Concurrency control | Transaction processing

Concurrency control

In information technology and computer science, especially in the fields of computer programming, operating systems, multiprocessors, and databases, concurrency control ensures that correct results for concurrent operations are generated, while getting those results as quickly as possible. Computer systems, both software and hardware, consist of modules, or components. Each component is designed to operate correctly, i.e., to obey or to meet certain consistency rules. When components that operate concurrently interact by messaging or by sharing accessed data (in memory or storage), a certain component's consistency may be violated by another component. The general area of concurrency control provides rules, methods, design methodologies, and theories to maintain the consistency of components operating concurrently while interacting, and thus the consistency and correctness of the whole system. Introducing concurrency control into a system means applying operation constraints which typically result in some performance reduction. Operation consistency and correctness should be achieved with as good as possible efficiency, without reducing performance below reasonable levels. Concurrency control can require significant additional complexity and overhead in a concurrent algorithm compared to the simpler sequential algorithm. For example, a failure in concurrency control can result in data corruption from . (Wikipedia).

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From playlist Demonstrations

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From playlist Control Systems in Practice

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Chances are you’ve interacted with something that uses a form of this control law, even if you weren’t aware of it. That’s why it is worth learning a bit more about what this control law is, and how it helps. PID is just one form of feedback controller. It is the simplest type of contro

From playlist Understanding PID Control

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Fuzzy control of inverted pendulum,

Fuzzy control of inverted pendulum, State-feedback controller is designed based on T-S fuzzy model with the consideration of system stability and performance. Details can be found in https://nms.kcl.ac.uk/hk.lam/HKLam/index.php/demonstrations

From playlist Demonstrations

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From playlist Learning-Based Control

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From playlist Understanding Control Systems

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From playlist Control Systems in Practice

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From playlist Go Programming Language Tutorial | Golang Tutorial

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From playlist RubyConf 2010

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From playlist Rails Conf 2012

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From playlist Teamwork and Leadership

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From playlist Python

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From playlist MIT 6.824 Distributed Systems (Spring 2020)

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From playlist Data-Driven Control with Machine Learning

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Distributed concurrency control | Lock (computer science) | Non-blocking algorithm | Commitment ordering | ACID | Isolation (database systems) | Concurrency control | Schedule (computer science) | Deadlock | Two-phase locking | Scalability | Abstract data type | Distributed transaction | Sequential algorithm | Data integrity | Database transaction | Serializability | Global serializability | Cycle (graph theory) | Durability (database systems) | Optimistic concurrency control | Index locking | Atomicity (database systems) | Rollback (data management) | Timestamp-based concurrency control | Multiversion concurrency control | Data recovery | Consistency (database systems) | Read-copy-update | Snapshot isolation | Reliability engineering | Directed graph | Two-phase commit protocol