Disco: Running Commodity Operation Systems on Scalable Multiprocessors

1. Disco: Running Commodity Operation Systems on Scalable Multiprocessors E Bugnion, S Devine, K Govil, M Rosenblum Computer Systems Laboratory, Stanford University Presented by Xiaofei Wang

2. Background • Hardware faster than System Software • Late, incompatible, and possibly bug • Multiprocessor in the market (1990s) • Innovative Hardware • Resource-intensive Modification of OS (hard and time waste for size, etc) • Make a Virtual Machine Monitor (software) between OS and Hardware to resolve the Problem [Old Idea]

3. Two opposite Way for System Software • Address these challenges in the operating system: OS-Intensive • Hive , Hurricane, Cellular-IRIX, etc • innovative, single system image • But large effort. • Hard-partition machine into independent failure units: OS-light • Sun Enterprise10000 machine • Partial single system image • Cannot dynamically adapt the partitioning

4. One Compromise Way between OS-intensive & OS-light • Disco was introduced to allow trading off between the costs of performance and development cost.

5. Target of Disco • Run operating systems efficiently on Innovative hardware: Scalable Shared-Memory Machines • Small implementation effort with no major changes to the OS • Three challenges • Overhead • Resource Management • Communication & Sharing

6. Outline • Disco Architecture • Disco Implementation • Virtual CPUs • Virtual Physical Memory • NUMA Men management • Copy-On-Write Disks. • Virtual I/O devices • Virtual Network interfaces • Running Commodity Operating Systems • Experience • Related Work

7. Disco Architecture(1)

8. Disco Architecture(2) • Interface • Processors: Emulates MIPS R10000 • Physical Memory: An abstraction of main memory residing in a contiguous physical address space starting at address zero. • I/O Devices: Each virtual machine is created with a specified set of I/O devices

9. Virtual CPUs • Schedules virtual machine/CPU as task • Sets the real machines’ registers to the virtual CPU’s • Jumps to the current PC of the virtual CPU, Direct execution on the real CPU • Detection and fast emulation of operations that cannot be safely exported to the virtual machine (privileged instructions or physical memory) • TLB modification • Direct access to physical memory and I/O devices. • Controlled access to memory & Instruction

10. Virtual Physical Memory(1) • Address translation & maintains a physical-to-machine address mapping. • Virtual machines use physical addresses • Disco map physical addresses to machine addresses (FLASH’s 40 bit addresses) • Software reloaded translation-lookaside buffer (TLB) of the MIPS processor (Hardware reload or Software reload?)

11. Virtual Physical Memory(2) • OS want to update the TLB, Disco emulates this operation, compute the corrected TLB entry & insert it. • Pmap ->Quick compute TLB • Each VM has an associated pmap in the monitor • pmap also has a back pointer to its virtual address to help invalidate mappings in the TLB

12. Virtual Physical Memory(4) • MIPS has a tagged TLB, called address space identifier (ASID). • ASIDs are not virtualized, so TLB must be flushed on VM context switches • TLB missing: 2nd level software TLB, idea like cache?

13. NUMA Memory Management(1) • NUMA: memory access time depends on the memory location relative to a processor. local memory faster than non-local memory (SGI) • CCNUMA: all NUMA computers sold to the market use special-purpose hardware to maintain cache coherence (Non CCNUMA system is Complex in programing) • Cache misses should be satisfied from local memory rather (fast) than remote memory (slow) • Dynamic page migration and page replication system

14. NUMA Memory Management(2) • Read and read-shared pages are migrated to all nodes that frequently access them • Write-shared are not, since maintaining consistency requires remote access anyway • Migration and replacement policy is driven by cache-miss-counting facility provided by the FLASH hardware

15. NUMA Memory Management(3) • two different virtual processors of the same virtual machine logically read-share the same physical page, but each virtual processor accesses a local copy. • memmap tracks which virtual page references each physical page. Used during TLB shootdown

16. Disco Mem Management

17. Virtual I/O Devices • Disco intercepts all device accesses from the virtual machine and forwards them to the physical devices • Each Disco device defines a monitor call used by the device driver to pass all command arguments in a single trap • DMA requests to translate the physical addresses into machine addresses.

18. Copy-On-Write Disks • Disco intercepts every disk request that DMAs data into memory • Attempts to modify a shared page will result in a copy-on-write fault handled internally by the monitor.

19. Virtual Network Interface • Communication between virtual machines by accessing data in shared cache • Avoid duplication of data • Use sharing whenever possible

20. Running Commodity Operating Systems • Minor changes to kernel code and data segment (MIPS architecture) • Disco uses original device drivers • Added code to the HAL to pass hints to the monitor for better decision • IRIX

21. SPLASHOS • Thin specialized library OS, supported directly by Disco (no need for virtual memory subsystem)

22. Weakness of Disco • Virtual physical memory is done in Disco by catching TLB misses. However, this only applies to the MIPS architecture. Nearly all other architectures use hardware loaded TLB. The paper doesn't propose a way to provide virtual physical memory without the ability to catch TLB misses, which is often true. It seems that there isn't a simple solution to this. So it may be a considerable problem for implementing virtual machines efficiently on other architectures

23. Experiments(1) • No Flash Hardwar-> Simulation on SimOS • Four Workloads • Execution Overheads • Memory Overheads • Scalability • Dynamic Page Migration and Replication

24. Experiments(2) • Single Hardwar-> SGI Origen200

25. Related Work • System Software for Scalable Shared Memory Machines • Virtual Machine Monitors • Other System Software Structuring Techniques • CC-NUMA Memory Management

26. Problem • In p.3, the authors said that "..., the changes for CC-NUMA machine represent a significantdevelopment cost". Why is it so difficult to adapt the existing OS to multi-processor? In the case of Linux, the machine-dependent codes are only about 2% of the entire system.Do you think it requires less effort to implement virtual machines instead of adapting the existingOS to multiple processors?

27. Does virtual machine the real solution for really utilizing the power of multiple processors? • Or people use it because of its benefits of running multiple OS in the same machine, and theability of running several specialized OSs instead of a general-purpose one?

28. This seems like a good idea.  How has this been improved since then?  Is it still in use? Out of curiosity, how many people and how much time has been spent on Disco? • Why didn't this CC-NUMA multiprocessor architecture kick off? I mean, it is not a very popular system nowadays, right? • ( No further information about it , the page dead at year 2000)

29. The performance studies presented in the paper are largely short workloadsbecause the simulation environment made it impossible to run long runningworkloads.  How well would this operating system run on long workloads?How much does the simulation environment affect the results of runningshort workloads?

30. In the introduction, the authors mention that operating systems specialized for scalable shared memory multiprocessor units requiresignificant changes to operating systems that result in late, incompatible, and buggy system software. They also mention a few prototype systems that try to deal with this.  My question is why none of these were compared to Disco in the performance studies?

31. Why did the use of virtual machine monitors fade away after the 1970s (iewhat problems did they have) and what changes/improvements today havebrought them back?  Will they stick around this time?

32. Can we use CC-NUMA type machine across the internet? So everybody contributes their machine to combine to a giant computer.  (NC? Pervasive Computing?)

33. 1.The paper used shared memory multiprocessors example to show that VMM is the right solution of handling hardware innovations. Do you think VMM isstill the best choice for multi-core CPU structure (one of the latest hardware innovation) ? ( yes)2.In section 7.3, the paper mentioned the similarities between DISCO and microkernel structure. So, are virtual machine monitors microkernels doneright? (reference:http://scholar.google.ca/scholar?hl=en&lr=&safe=off&q=Are+virtual+machine+monitors+microkernels+done+right%3F&btnG=Search) (Andrew Warfield )

34. Thank you~