Handling overload effectively with load balancers
1. Fail Smarter
- Average latency has a spike, availability down
- reasons
- dependencies
- cascading effect
- client change peak
2. Load balancers
- clients - load balancers - servers
- hardware devices
- multi tenant - use VIP - for efficiency
- used for (features)
- scaling
- even traffic distribution
- overload protection
- how
- how to pick a server
- random dice
- round robin
- least conns
- desired algorithm - we use least conns
- simple
- reliable
- even
- max conns?
- how to deal with overload?
- attributes
- cheap
- local
- buffering - having capacity soon
- priority
- reject requests - spillover - choose!
- close the TCP connection
- no buffer
- hang on, wait in a queue - surge queue
- may mkes it take longer
- attributes
- how to pick a server
- some default we set - maxConns - perhost setting
- little’s law
- arrival rate * time shopping = people in the store
- see load balancers how many services we have
- fleet-wide concurrent requests / host count = avg conns
- little’s law
- metrics matter
- latency netwrok latency + 25%
- client side
- server side
- request rate
- latency netwrok latency + 25%
- coral server
- concurrent requests - outstanding request - in one host
- 33% overhead room - dependency failures
3. Actual behave
- increase load to see average latency
- run actual test
- generate graph with outstanding requests
- see the cross of client timeout and p99
4. Abnormal cases
- dependency latency, timeout
- network packet loss
- let server decide what’s the maxCon should be
- coral has
- connection
- worker work thread
- classify requests
- importance
- droppable
- priority
- order
- importance
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文章标题:Handling overload effectively with load balancers
文章字数:215
本文作者:Leilei Chen
发布时间:2020-02-05, 01:13:39
最后更新:2020-02-05, 01:14:06
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