Detecting and Localizing Anomalies in Container Clusters Using Markov Models

Detecting the location of performance anomalies in complex distributed systems is critical to ensuring the effective operation of a system, in particular, if short-lived container deployments are considered, adding challenges to anomaly detection and localization. In this paper, we present a framework for monitoring, detecting and localizing performance anomalies for container-based clusters using the hierarchical hidden Markov model (HHMM). The model aims at detecting and localizing the root cause of anomalies at runtime in order to maximize the system availability and performance. The model detects response time variations in containers and their hosting cluster nodes based on their resource utilization and tracks the root causes of variations. To evaluate the proposed framework, experiments were conducted for container orchestration, with different performance metrics being used. The results show that HHMMs are able to accurately detect and localize performance anomalies in a timely fashion.

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dfgcompare: a library to support process variant analysis through Markov models

BMC Medical Informatics and Decision Making ◽

10.1186/s12911-021-01715-3 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Amin Jalali ◽

Paul Johannesson ◽

Erik Perjons ◽

Ylva Askfors ◽

Abdolazim Rezaei Kalladj ◽

...

Keyword(s):

Performance Metrics ◽

Markov Models ◽

Process Analysis ◽

Cohort Analysis ◽

Data Driven ◽

Software Support ◽

Cohort Comparison ◽

Analysis Technique ◽

Variant Analysis ◽

And Performance

Abstract Background Data-driven process analysis is an important area that relies on software support. Process variant analysis is a sort of analysis technique in which analysts compare executed process variants, a.k.a. process cohorts. This comparison can help to identify insights for improving processes. There are a few software supports to enable process cohort comparison based on the frequencies of process activities and performance metrics. These metrics are effective in cohort analysis, but they cannot support cohort comparison based on the probability of transitions among states, which is an important enabler for cohort analysis in healthcare. Results This paper defines an approach to compare process cohorts using Markov models. The approach is formalized, and it is implemented as an open-source python library, named dfgcompare. This library can be used by other researchers to compare process cohorts. The implementation is also used to compare caregivers’ behavior when prescribing drugs in the Stockholm Region. The result shows that the approach enables the comparison of process cohorts in practice. Conclusions We conclude that dfgcompare supports identifying differences among process cohorts.

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Nanoprobe Characterization of Soft SRAM bit Fails in Advanced Technologies

ISTFA 2019: Conference Proceedings from the 45th International Symposium for Testing and Failure Analysis ◽

10.31399/asm.cp.istfa2019p0273 ◽

2019 ◽

Author(s):

Satish Kodali ◽

Chen Zhe ◽

Chong Khiam Oh

Keyword(s):

Yield Loss ◽

Performance Metrics ◽

Root Cause ◽

Advanced Technologies ◽

Capacitance Voltage ◽

Defect Localization ◽

Cv Measurements ◽

Lightly Doped Drain ◽

Dc Characterization

Abstract Nanoprobing is one of the key characterization techniques for soft defect localization in SRAM. DC transistor performance metrics could be used to identify the root cause of the fail mode. One such case report where nanoprobing was applied to a wafer impacted by significant SRAM yield loss is presented in this paper where standard FIB cross-section on hard fail sites and top down delayered inspection did not reveal any obvious defects. The authors performed nanoprobing DC characterization measurements followed by capacitance-voltage (CV) measurements. Two probe CV measurement was then performed between the gate and drain of the device with source and bulk floating. The authors identified valuable process marginality at the gate to lightly doped drain overlap region. Physical characterization on an inline split wafer identified residual deposits on the BL contacts potentially blocking the implant. Enhanced cleans for resist removal was implemented as a fix for the fail mode.

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