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cs6200 > Final Exam > Flashcards

Final Exam Flashcards

1
Q

Pthreads Sync for IPC

A 
• Init'ed w/Pthread_process_shared:
   - pthread_mutexattr_t
   - pthread_condattr_t
• Sync info must be avail to all processes - create mutex in shared memory region
(See video #274 for code sample)
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2
Q

Copy (Messages) vs Shared Memory

A 
Copy (Messages)
CPU cycles to copy data to/from port
• Smaller messages
Map (Shmem)
CPU cycles to map mem to addr space
CPU to copy data to channel
Set up once, use many x || lg block 1 x-> good payoff
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3
Q

Freeing Physical Memory

A

• When should pages be swapped?
Memory usage above threshold (high watermark)
CPU usage below threshold (low watermark)
• Which pages should be swapped?
History based prediction - Least Recently Used (LRU)
Pages without dirty bit set
Avoid non-swappable pages
Catagorize pages into diff types
Second chance variation of LRU - make 2 passes
before deciding which page to swap

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4
Q

Memory Allocation

A

Memory Allocator
• Determines VA->PA mapping, checks validity/perm
Kernel Level Allocators
• Allocate pages for kernel state, processes states
• Keep track of free memory
User-level allocators
• Dynamic process state (heap) using malloc/free
• Once malloc’d, kernel not involved. Used by dlmalloc, jemalloc, hoard, tcmalloc

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5
Q

Homogeneous Architectures

A

• Each node can do any processing step
+ Keeps front end simple - doesn’t mean each node has all data but can get to all data
- How to benefit from caching?
• Scale by adding more nodes

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6
Q

Priority Scheduling

A
  • Tasks have different priority levels
  • Run highest priority task next
  • Can preempt lower priority tasks
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7
Q

Copy-on-Write

A

If new process is created, why copy all the memory?
• Instead, map new VA to original page
• Write protect original page
• If only read, this will save memory & time
• On write, page fault and copy/update page tables
• Only copy pages on write => Copy-on-Write

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8
Q

Split Device Driver Model

A

• Approach: device access ctrl split btwn
front end driver in guest VM (device API)
back-end driver in service VM (or host)
• Modified guest drivers
• Limited to paravirtualized guests
+ Eliminates emul OHs
+ Allow better mgmt of shared devices

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9
Q

Internet Service

A 
• Any service avail through web interface (mail, weather, banking, etc)
3 components:
• Presentation - static content
• Biz logic - dynamic content
• Database tier - data store
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10
Q

Problems with Trap & Emulate

A

• x86 pre 2005:
4 rings, no root/non-root modes yet
HV = ring 0, guest OS = ring 1
• 17 instructions do not trap, fail silently
• i.e. interupts, POPF/PUSHF
• HV doesn’t know there was an error, assumes successful

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11
Q

Strict consistency

A

Strict consistency
• Updates are visible everywhere immediately
• In practice, even on SMP there are no guarantees
• Even harder on DSM
• Doesn’t really exist

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12
Q

More Sync Constructs (All need hardware support)

A
  • Serializers - Good w/priorities, hides need for signalling, condition vars
  • Path expressions - uses reg exp to define access
  • Barriers - Reverse semaphore
  • Rendevous Points - Waits for mult threads to need point
  • Optimistic Wait Free Sync (RCU) - Bet on no conflict
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13
Q

What are the overheads associated with scheduling? Do you understand the tradeoffs associated with the frequency of preemption and scheduling/what types of workloads benefit from frequent vs. infrequent intervention of the scheduler (short vs. long timeslices)?

A
  • Longer timeslice keeps CPU util & throughput high, benefits CPU-bound tasks (fewer cxt switches)
  • Short timeslice issues I/O ops sooner, better CPU and device utilization, and better user-perceived performance.
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14
Q

Device Drivers

A
  • Driver needed for each type of hardware
  • Resp for device access, mgmt, control
  • Provided by device manufactuerer
  • OS standarizes interfaces to drivers
  • Device independence & diversity
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15
Q

Summary of Page based DSM

A
  • Each node contribs some mm
  • Needs local caches for perf (latency)
  • All nodes resposible for part of dist mem
  • Home node mngs access & tracks page owner
  • Explicit replication - possible for load balance, perf, reliability
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16
Q

Performance Considerations

A 
DSM perf metric == access latency
How do we make most from local memory? 
Migration
• Makes sense for SRSW
• Requires data movement
Replication (Caching)
• More general, requires consistency mgmt
• Best for low latency
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17
Q

CPU - Device Interconnect

A
  • Peripheral Component Interconnect (PCI)
  • PCIX - PCI Extended
  • PCI Express (PCIe) - Faster, low latency, more devs
  • SCSI - Connects disks
  • Bridge = converter from one bus to another
  • Bridge/mem controller routes req to I/O = Mem map I/O
  • I/O Port model
  • Interupts: + immediate, - stops current exec
  • Polling: + when ok for OS, - delay or CPU overhead
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18
Q

inode example

A

(Quiz on video #354 & 355)

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19
Q

Memory Virtualization Full

A

• Full virtualization
• Guest OS expects contig phys mem start @ 0
• Virtual (VM), Physical(Guest thinks phys), Machine (Actual phys mem)
Option 1:
• Guest page table: VA => PA, HypVisor: PA => MA
• Still uses TLB, MMU for PA=>MA, software VA=>PA
Option 2:
• Guest page tables VA=>PA
• Hypervisor shadow PT: VA=>MA
• Hypervisor maintains consistence

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20
Q

Hosted Virtualization - Type 2

A
  • Host OS in place of hypervisor
  • Host OS has device drivers / VMM module
  • Guest VMs run on VMM module
  • Natve apps run directly on OS
  • Example: KVM - based on Linux. Guest VMs run on KVM & QEMU for hardware virt.
  • Ex: Fusion, VirtualBox, VMPlayer
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21
Q

Virtual File System

A
  • Abstracts location of file sys - local or remote fs
  • Apps still use kernel i.e. POSIX API
  • OS abstracts drivers & devices
  • OS maintains persistent index of file blocks, size, permissions, etc = inode
  • Dentry - directory of paths to each file in OS mem/cache. ./users/ada => /, /users, /users/ada
  • Superblock abstraction - FS specific info regarding FS layout incl inode, other metadata
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22
Q

Demand Paging

A

• Virtual page memory not always in physical memory
• Phys page frame saved & restored to/from 2ndary storage
Demand Paging
• Pages swapped in/out of mem & swap partition
• Page can be ‘pinned’ - disables swapping

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23
Q

POSIX Shared Memory

A
  • Not as widely supported as SysV
  • Uses ‘files’ that exist in tmpfs
  • Shm_open() - opens ‘file descriptor’
  • mmap() / unmmap() - attach
  • shm_close()
  • shm_unlink()
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24
Q

Shared Memory Design Considerations

A
  • One large segment
  • Pool of small segments -> use queue of seg ids
  • What if data > seg size? Transfer data in rounds (See Project 3!)
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25
Q

Device Types

A
  • Block devices - i.e. disks - r/w blocks of data
  • Character - i.e. keyboard - get/put char
  • Network devices - btwn block & character - stream of data chunks
  • OS standarizes by type
  • OS represents devices as special device files
  • UNIX like systems - /dev, tmpfs, devfs
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26
Q

Disk Access Optimizations

A
  • Caching / buffering - reduce # of disk accesses
  • Buffer cache in main memory
  • Read/write from cache
  • Periodically flush to disk (use fsync)
  • Reduce head mvmt w/ I/O skeduler
  • Prefetching - leverages locality i.e. read 17, also read 18, 19
  • Journaling / Logging - reduce rand access. Writes updates in log with blk, offset, value. Periodically writes to proper location.
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27
Q

Inverted Page Tables

A
  • Process ID and page => index to physical mem

* Supplemented with hashing page tables

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28
Q

Memory Virtualization Paravirtualized

A
  • OS knows it’s in virt environ.
  • No need for contig physical mem
  • Reg pages with HypVisor.
  • Can batch page table updates to reduce VM exists
  • Other optimizations
  • OHs reduced or elim on newer platforms
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29
Q

Hardware Virtualization

A

Key Virt-related hardware features - x86
• AMD Pacifica & Intel Vanderpool technology
• Close 17 bad instructions
• New protection modes - root/non-root = host/guest
• VM Control struct per V(M)CPU, ‘walked by’ hardware
•Extended Page Tables & tagged TLB w/VM ids
• Multiqueue devices & interupt routing
• Security & mgmt support
• Add’l instructions for above features

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30
Q

Hardware vs Software DSM

A

• Hardware - relies on high-end interconnect - $$$
OS thinks it has larger physical memory when memory is really on other nodes
NICs translate remote mm accesses to msgs
NICs involved in all aspects of mm mgmt
• Software
Everything done by software, OS or lingo runtime

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31
Q

Bare Metal Virtualizers - Hypervisors (VMM) - Type 1

A
  • Bare metal / Hypervisor (VMM) - Privledged VM manages hardware resources & supports VMs
  • Xen (Open Source or Citrix) - VMs are called domains. Priv domain, has drivers = dom 0
  • ESX (VMWare) - Drivers in VMM, remote APIs
  • Microsoft Hypervisor
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32
Q

When do task enter the ready queue?

A
  • Creation (fork)
  • Interrupt
  • I/O completion
  • Expiration of time slice
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33
Q

Page Table Entry

A

Flags
• Valid bit (Present bit) - indicates validity of page
• Dirty bit - set when page written to
• Accessed bit - has page been red or written to
• Protection/Permission bits - R, W, X
• U/S - User mode / Supervisor mode only

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34
Q

File Access Operations in Sprite

A

(See Sprite paper)

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35
Q

Virtualization requirements

A
  • Present virtual platform interface to VMs
  • Provide isolation across VMs
  • Protect guest OS from apps
  • Protect VMM from guest OS
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36
Q

/dev types

A

had, sda, tty, null, zero, ppp, lp, mem, console, autoconf, etcÛ

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37
Q

Atomic Instructions

A
  • Hardware specific: Test & Set, Read & Increment, Compare & Swap
  • Guarantees: Atomicity, mut excl, queue all concurrent instructions but one
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38
Q

Passthrough Model of Device Virtualization

A

• Approach: VM level driver configs device access permissions directly
• Has exclusive access to device, bypass HV
• Also called VMM bypass model
- Device sharing difficult
- Guest VM must have exact type of driver for device
- VM migration tricky

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39
Q

Why does cloud computing work?

A
  • Law of Large Numbers - variation in resources needs per client on average over time is constant
  • Economies of scale
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40
Q

Issues with Mutexes & Condition Vars

A

• Limitation of mutexes

  • error prone effect correctness & ease of use
  • lack of expressive power
  • Require low level hardware support - atomic instructions
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41
Q

DSM Design - Consistency Mgmt

A
  • DSM is similar to Shmem Multiprocessors (SMP)
  • In SMP: W-I & W-U, coherence triggered on each write => OH too $$$
  • Push invalidations when data written (active/eager/pessimistic)
  • Pull modification info periodically (reactive/lazy/optimistic)
  • When triggered depends on consistency model
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42
Q

Message Queue System calls

A
  • Msgsnd - Send msg to queue
  • Msgrcv - Receive msg
  • Msgctl - Perform control op
  • Msgget - Get msg identifier
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43
Q

What is consistency model?

A

• Agreement between mem (state) and upper software layers
• Mem guarantees to behave correctlyÛ.
Access ordering
Propagation / Visibiity of updates

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44
Q

What is Virtualization?

A
  • Virtualization allows concurrent execution of multiple OSs (and their apps) on the same physical machine.
  • Virtual resources - each OS thinks that it “owns” hardware resources
  • VM - OS + apps + virtual resources
  • Virt layer - mgmt of phys hardware (hypervisor, VM monitor) incl isolation
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45
Q

Cloud Computing & Animoto

A
  • Poster child of cloud computing
  • Amazon added hardware for xmas, idle rest of yr
  • Opened access to resources via web based API (2006) - Birth of AWS and EC2
  • Went viral - 750k new users - 50 instances -> 3400 in one week
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46
Q

Device Access - Programmed I/O (PIO)

A
  • Control via command registers
  • Data r/w via registers
  • i.e. for 1500B w/8 bit reg or bus = 1 register command, 188 data commands
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47
Q

Processor Virtualization

A

• Guest instructions exec directly by hardware
• For non-priv ops, hardware speed = efficiency
• Priv-ops are trapped to hypervisor:
Illegal: terminate VM
legal: emulate behavior VM OS was expecting

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48
Q

Sprite - Analysis to Design

A
  • Cache with write-back - write-back blocks not accessed within 30 sec
  • Client wants to open file being currently written - get dirty blocks
  • Open goes to server, dirs not cached
  • On concurrent write -> disable caching
  • Sequentiual write sharing - caching & seq semantics
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49
Q

OS Bypass

A
  • Not necessary to go through kernel to get to device
  • OS bypass - user accesses device directly
  • Device regs/data directly accessible
  • Needs user level driver
  • OS retains coarse grain control
  • Device needs user-level driver/library
  • Demux capabiities to send results to correct process
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50
Q

Linux O(1)

A
  • Constant time to select/add task, regardless of task count
  • Pre-emptive, priority based
  • Diff. real-time tasks vs other
  • Different timeslice values for diff priorities
  • Feedback - uses history to boost or lower priority
  • Runqueue = 2 arrays of tasks - active & expired
  • Replaced in Linux 2.6.23 by CFS scheduler
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51
Q

Determine CPU bound vs Memory bound

A
  • Use heuristic based on history
  • Sleep time won’t work - td not sleeping when waiting for mem, takes too much time to comp.
  • Need hardware support - hardware counters - cache use, power use, etc
  • Linux perf tool, oprofile used to access counters
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52
Q

Device Access - Direct Memory Access (DMA)

A
  • Uses DMA controller
  • CPU programs device via cmd registers and via DMA controls
  • DMA controller config with in-memory addr and size of packet buffer
  • i.e. for 1500B w/8 bit reg, 2 instructions but DMA step is complex
  • Memory must be present in physical mem while write is happening - memory must be pinned
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53
Q

Run to Completion Scheduling

A

First-Come First Server (FCFS)
- Tasks in order of arrival | runqueue == FIFO queue
if T1 = 1s | T2 = 10s | T3 = 1s:
Throughput - 3/12s = 0.25 tasks/s
Avg job completion time (1+11+12) / 3 = 8 sec
Avg job waiting time - (0+1+11) / 3 = 4 sec

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54
Q

Cloud Service Models

A
  • On premise - you manage all
  • Infastructure as a service (Iaas) - Amazon EC2 - You manage OS -> Apps
  • Platform as a service (PaaS) - Google App Engine - You manage app & data only
  • Software as a Service (SaaS)- i.e. Gmail - you manage nothing
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55
Q

VFS on Disk

A
  • VFS data structs are software entities maintained by OS FS component
  • file => data blocks on disk
  • inode => track files’ blocks - on disk in a block!
  • Superblock has map of all blocks on device - which are inode, data, free, etc
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56
Q

Why has virtualization become popular only recently?

A
  • Mainframes were not ubiquitous
  • Other hardware was cheap
  • Then, realized servers were underutilized
  • Datacenters getting too large
  • Needed more & more sys admins
  • Escalating power bills
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57
Q

Cloud Computing Requirements

A

• Determine capacity on expected (peak) demand
• When demand exceeds capacity
- dropped requests => lost opportunity
• Ideal
Fine grained pricing | professionally mngd | API Acc
+ Scales elastically with demand instant & automatic
- You don’t own the resources

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58
Q

Overhead of address translation

A
  • Single-level PT: 1 for PT, 1 for mem = 2 accesses
  • Four level PT: 4 for PTs, 1 for mem = 5 accesses
  • Page Table Cache - Trans. Lookaside Buffer (TLB)
  • TLB has all flag bits
  • small # of cache addr => high TLB hit rate due to temporal & spacial locality
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59
Q

Typical Device Access

A
  • Process needs to access disk or NIC
  • Process does system call
  • OS runs in-kernel stack to form packet
  • OS invokes appropriate driver
  • Device driver request config
  • Device performs op
  • Results traverse this call chain in reverse
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60
Q

Cache Coherence - NCC / CC / WU / WI

A
  • Non-cache coherent (NCC) - writing to one cache does not update other caches w/same memory addr
  • Cache Coherent (CC) - Hardware updates all caches
  • Write-invalidate (WI) - If one cache is changed, all others are invlidated | + lower bandwidth used
  • Write-update (WU) - Hardware updates all caches w/addr | + Data avail right away
  • WI/WU determined by hardware
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61
Q

Weak consistency

A

• Sync points - ops avail (r/w/sync) to upper levels
• All updates prior to a sync point will be visible
• No guar what happens in between
• Sync must be called by both writer & reader
• Variations
Single sync operation
Sep sync per subset of state (page)
Sep “entry/acquire” vs “exit/release” ops
+ Limit data movement & coherence ops
- maintain extra state for addl ops

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62
Q

Shortest Job First (SJF) + Preemption

A
  • Use heuristic based on history to estimate time

* Windowed average - hold long did task run last n times?

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63
Q

Sync vs Async

A
  • What happens to calling thread after I/O call?
  • Depends if operation is sync or async
  • Sync - process blocks and waits for I/O to complete
  • Async - process continues, then checks or is notified by device or OS that oper is done (poll vs interupt)
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64
Q

What types of app access algos for DSM?

A
  • Single reader / single writer (SRSW)
  • Mult readers / single writers (MRSW)
  • Mult readers / mult writers (MRMW)
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65
Q

Threads and Simultaeous Multithreading (SMT)

A
  • Co-sked CPU-bound tds -> degrades perf 2x, mem idle
  • Co-sked Mem-bound tds -> Idle CPU cycles
  • Mixing CPU/Mem tds -> BINGO! Avoid contention for processor pipeline, CPU/Mem well utilized
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66
Q

Page Table

A
  • Map to map pages in VM to page frames in DRAM
  • Pages and page frames are same size
  • Virt Addr split into Virt Page No (VPN) & Offset
  • Physical Frame Num (PFN) indexs phys page
  • Unused pages are reclaimed
  • Each process has its own page table pointed to by CPU register (x86 it’s called CR3)
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67
Q

Heterogeneous Architectures

A

• Functionally heterogeneous
Diff nodes, diff tasks/requests
Data doesn’t have to uniformly accessible everywhere
+ Benefit of locality & caching
- Front end more complex
- More complex mgmt
• Scale by knowing demand and adding nodes accordingly

68
Q

Semaphores

A
  • Common sync construct
  • Acts like a traffic light - stop or go
  • Similar to mutex but more general
  • Have an init value - positive integer = max count
  • On Try (wait): If non-0, decrement & proceed
  • On Exit (post): Increment
  • Designed by Dijkstra Dutch - Proberen(P) - Wait, Verhogen (V) - Post
69
Q

Stateless File Server

A

• Stateless - keeps no state
- Can’t support caching & consistentcy mgmt
- Every request self contained -> more bits xfered
+ No resoources used on server side (CPU/mm)
+ On failure, simply restart

70
Q

IPC Command Line Tools

A

• ipcs - List all IPC facilities
-m display shmem info only
• ipcrm - Delete IPC facility
- [shmid] delete specific shmid

71
Q

Statefull File Server

A

• Stateful - keeps client state - needed for practical model to track what is cached
+ Supports locking, caching, inc ops
- On failure, needs checkpointing & recovery mech
- Overheads to maintain state & consistency - this depends on caching mech & consistency protocol

72
Q

Shortest Job First (SJF) Scheduling

A

Schedule tasks in order of exec time
runqueue == ordered queue or tree
if T1 = 1s | T3 = 1s | T2 = 10s:
Throughput - 3/12s = 0.25 tasks/s
Avg job completion time (1+2+12) / 3 = 4.7 sec
Avg job waiting time - (0+1+2) / 3 = 1 sec

73
Q

Why do we care about virtualization

A

NAME?

74
Q

Page Fault

A

If MMU detects physical memory access cannot be performed, it causes a Page Fault.
• Pushes error code on kernel stack (x86 - reg CR2)
• Traps into kernel
• Page fault handler in OS determines action - get page from disk, permission violation, etc

75
Q

Binary Translation

A
  • Main idea - rewrite VM binary to never issue 17 instructions that don’t trap
  • Mendal Rosenblum, founder of VMWare
  • Goal is to run unmodified Oss = full virtualization
  • Approach: dynamic binary translation
  • Inspect code blocks for 17 bad instructions
  • If found, translate to alt sequence - adds OH
76
Q

Multi-Level Page Tables

A

• Flat page tables no longer used
• Now hierarchical, multi-level structures
• Outer page table & inner page table
• More page levels can be added
Important w/64-bit arch. - memory is more sparse
+ more granular, - more memory access time

77
Q

Page Size

A

• Determined by offset size
• 10-bit = 2^10 = 1024 = 1k page size
• 12-bit = 2^12 = 4096 = 4k page size
• Linux default = 4k, can be 2MB (large), 1 GB (huge)
• Solaris/SPARC - 8kB, 4 MB, 2 GB
+ Lg’r page size => small’r pg table => more TLB hits
- internal fragmentation => wasted memory

78
Q

Priority Inversion

A

Higher priority thread may need mutex held by lower priority thread. Lower thread will complete first before giving up the lock, therefore boosting its priority temporarily.

79
Q

Linux Completely Fair Scheduler (CFS)

A
  • Better fairness & interactive task performance
  • Runqueue == Red-Black Tree (self balancing)
  • Ordered by virtual runtime (time spent on cpu)
  • Pick leftmost node (node w/ least time on cpu)
  • Periodically adjust virtual runtime
  • rate faster for low priority / slower for high priority
  • Select task: O(1) | Add task: O(log N)
80
Q

Defining Virtualization

A

• A VM is an efficient, isolated, duplicate of real machine
• Supported by VMM:
1. Provide environ essentially same as orig machine (Fidelity)
2. Programs show minor decrease in speed
3. VMM is in complete control of system resources
• Goals: fidelity, performance, saftey, isolation

81
Q

Hardware Protection Levels

A
  • on x86, 4 protection rings - Highest priv - Ring 0 (OS level), Lowest - Ring 3 (Application level)
  • Can be used to put hypervisor in Ring 0 as root, OS in Ring 1 / apps stay in Ring 3 - non-root
  • Guest which try illegal ops cause Vmexit, when handled by hypervisor, VMEntry back
82
Q

Direct control of device - ioctl()

A

Example - determines size of a blockdevice
fd = open(argvv[1], O_RDONLY);
ioctl(fd, BLKGETSIZE, &numblocks);
close(fd);

83
Q

Cloud Deployment Models

A
  • Public - 3rd party clients/tenants
  • Private - leverage tech internally
  • Hybrid - Public/Priavte - failover, dealing with spikes, redundency, testing
  • Community - Public cloud used by certain type of users
84
Q

Virtualization Advances

A
  • 1st vector - CPU modifications = VTX,VTX2, VTX3
  • 2nd vector - Chipset Focus = VTD
  • 3rd vector - IO Device Focus - DMA/Multiqueue = IOV, VMDq
85
Q

System V Shared Memory

A
  • Segments not necessarily contiguous
  • Shmem is system-wide - limits on # and size
  • Uses unique key created by ftok
  • Steps: Create, attach, detach, destroy (or reboot)
86
Q

Examples of Big Data Stacks

A
  • Hadoop
  • Berkeley Data Analytics Stack (BDAS)
  • HPCC Stack (Nexus-Lexus)
87
Q

Spinlock ‘Delay’ Alternatives

A

• Delay after lock is released
+ Contention improved | + Latency - ok
- Delay - much worse
• Can also delay in each loop. Works on NCC arch but increases delay even more
• Fixed delay - i.e. CPU ID
• Dynamic Delay (backoff based) - rand delay in range based on perceived contention in system
• Percevied = cnt of failed test_n_sets

88
Q

Spinlock Performance Comparisons

A

(from Anderson paper)
• Queue has huge overhead with many procs - worst, best < 7
• Spin-on-read is worse < ~5, best > ~7
• Static delay, dynamic delay, static ref - simiular performance -
• Delays - static better than dynamic
• Delay after every mem ref is better than delay after lock is freed
NO SINGLE GOOD ANSWER!

89
Q

Queueing Lock (Anderson)

A

• Uses array of flags with n elements (n = # of cpus)
• Two pointers - current lock holder & next queue
- Needs atomic operation
- O(N) size
- Latency - more costly r&inc | + Delay | + Contention
(See videos #315 & #316)

90
Q

Device Virtualizations

A
  • For CPUs and mem, less diversity => standarization of interface (ISA level)
  • For devices, more diversity, lack of standards
  • 3 key models to virtialize devices: Passthrough Model, HypVisor Direct Model, Split Device Driver Model
91
Q

Implementing DSM

A
  • DSM must intercept every access to DSM state
  • Need to decide if local or remote
  • Need to trigger coherence msgs
  • OHs should be avoided for local, non-sh state (pgs)
  • Dynamically ‘engage’ and ‘disengage’ DSM as need
  • Use MMU support - traps if invalid/not allowed
  • Trap will pass to DSM layer to send msg
  • Cached content write will trap and pass to DSM for coherence ops
  • Other MMU info useful (dirty, etc)
92
Q

Where can files be stored in DFS with single server and many clients?

A
  • Client memory
  • Client storage device (HD/ SSD)
  • Buffer cache in server memory - The usefulness of this depends on client loads, request interleaving, etc)
93
Q

Buddy Allocator

A

• On memory request, subdivides memory into 2^x chunks and finds the smallest 2^x chunk that can satisfy request.
• On free, check buddy to see if you can aggregate into a larger chunk. Aggregation works well/is fast.
(Video #250)

94
Q

Sprite DFS Access Patterns

A

• 33% of accesses are writes, 75% of files open < 0.5 sec, 90% of files open < 10 sec, 20-30% new data erased within 30 sec, 50% within 5 min, file share rare
=> caching ok but Write-Thru not sufficient, OH too high w/session-sem
• Write-back on close not necessary
• Must support concurrent access but not optimize

95
Q

Peer Distributed Apps

A
  • Big data apps, web search, content sharing, DSM
  • State is distrib & stored in all nodes
  • Each node owns part of the state, provides service
  • All nodes are peers, but may be a mgr node(s)
96
Q

Monitors

A

• Higher level sync construct -MESA, Java
• Specifies shared resource
• Entry procedure
• Possible condition vars
• On entry, locks & checks are done
• On exit, unlock, check, signal
Also, a programming style using threads & concurrency

97
Q

DFS Models

A
  • Client/server
  • File server distributed on multiple machines - mirrored (has limit) or partitioned (scales!)
  • Files store on / served from all machines - Blurs lines between clients & servers
98
Q

File vs Directory Services

A
  • Knowing access patterns
  • i.e. sharing freq, write freq, import of consistent view
  • Two types of files - directories & reg files
  • Could use session-sem for files, UNIX for dirs
  • Or less freq write-back for files than dirs
99
Q

Shared Memory IPC

A

• Physical memory mapped into VA space of multiple processes
• So different VAs map to same PA
• VAs are not the same
• Phys mem doesn’t need to be contiguous
+ Sys calls only for set-up, data copy reduced
- Explicit sync, comm protocol, shmem mgmt

100
Q

Scheduling on multi-CPU systems

A
  • Cache affinity is important - keep task on same CPU as possible
  • Hierarchical sked arch. = Load balancer divides work bx CPU, scheduler for each CPU
  • Multiple memory nodes - closer = faster
  • NUMA - Non Uniform Mem Access platform
  • QPI - Quick Path Interconnect
101
Q

What is the name of the data structure that the scheduler uses as its ready queue?

A
  • Runqueue - tightly coupled to scheduling algo

* OS can use multiple runqueues with different timeslices. See Multi-Level Feedback Queue (MLFQ)

102
Q

Replication vs Partitioning

A

• Replication - Each machine holds all files
+ can load balance => more avail & fault tolerant
- writes become more complex - can write to all or write one and propagate to others. Replicas need algo to resolve collisions -ie voting
• Partitioning - Each machine has subset of files
+ Better availability, scalability, single file writes are simpler
- In failure, some data can be lost. Harder to load balance => hot spots
• Combine techniques for best results

103
Q

Virtualization Technologies

A
  • Virtual Box is virtualization

* Java VM & Virtual Gameboy - not

104
Q

DSM Architecture

A 
• Page based DSM arch
   distrib nodes each contrib local mm
   pool of pages from all nodes
   each pg has id, frame #
• if MRMW
   need local caches for performance
   home (mgr) node drives coherence ops
   all nodes responsible for part of dist mem mgmt
• Home node - keeps state: page accessed, mods,  
   caching enable, locks
• Current owner (may not be home node)
• Exact replicas - for load bal, perf, reliability
105
Q

POSIX Semaphore API

A 
sem_t sem;
sem_init(sem_t •sem, int pshared, int count);
sem_wait(sem_t •sem);
sem_post(sem_t •sem);
Equiv to mutex:
   sem_init(sem, 0, 1);
106
Q

Indexing DSM - Where is particular page?

A 
DSM metadata - each page has:
• address = node ID + page frame #
• node ID = Home node
Global Map (replicated on all nodes)
• object (page) id => manager node id
• manager map avail on each node
Metadata for local pages
• Per page metadata is distrib across mgrs
107
Q

Casual consistency

A
  • Guar they will detect causal relations between updates

* Concurrent writes - no guarantees

108
Q

Basic I/O device features

A

• Have control registers to allow CPU/device comm
Command regs - controls device
Data regs - control data xfer
Status regs - info about device status
• Microcontroller - device ‘cpu’ | Device memory
• Other - i.e. A/D converters, etc

109
Q

System V Shared Memory API

A
  • Shmget(shmid, size, flags) - create or open using key generated by ftok(pathname, prog_id) IPC_CREATE used to create
  • Shmat(shmid, addr, flags) - addr can be NULL, cast addr to type needed
  • Shmdt(shmid) - detach
  • Shmctl(shmid, cmd, buf) - destroy w/IPC_RMID
110
Q

Distributed Shared Memory

A
  • Each node owns state
  • Provides services - mem r/w from any node, consistency protocols
  • Permits scaling beyond limits of a single machine
  • More shared mem at lower cost
  • Slower overall mem access
  • Commodity interconnect tech suport RDMA that provide low latency access to remote memory
111
Q

How many datacenters in the world in 2015? How many sq feet?

A

2011: 510k datacenters - 285.5M sq feet
2015: Est 1 - 1.5B datacenters!

112
Q

File Sharing Semantics in DFS

A
  • Single node (machine) environment - If process rites file, it is avail to other process immediately
  • Multi client - one client may change server but other clients not be updated => must use more relaxed semantics
  • UNIX semantics - every write avail immediately
  • Session semantics - write-back on close(), update on open(). Not great for concurrent sharing or when files stay open for a long time.
  • Lease on cached data (NOT a locking mech) => puts bounds on inconsistency
  • Augment with flush() / sync() API
  • Can use immutable files - can’t delete
  • Transactional guar - all changes atomic
113
Q

Network File System (NFS)

A
  • From Sun
  • System calls Virtual File System (VFS)
  • Divides local / network requests
  • Client talks to server via RPC which talks to server VFS as if reaauest came from server itself
  • Pass back if file exists (not ‘stale’)
114
Q

Requirements of Cloud

A
  • Fungible resources
  • Elastic, dynamic resource allocation
  • Scale: Mgmt at scale, scablable resource alloc
  • Dealing with failures
  • Multi-tenancy - performance & isolation
  • Security
115
Q

DSM - Sharing Granularity

A 
X Cache line granularity?  OH too high for DSM
X Variable granularity - too fine
Õ Page granularity (OS level)
Õ Object granularity (language runtime)
• Beware of false sharing!
116
Q

Spinlocks

A 
• Basic sync construct
• Similar to mutex:
   - mutual exclusion
   - lock/unlock (free)
• Differ from mutex:
   - When lock busy, execution continues, not blk'ed
   - Burns CPU cycles check for avail
• NEED HARDWARE SUPPORTED ATOMIC INSTRUCTS
117
Q

What is a common tasks that is implemented in software in hybrid (hard+software) DSM implementations?

A

• Prefetch pages

Addr translation & triggering invalidations done in hardware!

118
Q

Test and Test & Set Spinlock

A

while ((lock == busy) OR (test_and_set(lock) == busy))
• In this case, lock == busy checks cache, so no contention, 2nd arg not executed
• Also called ‘Spin on Read’ or ‘Spin on Cache’
+ Latency & Delay - ok
- Contention - better than t&s, but CC WI - BAD, CC WU - OK, NCC - No diff
• Complexity with WU - O(N) | WI - O(N^2)

119
Q

Sprite Distributed File System

A
  • Research DFS
  • Paper: Caching in the Sprite File Net. File System
  • Used trace data on file use/access patterns
120
Q

Test & Set

A
  • Atomically returns (test) orig val and sets new value (busy)
  • First thread: test_and_set(lock) = 0 => free
  • Next tds: test_and_set(lock) = 1 => busy. Sets lock to 1 which is ok since it’s already 1
121
Q

Page Table Size

A

32-bit architecture
• Page Table Entry (PTE) - 4 bytes PFN + flags
• VPN = 2^32 / Page Size
• Page size typically 4kb
• (2^32 / 2 ^ 12) • 4 bytes = 4MB per process!
• For 64-bit w/8 byte PTE = 32PB per process!!
• This assumes entry for each VP, needed or not

122
Q

Cloud Enabling Technologies

A
  • Virtualization
  • Resource provisioning (scheduling) - Yarn, Mesos
  • Big Data processing (hadoop, Mapreduce, Spark)
  • Storage: DFS, NoSQL, distib in-mem cache
  • Software-defined resource slices
  • Monitoring - real time processing (Flume, CloudWatch, Log Insight)
123
Q

Remote File System - Comprimise

A

• Allow clients to store some of file locally (blocks)
+ lower latency for cache access
+ Server load reduced => more scalable
• Force clients to interact with server (frequently)
+ Server has insight into client activity
+ Server can control access
- Server more complex, req diff file shring semantics

124
Q

Hypervisor Direct Model of Device Virtualization

A 
• Approach: VMM intercepts all device calls
• Emulate device operation:
   translate to generic I/O op
   translate VMM resident I/O stack
   invoke VMM resident I/O driver
\+ VM decoupled from phys device
\+ Sharing, migration simpler
- Adds latency to device access, complexities in HV drivers
125
Q

Sequential consistency

A
  • Similar to SMP with threads using no locks
  • Memory updates from diff processors may be arbitraily interleaved
  • All processes will see exact same interleaving
  • Ops from same process will appear in order they were issued, not interleaved
126
Q

Pseudo Devices

A
  • Accept and discard all output /dev/null

* Produce var len of pseudo rand nums - /dev/random

127
Q

Cloud as Big Data Engine

A
  • In 2011, 1.8 zettabytes of data created
  • All in US tweet 4320x/day
  • 200 billion feature films - takes 47 million years to watch
  • Data storage layer | Data processing layer | Caching layer | Language front ends | Analytics libs | Continuously streaming data
128
Q

Shared Memory & Sync

A
  • Since memory can be concurrently accessed, must be sync’ed
  • Can use pthreads mutexs & cond vars
  • OS supported IPC sync functions
  • Must coordinate concur access and signal when data is avail
129
Q

Round Robin Scheduling

A
  • Pick up first task from queue (like FCFS)
  • If task is block, it yields to next and goes to back of the line.
  • Can be used with priorities using preemption
  • RR w/interleaving == timeslicing - each thread gets a set amount of time on CPU - Comparable metrics to SJF
130
Q

CPU Bound vs I/O Bound Timeslicing

A

CPU Bound
• Use longer TS => Better throughput/avg. comp time, worse wait time
• Limits cxt swtch overhead
I/O Bound
• Use smaller TS => CPU & device util higher, better perceived performance

131
Q

Cache Coherence and Atomics

A

• Atomics always issued to memory controller
+ No race conditions will occur
- Atomics will be slower due to bus/IC contention
- Generates coherence traffic to update caches - changes or not - also makes op slower

132
Q

Paravirtualization

A

• Goal - give up on unmodified guests
• Modify guest so it knows it’s running in virt environ
• Makes explicit HV calls = hypercalls
• Hypercalls like system calls:
Package context info, spec. hypercall, trap to VM
• i.e. XenSource - Open Source HV, bought by Citrix

133
Q

Segmentation

A
  • Segments have aribitrary granularity
  • Segments correspond to different memory parts e.g. code, heap, stack, data, etc
  • Addr = Segment descriptor + offset
  • Segment size = seg base + limit regs = linear addr
  • Typically used with paging unit to get phys addr
134
Q

Hyperthreading

A
  • Multiple hardware supported execution contexts
  • Multiple sets of registers for separate threads
  • Also called hardware/chip/simultaneous multithreading
  • Still only one CPU
  • Up to 8 sets of registers
  • Can be enabled/disabled at startup
  • Can hide memory access latency
135
Q

Timeslice Scheduling

A

Maximum amount of uninteruptted time given to a task. Also called time quantum. Shares the CPU
+ Short tasks finish sooner => More responsive
+ lengthy I/O ops initiated sooner
- Overheads - interupt, schedule, cxt switch. Keep timeslice > overhead

136
Q

NFS Versions

A
  • Since 80’s
  • NFSv3 - stateless, NFSv4 - Stateful
  • Caching is session based
  • Also supports periodic updates - default: 3 sec files, 30 sec directories
  • Locking - lease-based mech. Limited time for lock.
  • NFSv4 has r/w lock - ‘share reservation’
137
Q

Formula for CPU utilization

A

• [cpu_running_time / (cpu_running_time + context_switching_overheads)] • 100
• The cpu_run_time and cxt_swtch_ovrhds should be calc’ed over a consistent, recurring interval
Helpful Steps to Solve:
• Determine a consistent, recurring interval
• In the interval, each task should be given an opportunity to run
• During that interval, how much time is spent computing? This is the cpu_running_time
• During that interval, how much time is spent cxt switch? This is the cxt_switch_overheads
• Calculate!

138
Q

Cycles Per Instruction (CPI) counter (Federova experiment)

A
  • Memory bound has high CPI
  • CPU bound has CPI of approx 1
  • In Federova experiment, mixed types worked best
  • In real world, most apps use same amont of CPI, so this doesn’t work
  • Ergo, sked’s should be aware of resource contention as well as load balancing.
  • Subsequent work shows Last Level Cache (LLC) is a hardware counter that works well
139
Q

Spinlock Performance Metrics

A
  • Reduce latency - Time to get free lock - ideally immed.
  • Reduce wait time (delay) - Time to stop spin & get lock - ideally immed.
  • Reduce contention - bus/IC traffic - ideally zero
  • First two metrics conflict with 3rd
140
Q

IPC Overview

A 
Processes share memory
  - Data in shared memory
Processes exchange messages
   - Message passing via sockets
Requires sync
   - Mutexes, waiting
141
Q

Test & Set Spinlock

A

(See code in video #309)
+ Latency is minimal - just atomic
+ Delay minimal - spinning on atomic
- Contention - all spinning processors go to mem on each spin

142
Q

Cloud computing vision

A
  • John McCarthy predicated in 1961 that computing would be a utility like electricity or phone
  • Computing == fungible utility
  • Limitations - API lock in, hardware dependencies, latency, privacy, security
143
Q

Cloud Computing Overview

A
  • Shared resources - infastructure & software/services
  • APIs for access & config - web based, libs, CLI
  • Billing/accounting - spot, reservation, entire mkt
  • Typically discreet quant: tiny, S, M, L, XL
  • Managed by cloud provider
  • OS Open Stack, VMWare stack
144
Q

IPC Message Passing

A

• OS creates & maintains a channel - buffer, queue
• OS provides interface to process - i.e. a port
• Processes send/write msgs to a port
• Processes rcv/read msgs from a port
• Kernel required to establish & perform
- overheads
+ simplicity - kernel does mgmt/sync

145
Q

Remote File Service - extremes

A

• Extreme 1 - Upload/download - gets entire file, operates on it, uploads it i.e. ftp, git
+ local r/w at client
- entire file up/down even for small access
- no server control
• Extreme 2 - True Remote File Access - every access done on remote file
+ file accessed centralized, easy consistency
- slow, high network costs, limited scalability

146
Q

Hardware support for memory

A
  • MMU translates virtual address -> physical addr & reports faults
  • Registers hold pointers to page table, base & limit size, no. of segments
  • MMU Cache - Translation Lookaside Buffer (TLB)
147
Q

IPC Message Types

A
  • Pipe - connects processes w/byte stream
  • Msg queues - carry msgs among processes, incl priorities, sked. APIs SysV & POSIX
  • Sockets - Uses ports, kernel level socket buffer, kernel processing - tcp/ip etc
148
Q

Multi-Level Feedback Queue (MLFQ)

A
  • OS can use multiple runqueues with different timeslices.
  • New slices go from shortest to longest timeslice as more time needed as well as vice-versa
  • Fernando Corbato won Turing Award for MLFQ
149
Q

Caching State in DFS

A
  • Client can locally maintain portion of state
  • Client can perform ops on cache state (open/r/w/cl)
  • Required coherence mechanisms - WU / WI get triggered on write to file, on demand, periodically, or on open
  • Client or server driven
150
Q

Block device stack

A
  • File = logical storage unit mapped to phys storage
  • Kernel file system has info how to find/access file
  • OS specifies interface - can replace FS
  • Linux uses POSIX API
  • Generic block layer - standard for all block devices
  • Device driver uses protocol specific APIs
151
Q

inodes with indirect pointers

A

• Solves limit on file size with inodes
• inode has metadata, blk pointers as before (1k blks)
• But also now has ‘indirect pointers’ to a block(s) of pointers - 256k per entry
• Double indirect pointer - block of block of pointers - 64MB
• Triple indirect pointer
+ Allows small inode, access large files
- Slower, more disk accesses

152
Q

Virtual vs Physical Memory

A
  • Virtual address space is much larger than physical
  • OS must allocate physical mem - swaps memory to/from disk.
  • VM has pages, Physical has page frames of same size
  • OS must arbitrate memory use - translate/verify virtual addr -> phys. Addr. Uses page tables or segments
153
Q

ext2: Second Extended Filesystem v2

A

• In Linux, now ext4
• For each block group => # inodes, # disk blocks, start of free blocks
• Group descrip - bitmaps, # free nodes, # dirs
• Bitmaps - tracks free blocks and inodes
• inodes - # from 1 to max, 1 per file 128 bytes
• Data Blocks - file data
(remember, directories are just single files that have their own dentry!)

154
Q

Reader / Writer Locks

A

• Read can have shared access, Write needs exclusive
• Specify type of access for lock
In Linux, rw_lock_t m;
read_lock(m); // critical sec; read_unlock(m);
write_lock(m); // critical sec; write_unlock(m);
Difference in diff OS’s: read_unlock effect, upgrade/downgrade priorities, interaction with sked policy

155
Q

Checkpointing

A

Failure & recovery management technique
• Periodically save process state
• When failure, restart from c.p. => faster
• Simple approach - pause & copy
• Better - Only copy pages that have been modified
except to rebuild, mult. diffs needed
• Other uses - debugging, migration

156
Q

Slab Allocator

A
  • Builds custom object caches of common sizes
  • Caches point to phys contiguous memory
  • Slabs are contiguous memory pages
  • Avoids interal fragmentation
157
Q

What file sharing semantics are supported by NFS and it’s cache consistency mechs?

A
  • Not Unix
  • Maybe a little session based dep on config
  • Maybe a little periodic based dep on config
  • Not immutable

So really none of the above!

158
Q

Distrub File Systems

A
  • Accessed by higher level well definied interface - access via VFS
  • Focus on consistent state
  • Mixed distribution models - replicated or partitioned, peer-like system
  • Can be as simple as local disk and remote disk
159
Q

Shared Memory Multiprocessors

A
  • Bus-based - only one request in flight at a time
  • Interconnect (I/C) based - multiple requests can be in flight at once
  • Also called Symmetric multiprocessors (SMPs)
  • Caches used to hide mem latency - more in shmem due to contention
  • No-write - CPU writes directly to mem, cache invalid
  • Write-thru - both cache and mem written to
  • Write-back - write to cache, mem updated later
160
Q

Cloud Failure Probability

A

Cloud has N = 10 CPUs. Prob of failure 0.03
• Prob of failure 1- (0.97^10) = 26%

n = 100: 1-(0.97^100) = 95%

161
Q

Metrics for Scheduling Algos

A

Throughput
Avg job completion time
Avg job waiting time
CPU Utilization

162
Q

IPC

A

OS-supported mechanisms for interaction amoung processes (coord & communication)
• Message passing - Sockets, pipes, message queues
• Memory-based IPC = Shmem, mem mapped files
• Higher level semantics - RPC, Files
• Sync primatives - Semaphores, mutexes

163
Q

How does scheduling work? What are the basic steps and datastructures involved in scheduling a thread on the CPU?

A

• A CPU scheduler decides how & when tasks (thread or process) access a shared CPU.
• A dataqueue is struct. used
• Steps:
Context switch to selected task
Enter user mode
Set PC to next instruction in scheduled task

164
Q

Other IPC Sync

A

• Message queues
Example: P1 sends ‘ready’, P2 rcvs & sends ‘OK’
• Semaphores
- OS supported comm construct
- Semaphores are binary - either 0 or 1
- Can be used like a mutex

165
Q

inodes

A

• index of all disk blocks correspoinding to file
• Uniquely numbered starting at 1
• Has list of all blocks in a file
• Other metadata - permissions, status, etc
+ Easy to perform seq or rand access to file
- Limit on file size

166
Q

OS Scheduler - Task Assignment (Processes or Threads)

A 
• Assign tasks immediately
   Simple Scheduling (FCFS)
• Assign simple tasks first
   Maximize throughput (SJF)
• Assign complex tasks first
   max. util of CPU, devices, memory
167
Q

Internet Services Architecture

A

• For scale, multi-process, multi-node -> scale-out architecture
1. Boss-worker: front-end distribs reuests to nodes
2. All equal: all nodes can handle all steps (func homogeneous)
3. Specialized nodes: nodes only handle certain steps
(functionaly heterogeneous)

cs6200 (2 decks)

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