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Related Concept Videos

Reinforcement Schedules01:24

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Positive reinforcement is a powerful method for teaching new behaviors to both animals and humans. B.F. Skinner demonstrated this with his experiments using rats in a Skinner box. When a rat pressed a lever, it received a food pellet. This immediate reward encouraged the rat to repeat the behavior. This method, where a reward follows every instance of the behavior, is known as continuous reinforcement. It is highly effective for establishing new behaviors quickly.
Once a behavior is learned,...
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The rate of a reaction is affected by the concentrations of reactants. Rate laws (differential rate laws) or rate equations are mathematical expressions describing the relationship between the rate of a chemical reaction and the concentration of its reactants.
For example, in a generic reaction aA + bB ⟶ products, where a and b are stoichiometric coefficients, the rate law can be written as:
rate = k[A]m[B]n
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While the differential rate law relates the rate and concentrations of reactants, a second form of rate law called the integrated rate law relates concentrations of reactants and time. Integrated rate laws can be used to determine the amount of reactant or product present after a period of time or to estimate the time required for a reaction to proceed to a certain extent. For example, an integrated rate law helps determine the length of time a radioactive material must be stored for its...
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Relating Reaction Mechanisms
In a multistep reaction mechanism, one of the elementary steps progresses significantly slower than the others. This slowest step is called the rate-limiting step (or rate-determining step). A reaction cannot proceed faster than its slowest step, and hence, the rate-determining step limits the overall reaction rate.
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Rolling resistance, also known as rolling friction, is the force that resists the motion of a rolling object, such as a wheel, tire, or ball, when it moves over a surface. It is caused by the deformation of the object and the surface in contact with each other, as well as other factors like internal friction, hysteresis, and energy losses within the materials. Rolling resistance opposes the object's motion, requiring additional energy to overcome it and maintain movement. In practical...
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Reinforcement01:23

Reinforcement

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Positive and negative reinforcement are key concepts in operant conditioning, a learning process where the consequences of a behavior affect the likelihood of that behavior being repeated.
Positive reinforcement occurs when a behavior is followed by the presentation of a rewarding stimulus, increasing the frequency of that behavior. For example:
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Noah: Reinforcement-Learning-Based Rate Limiter for Microservices in Large-Scale E-Commerce Services.

Zhao Li, Haifeng Sun, Zheng Xiong

    IEEE Transactions on Neural Networks and Learning Systems
    |April 11, 2023
    PubMed
    Summary
    This summary is machine-generated.

    Alibaba developed Noah, a dynamic rate limiter using deep reinforcement learning (DRL) to automatically manage container request rates. This system enhances microservice availability and performance in large-scale e-commerce environments.

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    Area of Science:

    • Computer Science
    • Software Engineering
    • Artificial Intelligence

    Background:

    • Large-scale online services utilize containerized microservices for flexible management.
    • Controlling request arrival rates in containers is critical to prevent overload and ensure stability.
    • Existing rate-limiting mechanisms are insufficient for the diverse and dynamic nature of containers in large e-commerce platforms.

    Purpose of the Study:

    • To address the limitations of current rate-limiting solutions in containerized microservice architectures.
    • To introduce Noah, an automated, dynamic rate limiter tailored for diverse container characteristics.
    • To leverage deep reinforcement learning (DRL) for adaptive and efficient container load management.

    Main Methods:

    • Development of Noah, a dynamic rate limiter employing deep reinforcement learning (DRL).
    • Implementation of a lightweight system monitoring mechanism for efficient container status collection.
    • Integration of synthetic extreme data injection during model training, coupled with curriculum learning for robust performance in extreme scenarios.

    Main Results:

    • Noah has been successfully deployed in Alibaba's production environment for two years, serving over 50,000 containers and 300 microservice types.
    • Demonstrated ability to adapt to diverse production scenarios, improving system availability.
    • Achieved shorter request response times compared to four state-of-the-art rate-limiting solutions.

    Conclusions:

    • Noah provides an effective, automated solution for dynamic rate limiting in large-scale containerized microservices.
    • The DRL-based approach with specialized training strategies enhances system resilience and performance.
    • The system offers significant improvements in availability and response time for e-commerce platforms.