Authors: Keon Jang (Intel Labs), Justine Sherry (UC Berkeley), Hitesh Ballani (Microsoft Research), Toby Moncaster (University of Cambridge)
Presenter: Keon Jang
The author started his presentation by stating how shared network effect tenant performance, and motivate this statement by showing simple experiment with memcached, a popular in-memory key-value store by running memcached against netperf. The found out that request latency increases significantly due to network contention. Thus, important aspects such as bandwidth, packet latency, and burst allowances cannot be guaranteed for tenants.
The author introduced Silo, a system that offers these guarantees in multi-tenant datacenters. Silo leverages the tight coupling between bandwidth and delay. In other words, controlling tenant bandwidth leads to deterministic bounds on network queuing delay. The author showed how Silo performs in comparison to existing solutions such as TCP, DCTCP, Okto, Okto+
The authors evaluate Silo across three platforms: testbed experiments with memcached, through ns2 simulations, and through flow-level simulations. The evaluation showed how Silo ensures predictability for message latency and provides simple design where VM placement satisfy both latency and bandwidth requirement
Q: How much bandwidth and latency real applications need?
A: There are other works that specify these needs and tenant can specify these requirements as well.
Q: Did you figure out how to use determinist network calculs to do the modeling of batched packets?
Yes
Presenter: Keon Jang
The author started his presentation by stating how shared network effect tenant performance, and motivate this statement by showing simple experiment with memcached, a popular in-memory key-value store by running memcached against netperf. The found out that request latency increases significantly due to network contention. Thus, important aspects such as bandwidth, packet latency, and burst allowances cannot be guaranteed for tenants.
The author introduced Silo, a system that offers these guarantees in multi-tenant datacenters. Silo leverages the tight coupling between bandwidth and delay. In other words, controlling tenant bandwidth leads to deterministic bounds on network queuing delay. The author showed how Silo performs in comparison to existing solutions such as TCP, DCTCP, Okto, Okto+
The authors evaluate Silo across three platforms: testbed experiments with memcached, through ns2 simulations, and through flow-level simulations. The evaluation showed how Silo ensures predictability for message latency and provides simple design where VM placement satisfy both latency and bandwidth requirement
Q: How much bandwidth and latency real applications need?
A: There are other works that specify these needs and tenant can specify these requirements as well.
Q: Did you figure out how to use determinist network calculs to do the modeling of batched packets?
Yes
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