Flexible Magnolia Hosting in the Cloud
Post on 28-Nov-2014
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Flexible Magnolia Hostingin the Cloud
Mathias Seiler
www.mirocloud.com
About us
☁ VMware vCloud™ Powered Service Provider
☁ Hosting hundreds of Magnolia CMS instances
☁ (Hosting Magnolia’s server infrastructure too)
Cloud starts today. With us.http://www.mirocloud.com/
www.mirocloud.com
A few words on Java
☁ Fast and scalable
☁ Stable
☁ But ... Eats memory for breakfast
www.mirocloud.com
A few words on Java
☁ Start with 256M RAM at least (!)
☁ For STK: 512M or more
☁ (btw: not taking Derby into account)
www.mirocloud.com
How to scale?
☁ Throw hardware at the problem?
☁ Try to optimize memory allocations?
☁ Magic?
But before that ...
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About Memory
☁ How is JVM memory “seen” by the OS (kernel) ?
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About Memory
☁ Like a big chunk of memory assigned to a process.
☁ Kernel won’t manage it (except if swapping!)
☁ No visibility
☁ Extremely painful if it’s virtual memory(on a hypervisor, set full reservations)
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Quick poll
☁ Who’s using VMware (vSphere) ?
☁ Hyper-V ?
☁ Xen ?
☁ KVM?
☁ Any other virtualization technology?
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EM4J
☁ Part of VMware vFabric
☁ Elastic Memory for Java
☁ Java Heap Memory Pooling
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How does it work?
☁ Ballooning
☁ Coordination with the Hypervisor
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How does it work?
☁ Balance memory between VMs and hosts*
(*taking VMware’s Distributed Resource Scheduler into account)
www.mirocloud.com
How does it work?OS
JVM
Balloon
OS
JVM
Balloon
OS
JVM
Balloon
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How does it work?
ESXi
vFabric tc
Balloon.jar
OS
Magnolia ...
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Why?
☁ Less memory = less cost/power/licenses ...
☁ (but the most interesting ...)
☁ It’s elastic!
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What does this mean?
☁ You can run applications ...
☁ ... more dynamically
☁ ... with less or no OOM events at all
☁ ... without over-allocating memory
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What does this mean?
☁ In fact:
☁ We even allocate more memorythan before!
☁ Because that allocation isthe maximum memory the JVMis going to consume.
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Numbers
☁ You can save up to 40% of system memory(at least that’s what we measured)
☁ Even if you allocate more than you did before
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“Elastic”
☁ real dynamic workload distribution(“cloud”)
☁ traffic / workload “spikes” flattened out
☁ easy configuration
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Interesting notes
☁ It’s adaptive by design
☁ = very little performance impact
☁ more “busy” JVMs get morememory over time
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