3,647 research outputs found
Predicting Temporal Aspects of Movement for Predictive Replication in Fog Environments
To fully exploit the benefits of the fog environment, efficient management of
data locality is crucial. Blind or reactive data replication falls short in
harnessing the potential of fog computing, necessitating more advanced
techniques for predicting where and when clients will connect. While spatial
prediction has received considerable attention, temporal prediction remains
understudied.
Our paper addresses this gap by examining the advantages of incorporating
temporal prediction into existing spatial prediction models. We also provide a
comprehensive analysis of spatio-temporal prediction models, such as Deep
Neural Networks and Markov models, in the context of predictive replication. We
propose a novel model using Holt-Winter's Exponential Smoothing for temporal
prediction, leveraging sequential and periodical user movement patterns. In a
fog network simulation with real user trajectories our model achieves a 15%
reduction in excess data with a marginal 1% decrease in data availability
Maat: Performance Metric Anomaly Anticipation for Cloud Services with Conditional Diffusion
Ensuring the reliability and user satisfaction of cloud services necessitates
prompt anomaly detection followed by diagnosis.
Existing techniques for anomaly detection focus solely on real-time
detection, meaning that anomaly alerts are issued as soon as anomalies occur.
However, anomalies can propagate and escalate into failures, making
faster-than-real-time anomaly detection highly desirable for expediting
downstream analysis and intervention.
This paper proposes Maat, the first work to address anomaly anticipation of
performance metrics in cloud services.
Maat adopts a novel two-stage paradigm for anomaly anticipation, consisting
of metric forecasting and anomaly detection on forecasts.
The metric forecasting stage employs a conditional denoising diffusion model
to enable multi-step forecasting in an auto-regressive manner.
The detection stage extracts anomaly-indicating features based on domain
knowledge and applies isolation forest with incremental learning to detect
upcoming anomalies.
Thus, our method can uncover anomalies that better conform to human
expertise.
Evaluation on three publicly available datasets demonstrates that Maat can
anticipate anomalies faster than real-time comparatively or more effectively
compared with state-of-the-art real-time anomaly detectors.
We also present cases highlighting Maat's success in forecasting abnormal
metrics and discovering anomalies.Comment: This paper has been accepted by the Research track of the 38th
IEEE/ACM International Conference on Automated Software Engineering (ASE
2023
Educational Technology and Related Education Conferences for June to December 2015
The 33rd edition of the conference list covers selected events that primarily focus on the use of technology in educational settings and on teaching, learning, and educational administration. Only listings until December 2015 are complete as dates, locations, or Internet addresses (URLs) were not available for a number of events held from January 2016 onward. In order to protect the privacy of individuals, only URLs are used in the listing as this enables readers of the list to obtain event information without submitting their e-mail addresses to anyone. A significant challenge during the assembly of this list is incomplete or conflicting information on websites and the lack of a link between conference websites from one year to the next
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