A Few Things You Don’t Know You Have To Worry About Yet David Boyes CMG Philadelphia, Nov 2008

2 Previews Overview of Scalable Virtualization Requirements Lions: Memory Footprint Tigers: Processor Footprint Bears: I/O Scheduling Opportunities What Evidence of Horrors Exist? Where Does Mixed Workload Fit? What Can We Learn from This Sordid Tale? A Nice Present to Take Home

Overview of Scalability for Virtualization Recent design patters reflect several assumptions about application and infrastructure design: –CPU resources are relatively unlimited and follow Moore’s Law without a known bounding function –Memory is cheap and also follows Moore’s Law to the point that optimization is seldom worth the effort –I/O is expensive and should be avoided at all costs 3

Scalability for Virtualization Observing efforts to produce virtual machines on several different platforms (x86, POWER, System z) tells us a different story: – CPU is a shared resource; idling and unnecessary tasks are bad for overall performance – Memory is as valuable as gold in a shared environment, and most modern operating systems are quite profligate in using it – I/O isn’t so expensive on non-x86 platforms, so there might be a case to consider them for non-traditional (NIC or transactional) workloads 4

Questions to Ask? Are there characteristics of operating systems that render them more virtualization-friendly? How do we exploit those characteristics in modern systems? How do we “discipline” our applications to not be such resource pigs? 5

Lions: Memory Footprint Most modern operating systems are not designed to use memory efficiently for shared resource systems –Use of memory as host caching mechanism due to cost balance between RAM and higher performance I/O subsystems –Application heap allocation in gigabyte ranges 6

Virtualization Memory Management Smaller is Better Use of virtual disks as alternative to larger RAM sizes Tuning buffer space (OpenSolaris) –Iotune –Use of containers Instrumentation is key (no surprises…) 7

Tigers: Processor Footprint Idle timer LRU page stealer Timer interrupt MP effect Dispatch priority Processor emulation passthru 8

Bears: I/O Scheduling Oppts. Grouping of I/O in hypervisor Value of block cache Overhead of I/O intercept in hypervisor 9

Where Does Mixed Workload Fit? No easy answer – needs to be matched against several criteria: –Data proximity –Usage patterns –Flexibility 10

What Can We Learn From This? Optimally, hypervisor solutions need to supply lightweight cross-VM memory pools (cf Plan 9 or Amoeba) Manageability of storage and network virtualization integrated with provisioning will be important Workload mobility is increasingly important, which implies stateless server infrastructure 11

What Can We Learn From This? Software profiling is more important than ever (both at compile and execution time) Consolidation of physical processor architecture does not necessarily imply that logical architecture follows 12

A Present… 13

Early Prototype: OpenVMS on Z

Early Prototype: OpenVMS on System z

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