The terminator : an AI-based framework to handle dependability threats in large-scale distributed systems

With the advent of resource-hungry applications such as scientific simulations and artificial intelligence (AI), the need for high-performance computing (HPC) infrastructure is becoming more pressing. HPC systems are typically characterised by the scale of the resources they possess, containing a large number of sophisticated HW components that are tightly integrated. This scale and design complexity inherently contribute to sources of uncertainties, i.e., there are dependability threats that perturb the system during application execution. During system execution, these HPC systems generate a massive amount of log messages that capture the health status of the various components. Several previous works have leveraged those systems’ logs for dependability purposes, such as failure prediction, with varying results. In this work, three novel AI-based techniques are proposed to address two major dependability problems, those of (i) error detection and (ii) failure prediction. The proposed error detection technique leverages the sentiments embedded in log messages in a novel way, making the approach HPC system-independent, i.e., the technique can be used to detect errors in any HPC system. On the other hand, two novel self-supervised transformer neural networks are developed for failure prediction, thereby obviating the need for labels, which are notoriously difficult to obtain in HPC systems. The first transformer technique, called Clairvoyant, accurately predicts the location of the failure, while the second technique, called Time Machine, extends Clairvoyant by also accurately predicting the lead time to failure (LTTF). Time Machine addresses the typical regression problem of LTTF as a novel multi-class classification problem, using a novel oversampling method for online time-based task training. Results from six real-world HPC clusters’ datasets show that our approaches significantly outperform the state-of-the-art methods on various metrics

Time machine : generative real-time model for failure (and lead time) prediction in HPC systems

High Performance Computing (HPC) systems generate a large amount of unstructured/alphanumeric log messages that capture the health state of their components. Due to their design complexity, HPC systems often undergo failures that halt applications (e.g., weather prediction, aerodynamics simulation) execution. However, existing failure prediction methods, which typically seek to extract some information theoretic features, fail to scale both in terms of accuracy and prediction speed, limiting their adoption in real-time production systems. In this paper, differently from existing work and inspired by current transformer-based neural networks which have revolutionized the sequential learning in the NLP tasks, we propose a novel scalable log-based, self-supervised model (i.e., no need for manual labels), called Time Machine1 , that predicts (i) forthcoming log events (ii) the upcoming failure and its location and (iii) the expected lead time to failure. Time Machine is designed by combining two stacks of transformer-decoders, each employing the selfattention mechanism. The first stack addresses the failure location by predicting the sequence of log events and then identifying if a failure event is part of that sequence. The lead time to predicted failure is addressed by the second stack. We evaluate Time machine on four real-world HPC log datasets and compare it against three state-of-the-art failure prediction approaches. Results show that Time Machine significantly outperforms the related works on Bleu, Rouge, MCC, and F1-score in predicting forthcoming events, failure location, failure lead-time, with higher prediction speed

Differential Association of Selected Adipocytokines, Adiponectin, Leptin, Resistin, Visfatin and Chemerin, with the Pathogenesis and Progression of Type 2 Diabetes Mellitus (T2DM) in the Asir Region of Saudi Arabia: A Case Control Study

Background: Sedentary lifestyles, urbanization and improvements in socio-economic status have had serious effects on the burden of diabetes across the world. Diabetes is one of the 10 leading causes of death globally, and individuals with diabetes have a 2–3-fold increased risk of all-cause mortality. Adipose tissue is increasingly understood as a highly active endocrine gland that secretes many biologically active substances, including adipocytokines. However, the exact and discrete pathophysiological links between obesity and T2DM are not yet fully elucidated. Methods: In the current study, we present the association of five diverse adipocytokines, adiponectin, leptin, resistin, visfatin and chemerin, with T2DM in 87 patients (46 males and 41 females) with type 2 diabetes mellitus and 85 healthy controls (44 males and 41 females) from the Asir region of Saudi Arabia. The patients were divided into four groups: normal BMI, overweight, obese and severely obese. The baseline biochemical characteristics, including HbA1c and anthropometric lipid indices, such as BMI and waist–hip ratio, were determined by standard procedures, whereas the selected adipokine levels were assayed by ELISA. Results: The results showed significantly decreased levels of adiponectin in the T2DM patients compared to the control group, and the decrease was more pronounced in obese and severely obese T2DM patients. Serum leptin levels were significantly higher in the females compared to the males in the controls as well as all the four groups of T2DM patients. In the male T2DM patients, a progressive increase was observed in the leptin levels as the BMI increased, although these only reached significantly altered levels in the obese and severely obese patients. The serum leptin levels were significantly higher in the severely obese female patients compared to the controls, patients with normal BMI, and overweight patients. The leptin/adiponectin ratio was significantly higher in the obese and severely obese patients compared to the controls, patients with normal BMI, and overweight patients in both genders. The serum resistin levels did not show any significant differences between the males and females in thr controls or in the T2DM groups, irrespective of the BMI status of the T2DM patients. The visfatin levels did not reveal any significant gender-based differences, but significantly higher levels of visfatin were observed in the T2DM patients, irrespective of their level of obesity, although the higher values were observed in the obese and highly obese patients. Similarly, the serum chemerin levels in the controls, as well as in T2DM patients, did not show any significant gender-based differences. However, in the T2DM patients, the chemerin levels showed a progressive increase, with the increase in BMI reaching highly significant levels in the obese and severely obese patients, respectively. Conclusion: In summary, it is concluded that significantly altered concentrations of four adipokines, adiponectin, leptin, visfatin and chemerin, were found in the T2DM patient group compared to the controls, with more pronounced alterations observed in the obese and highly obese patients. Thus, it can be surmised that these four adipokines play a profound role in the onset, progression and associated complications of T2DM. In view of the relatively small sample size in our study, future prospective studies are needed on a large sample size to explore the in-depth relationship between adipokines and T2DM