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Hash based central agent workload partitioning for ceilometer

Describes a hash-based central agent workload partitioning for ceilometer

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Hash based central agent workload partitioning for ceilometer

  1. 1. Eoghan Glynn Hash-based partitioning: Sharing load without sharing (much) knowledge OpenStack Telemetry 1
  2. 2. The background ● inspired by the consistent hash-ring approach used to allocate nodes to conductors in Ironic ● the key idea is to allow the central agents to self- organize while sharing minimal information ● “blissful ignorance” is the key phrase in the BP spec 2
  3. 3. How does it work? ● multiple central agents start up ● tooz group membership primitives allow each agent to be aware of the existence of its peers ● an identical set of discovery extensions are loaded by each agent ● each discoverer attempts to discover all the resources ● but only polls the subset of those resources that it’s been assigned 3
  4. 4. Wait a minute, how does the agent know which resources it’s been assigned? ● each discovered resource has a resource ID ○ maybe a UUID, or IP addr, or something fabricated ● each individual agent uses its knowledge of the cardinality of the agent pool to size a bucket list for hashing ● we rely on the uniform distribution property of the hashing algorithm ● so each agent can answer the question independently: am I responsible for this thing? 4
  5. 5. What happens when an agent dies? Or a fresh agent is started? ● each agent registers a tooz group membership callback so is informed when the pool of live agents changes ● on join/leave events, the hash bucket-list is simply resized ● on the next polling cycle, each agent carves out a different disjoint subset to what went before ● overall we achieve coverage of all resources, modulo a single polling cycle 5
  6. 6. What are the keys win for this approach? ● light ● fast ● simple ● ignorant ● thrifty 6
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