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Scalability - PowerPoint PPT Presentation

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Scalability. By Alex Huang. Current Status. 10k resources managed per management server node Scales out horizontally (must disable stats collector) Real production deployment of tens of thousands of resources

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By Alex Huang

current status
Current Status
  • 10k resources managed per management server node
  • Scales out horizontally (must disable stats collector)
  • Real production deployment of tens of thousands of resources
  • Internal testing with software simulators up to 30k physical resources with 30k VMs managed by 4 management server nodes
  • We believe we can at least double that scale per management server node
balancing incoming requests
Balancing Incoming Requests
  • Each management server has two worker thread pools for incoming requests: effectively two servers in one.
    • Executor threads provided by tomcat
    • Job threads waiting on job queue
  • All incoming requests that requires mostly DB operations are short in duration and are executed by executor threads because incoming requests are already load balanced by the load balancer
  • All incoming requests needing resources, which often have long running durations, are checked against ACL by the executor threads and then queued and picked up by job threads.
  • # of job threads are scaled to the # of DB connections available to the management server
  • Requests may take a long time depending on the constraint of the resources but they don’t fail.
the much harder problem
The Much Harder Problem
  • CloudStack performs a number of tasks on behalf of the users and those tasks increases with the number of virtual and physical resources available
    • VM Sync
    • SG Sync
    • Hardware capacity monitoring
    • Virtual resource usage statistics collection
    • More to come
  • When done in number of hundreds, no big deal.
  • As numbers increase, this problem magnifies.
  • How to scale this horizontally across management servers?
comparison of two approaches
Comparison of two Approaches
  • Stats Collector – collects capacity statistics
    • Fires every five minutes to collect stats about host CPU and memory capacity
    • Smart server and dumb client model: Resource only collects info and management server processes
    • Runs the same way on every management server
  • VM Sync
    • Fires every minute
    • Peer to peer model: Resource does a full sync on connection and delta syncs thereafter. Management server trusts on resource for correct information.
    • Only runs against resources connected to the management server node
  • Assume 10k hosts and 500k VMs (50 VMs per host)
  • Stats Collector
    • Fires off 10k requests every 5 minutes or 33 requests a second.
    • Bad but not too bad: Occupies 33 threads every second.
    • But just wait:
      • 2 management servers: 66 requests
      • 3 management servers: 99 requests
    • It gets worse as # of management servers increase because it did not auto-balance across management servers
    • Oh but it gets worse still: Because the 10k hosts is now spread across 3 management servers. While it’s 99 requests generated, the number of threads involved is three-fold because requests need to be routed to the right management server.
    • It keeps the management server at 20% busy even at no load from incoming requests
  • VM Sync
    • Fires off 1 request at resource connection to sync about 50 VMs
    • Then, push from resource as resource knows what it has pushed before and only pushes changes that are out-of-band.
    • So essentially no threads occupied for a much larger data set.
what s the down side
What’s the Down Side?
  • Resources must reconcile between VM states caused by management server commands and VM states it collects from the physical hardware so it requires more CPU
  • Resources must use more memory to keep track of what amounts to a journal of changes since the last sync point.
  • But data centers are full of these two resources.
resource load balancing
Resource Load Balancing
  • As management server is added into the cluster, resources are rebalanced seamlessly.
    • MS2 signals to MS1 to hand over a resource
    • MS1 wait for the commands on the resources to finish
    • MS1 holds further commands in a queue
    • MS1 signals to MS2 to take over
    • MS2 connects
    • MS2 signals to MS1 to complete transfer
    • MS1 discards its resource and flows the commands being held to MS2
  • Listeners are provided to business logic to listen on connection status and adjusts work based on who’s connected.
  • By only working on resources that are connected to the management server the process is on, work is auto-balanced between management servers.
  • Also reduces the message routing between the management servers.
designing for scalability
Designing for Scalability
  • Take advantage of the most abundant resources in a data center (CPU, RAM)
  • Auto-scale to the least abundant resource (DB)
  • Do not hold DB connections/Transactions across resource calls.
    • Use lock table implementation (Merovingian2 or GenericDao.acquireLockInTable() call) over database row locks in this situation.
    • Database row locks are still fine quick short lock outs.
  • Balance the resource intensive tasks as # of management server nodes increases and decreases
    • Use job queues to balance long running processes across management servers
    • Make use of resource rebalancing in CloudStack to auto-balance your world load.


By Alex Huang

the five w s of unreliability
The Five W’s of Unreliability
  • What is unreliable? Everything
  • Who is unreliable? Developers& administrators
  • When does unreliability happen? 3:04 a.m. no matter which time zone… Any time.
  • Where does unreliability happen? In carefully planned, everything has been considered data centers.
  • How does unreliability happen? Rather nonchalantly
dealing with unreliability
Dealing with Unreliability
  • Don’t assume!
  • Don’t bang your head against the wall!
  • Know when you don’t know any better.
  • Ask for help!
designs against unreliability
Designs against Unreliability
  • Management Servers keeps an heartbeat with the DB. One ping a minute.
  • Management Servers self-fences if it cannot write the heartbeat
  • Other management servers wait to make sure the down management server is no longer writing to the heartbeat and then signal interested software to recover
  • Check points at every call to a resource and code to deal with recovering from those check points
  • Database records are not actually deleted to help with manual recovery when needed
  • Write code that is idempotent
  • Respect modularity when writing your code