Joel Liu's personal annotations on this page
Joel bookmarked
on 2005-11-16
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SEDA is an acronym for staged event-driven architecture, and
decomposes a complex, event-driven application into a set of
stages connected by queues. This design
avoids the high overhead associated with thread-based concurrency
models, and decouples event and thread scheduling from application
logic. By performing admission control on each
event queue, the service can be well-conditioned to load, preventing
resources from being overcommitted when demand exceeds service
capacity.
SEDA employs dynamic control to automatically tune runtime parameters
(such as the scheduling parameters of each stage), as well as to
manage load, for example, by performing adaptive load shedding.
Decomposing services into a set of stages also enables modularity and
code reuse, as well as the development of debugging tools for complex
event-driven applications.
This link has been bookmarked by 35 people . It was first bookmarked on 02 Mar 2006, by Joel Liu.
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SEDA is an acronym for staged event-driven architecture, and
decomposes a complex, event-driven application into a set of
stages connected by queues. This design
avoids the high overhead associated with thread-based concurrency
models, and decouples event and thread scheduling from application
logic.
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koei kimMy Ph.D. thesis work at UC Berkeley focused on the development of a robust, high-performance platform for Internet services, called SEDA.
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chunzhong zhangSEDA is an acronym for staged event-driven architecture, and decomposes a complex, event-driven application into a set of stages connected by queues.
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er of open source and commercial systems are based on SEDA and NBIO. These include:
* LimeWire runs runs its server based Web crawler on NBIO.
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Antonio Alvarado Hernández
(tags: server seda projects networking architecture) -
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SEDA: An Architecture for Highly Concurrent Server Applications
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SEDA is an acronym for staged event-driven architecture, and
decomposes a complex, event-driven application into a set of
stages connected by queues. This design
avoids the high overhead associated with thread-based concurrency
models, and decouples event and thread scheduling from application
logic. By performing admission control on each
event queue, the service can be well-conditioned to load, preventing
resources from being overcommitted when demand exceeds service
capacity.
SEDA employs dynamic control to automatically tune runtime parameters
(such as the scheduling parameters of each stage), as well as to
manage load, for example, by performing adaptive load shedding.
Decomposing services into a set of stages also enables modularity and
code reuse, as well as the development of debugging tools for complex
event-driven applications.
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Andrae MuysHow not to do it
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The goal is to build a system capable of supporting massive concurrency (on the order of tens of thousands of simultaneous client connections) and avoid the pitfalls which arise with traditional thread and event-based approaches.
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SEDA is an acronym for staged event-driven architecture, and decomposes a complex, event-driven application into a set of stages connected by queues. This design avoids the high overhead associated with thread-based concurrency models, and decouples event and thread scheduling from application logic. By performing admission control on each event queue, the service can be well-conditioned to load, preventing resources from being overcommitted when demand exceeds service capacity. SEDA employs dynamic control to automatically tune runtime parameters (such as the scheduling parameters of each stage), as well as to manage load, for example, by performing adaptive load shedding. Decomposing services into a set of stages also enables modularity and code reuse, as well as the development of debugging tools for complex event-driven applications.
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SEDA is an acronym for staged event-driven architecture, and decomposes a complex, event-driven application into a set of stages connected by queues. This design avoids the high overhead associated with thread-based concurrency models, and decouples event and thread scheduling from application logic. By performing admission control on each event queue, the service can be well-conditioned to load, preventing resources from being overcommitted when demand exceeds service capacity. SEDA employs dynamic control to automatically tune runtime parameters (such as the scheduling parameters of each stage), as well as to manage load, for example, by performing adaptive load shedding. Decomposing services into a set of stages also enables modularity and code reuse, as well as the development of debugging tools for complex event-driven applications.
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