improves both the efficiency and effectiveness of self-adaptation.

INSMA: An integrated system for multimodal data acquisition and analysis in the intensive care unit

“…A new dataset is analyzed (Step 4) with the same algorithm used in Step 2, the ANN classifies the output of the algorithm in Step 5, and the result of the ANN classification is then mapped into the physiological phase space in Step 6. This methodology reduces the dimensionality of multiscale variability dynamics in a clinically relevant manner, thereby facilitating the development of clinician-centric visualization tools that can be implemented in a bedside display, and easily integrated in the ICU workflow as a generalized early warning system for clinical decompensation in ICU patients [18]. Any algorithm that quantifies multiscale variability dynamics [16,22] can be used to process the waveform data in order to classify the information extracted from the raw data in an intuitive and physiologically relevant manner [23,24], and thus to facilitate the incorporation of subtle and dynamic fluctuations in physiological waveform data. By assessing the current status of a patient in Fig.…”

Eliminating Search Intent Bias in Learning to Rank

“…Wang et al [12] propose a regression based EM algorithm that is based on a position bias click model to handle highly sparse clicks in personal search without relying on randomization. Related research on this topic is also discussed in [35][36][37][38][39][40][41][42][43].…”

Conversational Structure Aware and Context Sensitive Topic Model for Online Discussions

“…However, such external resources are not always available [42][43][44][45][46][47]. The SeaNMF [30] model learns the semantic relationship between words and their context from a skip-gram view of the corpus.…”

“…These topic models rely on a meaningful embedding of words obtained through training on a large-scale high-quality external corpus, which should be both in the same domain and language as the data used for topic modeling. However, such external resources are not always available [42][43][44][45][46][47]. The SeaNMF [30] model learns the semantic relationship between words and their context from a skip-gram view of the corpus.…”