Lecture

Chaos Engineering: Advanced Principles

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In course

CS-438: Decentralized systems engineering

A decentralized system is one that works when no single party is in charge or fully trusted. This course teaches decentralized systems principles while guiding students through the engineering of their own decentralized system featuring messaging, file sha ...

Description

This lecture covers advanced principles in Chaos Engineering, focusing on risk management, running experiments in production, automation, minimizing blast radius, and tools/resources available. It delves into fault injection testing, beyond Chaos Monkey & Chaos Kong, and discusses the importance of fault injection, hypothesis testing, and minimizing the impact of failures.

Instructors (2)

Pierluca Borsò

Expertise Teaching & PhD Courses Decentralized systems engineering CS-438 A decentralized system is one that works when no single party is in charge or fully trusted. This course teaches decentralized systems principles while guiding students through the engineering of their own decentralized system featuring messaging, file sharing, encryption, and blockchain concepts.

Bryan Alexander Ford

Links DEDIS lab home pagePersonal home page Curriculum vitae https://bford.info/cv.pdf Teaching & PhD PhD Students Shailesh Mishra Past EPFL PhD Students Eleftherios Kokoris Kogias, Kirill Nikitin, Cristina Basescu, Enis Ceyhun Alp, Simone Colombo, Pasindu Nivanthaka Tennage, Louis-Henri Manuel Jakob Merino Past EPFL PhD Students as codirector David Froelicher, Ludovic Barman, Georgia Fragkouli Courses Decentralized systems engineering CS-438 A decentralized system is one that works when n

Official source

Related concepts (28)

Statistical hypothesis testing

A statistical hypothesis test is a method of statistical inference used to decide whether the data at hand sufficiently support a particular hypothesis. Hypothesis testing allows us to make probabilistic statements about population parameters. While hypothesis testing was popularized early in the 20th century, early forms were used in the 1700s. The first use is credited to John Arbuthnot (1710), followed by Pierre-Simon Laplace (1770s), in analyzing the human sex ratio at birth; see .

Fault injection

In computer science, fault injection is a testing technique for understanding how computing systems behave when stressed in unusual ways. This can be achieved using physical- or software-based means, or using a hybrid approach. Widely studied physical fault injections include the application of high voltages, extreme temperatures and electromagnetic pulses on electronic components, such as computer memory and central processing units.

Software testing

Software testing is the act of examining the artifacts and the behavior of the software under test by validation and verification. Software testing can also provide an objective, independent view of the software to allow the business to appreciate and understand the risks of software implementation. Test techniques include, but are not necessarily limited to: analyzing the product requirements for completeness and correctness in various contexts like industry perspective, business perspective, feasibility and viability of implementation, usability, performance, security, infrastructure considerations, etc.

Null hypothesis

In scientific research, the null hypothesis (often denoted H0) is the claim that no relationship exists between two sets of data or variables being analyzed. The null hypothesis is that any experimentally observed difference is due to chance alone, and an underlying causative relationship does not exist, hence the term "null". In addition to the null hypothesis, an alternative hypothesis is also developed, which claims that a relationship does exist between two variables.

System testing

System testing is testing conducted on a complete integrated system to evaluate the system's compliance with its specified requirements. System testing takes, as its input, all of the integrated components that have passed integration testing. The purpose of integration testing is to detect any inconsistencies between the units that are integrated together (called assemblages). System testing seeks to detect defects both within the "inter-assemblages" and also within the system as a whole.

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