SmallTail: Scaling cores and probabilistic cloning requests for web systems Conference Poster

abstract

  • Users quality of experience on web systems are largely determined by the tail latency, e.g., 95th percentile. Scaling resources along, e.g., the number of virtual cores per VM, is shown to be effective to meet the average latency but falls short in taming the latency tail in the cloud where the performance variability is higher. The prior art shows the prominence of increasing the request redundancy to curtail the latency either in the off-line setting or without scaling-in cores of virtual machines. In this paper, we propose an opportunistic scaler, termed SmallTail, which aims to achieve stringent targets of tail latency while provisioning a minimum amount of resources and keeping them well utilized. Against dynamic workloads, SmallTail simultaneously adjusts the core provisioning per VM and probabilistically replicates requests so as to achieve the tail latency target. The core of SmallTail is a two level controller, where the outer loops controls the core provision per distributed VMs and the inner loop controls the clones in a finer granularity. We also provide theoretical analysis on the steady-state latency for a given probabilistic replication that clones one out of N arriving requests. We extensively evaluate SmallTail on three different web systems, namely web commerce, web searching, and web bulletin board. Our testbed results show that SmallTail can ensure the 95th latency below 1000 ms using up to 53% less cores compared to the strategy of constant cloning, whereas scaling-core only solution exceeds the latency target by up to 70%.

publication date

  • 2018-10-18

keywords

  • Art
  • Bulletin boards
  • Clone
  • Cloning
  • Controller
  • Controllers
  • Evaluate
  • Exceed
  • Granularity
  • Latency
  • Line
  • Percentile
  • Performance
  • Redundancy
  • Replication
  • Resources
  • Scaling
  • Strategy
  • Tail
  • Target
  • Testbed
  • Testbeds
  • Theoretical Analysis
  • Virtual Machine
  • Virtual machine
  • Workload

ISBN

  • 9781538651391

number of pages

  • 10

start page

  • 31

end page

  • 40