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Transcript
CS 620 Advanced Operating
Systems
Lecture 1 - Introduction
Professor Timothy Arndt
BU 331
Operating Systems Review
• An operating system is a program which is
interposed between users and the hardware
of a system
• An operating system can also be seen as a
manager of resources (e.g. processes, files
and I/O devices)
 Examples include Windows 98, Windows XP,
Mac OS X, Palm OS, and various varieties of
UNIX (HP-UX, Linux, AIX, etc.)
The Place of Operating
Systems
Early Operating Systems
 Early computers were controlled directly by a
programmer or computer operator who entered
a job (given on a deck of punched cards) and
collected the output (from a line printer).
 Groups of jobs were placed together in a single
deck giving rise to batch systems.
 In order to minimize wasted time, a program
called an operating system was developed. The
program was always in the computer’s memory
and controlled the execution of the jobs.
Early Operating Systems
Batch Operating Systems
 In a batch system, various types of cards were
put together in a deck
• Job control cards contained cards often
distinguished by a particular character in the first
row, column position. These cards contained
instructions for the OS written in the machine’s job
control language (JCL)
• Program source code (in a language such as
FORTRAN) were compiled into an executable form.
• Data cards contained the data needed for a run of the
program.
Batch Operating Systems
Multiprogramming System
 These systems were inefficient since the CPU was idle
while a running program waited for (e.g.) a slow I/O
operation to finish.
 In order to get around this problem, several jobs were
kept in memory, and when the running program was
blocked waiting for I/O, the OS switched to one of the
other jobs. This type of system is called a
multiprogramming system.
• Note that this type of system is still not interactive.The user
must wait for his job to finish before he sees the output.
Multiprogramming System
Timesharing systems
 As time went on, users were connected to the
CPU via terminals, and the switching between
jobs was fast enough that each user had the
illusion of being the sole user of the system (if
the system wasn’t overloaded!). This type of
system is called a timesharing system.
• In this type of system the OS’s scheme for control
of the CPU must be much more complicated. In
general, each job consists of one or more processes.
• Processes can create other processes called child
processes.
Processes
Processes
• A running process consists of several segments in
the computer’s main memory.
 The text segment contains the processes executable
machine instructions.
 The global segment contains global and static (variables
declared in a procedure whose value doesn’t change
between invocations of the procedure) variables.
 The data segment (or heap) contains dynamically
allocated memory.
 The stack contains activation records for subprograms.
The activation records contain local variables, parameters,
return location, etc. When a subprogram is called an
activation record is pushed on the stack. When a
subprogram exits, it’s record is popped off the stack.
A Running Process
Processes
 Each process has its own virtual address space.
The size of the space depends on the word size
of the computer.
 The virtual address space is in general larger
than the computer’s physical memory, so some
virtual memory scheme is needed.
 The OS ensures the virtual to physical mapping
for each process and also that each process can
only access memory locations in its own
address space.
File System
 Another important resource that the OS
manages is the set of files that programs access.
• The file system is typically structured as a tree in
which leaf nodes are files and interior nodes are
directories.
• Even if files are on separate physical devices, they
can be combined in a single virtual hierarchy.
 The command used to add a new subtree (on a separate
physical device) to the file system is called mount.
• A single file (or directory) can be placed in multiple
directories without copying the file by linking the
file. A link is a special type of file.
File System
Virtual File Hierarchy
Interprocess Communication
 Separate processes can communicate with each
other using an OS provided service called
Interprocess Communication (IPC).
 UNIX processes can use sockets or pipes to
establish communication with other processes.
 IPC and process spawning are relatively slow,
leading to the idea of light-weight processes or
threads in many modern OSs.
 Threads spawned by the same process can
communicate through shared memory.
Interprocess Communication
User vs. Kernel Mode
 In order to make the system more stable, many
OSs distinguish between processes operating in
user mode versus those operating in kernel
mode.
 User mode processes can access only their
memory locations in their own address space,
cannot directly access hardware, etc. Kernel
mode processes have no such restrictions.
• The fewer processes which run in kernel mode, the
more robust the system should be. On the other
hand, mode switching slows down the system.
User vs. Kernel Mode
Other OS Requirements
• Besides controlling files, processes and IPC,
an OS has several other important tasks:
 Controlling I/O and other peripheral devices
 Networking
 Security and access control for the various
resources
 Processing user commands
Introduction to Distributed
Systems
 Distributed systems (as opposed to centralized
systems) are composed of large numbers of
CPUs connected by a high-speed network.
Definition of a Distributed
System
• A distributed system is:
• A collection of independent
computers that appears to its
users as a single coherent
system.
Introduction to Distributed
Systems
 Advantages of distributed systems:
• Some applications involve spatially separated
machines.
• If one machine crashes, the system as a whole can
still survive.
• Computing power can be added in small increments.
• Allow many users to share expensive peripherals
like color printers.
• Spread the workload over the available machines in
the most cost effective way.
Introduction to Distributed
Systems
 Disadvantages of distributed systems:
• Less software exists for distributed systems.
• The network can saturate or cause other problems.
• Easy access also applies to secret data.
Classification of Multiple
CPU Systems
 Distributed systems are multiple CPU systems
(as are parallel systems - we don’t distinguish
between these two).
 In order to compare various multiple CPU
systems, we would like a classification.
 There is no completely satisfactory
classification. The most well-known (but now
outdated) is due to Flynn:
• MIMD
 Multiprocessor (shared memory)
 Multicomputer (no shared memory)
Introduction to Distributed
Systems
• SIMD (Single Instruction Multiple Data Stream)
• SISD (Single Instruction Single Data Stream):
traditional computers.
• MISD (Multiple Instruction Stream Single Data
Stream): No known examples.
 Each of these categories can further be
characterized as either:
•
•
•
•
bus or
switch
tightly coupled or
loosely coupled
Hardware Concepts
1.6
Bus-Based Multiprocessors
 Bus-based multiprocessors consist of some
number of CPUs all connected to a common
bus, along with a memory module.
• A typical bus has 32 or 64 address lines, 32 or 64
data lines, and 32 or more control lines, all operating
in parallel.
• Since there is just one memory, if one CPU writes a
word of memory, and another CPU immediately
reads that word, the value read will be that written.
• The memory is said to be coherent.
• This configuration soon causes the bus to be
overloaded.
Bus-Based Multiprocessors
• A bus-based multiprocessor.
1.7
Bus-Based Multiprocessors
 The solution to this problem is to add a highspeed cache memory between the CPU and the
bus.
 The cache holds the most recently accessed
words. All memory request go through the
cache.
 The probability that a requested word is found
in the cache is called the hit rate.
 If the hit rate is high, the bus traffic will be
dramatically reduced.
Bus-Based Multiprocessors
 The use of a cache gives rise to the cache
coherence problem.
• One solution is the use of a write-through
cache.
• The caches must also the monitor the bus
and invalidate cache entries when writes to
that address occur.
 This process is called snooping.
• Most bus-based multiprocessors use these
techniques.
Bus-Based Multiprocessors
• Facts about caches:
• They can be write through, when the processor
issues a store the value goes in the cache and also is
sent to memory
• They can be write back, the value only goes to the
cache.
 In this case, the cache line is marked dirty and when it is
evicted it must be written back to memory.
• Because of the broadcast nature of the bus it is not
hard to design snooping caches that maintain
consistency.
Bus-Based Multiprocessors
 Bus-based multiprocessors are also called
Symmetric Multiprocessors (SMPs) and they
are very common.
 The bus can also connect multiple processors
on the same die, leading to Multicore Systems
 Disadvantage (this is really a limitation)
• Cannot be used for a large number of processors
since the bus bandwidth grows with the number of
processors and this gets increasingly difficult and
expensive to supply.
• Moreover the latency grows as the number of
processors for both speed of light and more
complicated (electrical) reasons.
Symmetric Multiprocessors
Processor
Processor
Memory
Processor
I/O
LAN
Processor
Two Non-SMP
Multiprocessors
Processor
Memory
Private
Memory
Private
Memory
I/O
LAN
Processor
Two Non-SMP
Multiprocessors
Processor
Processor
Memory
I/O
LAN
Dual-core Dual-Processor
System
Switched Multiprocessors
• To build a multiprocessor with more than
(say) 64 processors, a different method is
needed to connect the CPUs with the
memory.
 One interconnection method is the use of a
crossbar switch in which each CPU is
connected to each interleaved bank of memory.
• This method requires n2 crosspoint switches for n
memories and CPUs, which can be expensive for a
large n.
Switched Multiprocessors
a) A crossbar switch
b) An omega switching network
1.8
Switched Multiprocessors
 The omega network is an example of a
multistage network which requires a smaller
number of switches - (nlog2n)/2 with log2n
switching stages.
• The number of stages slows down memory access,
however.
 Another alternative is the use of hierarchical
systems called NUMA (NonUniform Memory
Access).
• Each CPU accesses its own memory quickly, but
everyone else’s more slowly.
A CC-NUMA
Proc 1
cache
I/O
Proc 2
cache
D
Memory 0
D
Proc 3
Proc 4
cache
cache
Memory 1
I/O
NUMA
 Production of cc-NUMAs is well-supported by
the AMD Opteron (e.g. by SGI).
 Other cc-NUMA systems are being built using
Intel’s Itanium processor with additional
hardware support (e.g. by HP running HP-UX).
NUMA
 CC-NUMAs (Cache Coherent NUMAs) are
programmed like SMPs but to get good
performance:
• must try to exploit the memory hierarchy and have
most references hit in your local cache
• most others must hit in the part of the shared
memory in your "node” (CPUs on the local bus).
 NC-NUMAs (Non Coherent NUMAs) are still
harder to program as you must maintain cache
consistent manually (or with compiler help).
Bus-Based Multicomputers
 Multicomputers (with no shared memory) are
much easier to build (i.e. scale much more
easily).
 Each CPU has a direct connection to its own
local memory.
 The only traffic on the interconnection network
is CPU-to-CPU, so the volume of traffic is
much lower.
 The interconnection can be done using a LAN
rather than a high-speed backplane bus.
Bus-Based Multicomputers
 In some sense all the computers on the internet
form one enormous multicomputer.
 The interesting case is when there is some
closer cooperation between processors; say the
workstations in one distributed systems
research lab cooperating on a single problem.
 Application must tolerate long-latency
communication and modest bandwidth, using
current state-of-the-practice.
Switched Multicomputers
• The final class of systems are switched
multicomputers.
 Various interconnection networks have been proposed
and built. Examples are grids and hypercubes.
 A hypercube is an n-dimensional cube. For an ndimensional hypercube, each CPU has n connections to
other CPUs.
 Hypercubes with 1000s of CPUs (Massively Parallel
Processors or MPPs) have been available for several
years.
Switched Multicomputers
a) Grid
b) Hypercube
1-9
Software Concepts
System
Description
Main Goal
DOS
Tightly-coupled operating system for multiprocessors and homogeneous
multicomputers
Hide and manage
hardware
resources
NOS
Loosely-coupled operating system for
heterogeneous multicomputers (LAN and
WAN)
Offer local
services to remote
clients
Middleware
Additional layer atop of NOS implementing
general-purpose services
Provide
distribution
transparency
• DOS (Distributed Operating Systems)
• NOS (Network Operating Systems)
• Middleware
Network Operating Systems
 Network operating systems allow one or
machines on a LAN to serve as file servers
which provide a global file system accessible
from all workstations.
 An example of the use of a file server is NFS
(Network File System).
• The file server receives requests from nonserver
machines called clients, to read and write files.
 It is possible that the machines all run the same
OS, but it is not required.
Network Operating Systems
 A situation like this, where each machine has a
high degree of autonomy and there are few
system-wide requirements is known as a
network operating system.
 It is apparent to the users such a system consists
of numerous computers.
• There is no coordination among the computers,
except that the client-server traffic must obey the
system’s protocols.
• The next step up consists of tightly-coupled
software on the same loosely-coupled hardware.
Network Operating Systems
• General structure of a network operating system.
1-19
Network Operating Systems
• Two clients and a server in a network operating system.
1-20
Network Operating Systems
• Different clients may mount the servers in different places.
1.21
Distributed Operating
Systems (DOS)
 The goal of such a system is to create the illusion that
the entire network of computers is a single timesharing
system.
 The characteristics of a distributed system include:
•
•
•
•
•
•
A single, global IPC mechanism
A global protection scheme
Homogeneous process management
A single set of system calls
A homogeneous file systems
Identical kernels on all CPUs
DOS
• A global file system
 Multiprocessor timesharing systems consist
of tightly-coupled software on tightly-coupled
hardware.
 The key character of this class of system is the
existence of a single run queue.
• Ready to run processes can be run on any of the
CPUs of the system.
 The operating system in this type of
organization normally maintains a traditional
file system.
DOS
• General structure of a multicomputer operating system
1.14
Middleware
• Middleware allows heterogeneous systems
to achieve the appearance of a single
coherent system
• An additional layer is added on top of the
NOS services
Middleware
• General structure of a distributed system as middleware.
1-22
Middleware and Openness
1.23
• In an open middleware-based distributed system, the protocols
used by each middleware layer should be the same, as well as
the interfaces they offer to applications.
Comparison between
Systems
Item
Distributed OS
Network
OS
Middlewarebased OS
Multiproc.
Multicomp.
Degree of transparency
Very High
High
Low
High
Same OS on all nodes
Yes
Yes
No
No
Number of copies of OS
1
N
N
N
Basis for
communication
Shared
memory
Messages
Files
Model specific
Resource management
Global,
central
Global,
distributed
Per node
Per node
Scalability
No
Moderately
Yes
Varies
Openness
Closed
Closed
Open
Open
Design Issues
 The single most important issue is the
achievement of transparency.
 There are several types of transparency that we
might try to achieve:
• Location transparency: users cannot tell where
resources are located.
• Migration transparency: resources can move at will
without changing names.
• Replication transparency: users cannot tell how
many copies exist.
Design Issues
• Concurrency transparency: multiple users can share
resources automatically.
• Parallelism transparency: activities can happen in
parallel without users knowing.
 The second key design issue is flexibility.
• Flexibility can be provided by a microkernel (as
opposed to a monolithic kernel) which provides just:
 An IPC mechanism.
 Some memory management.
 A small amount of low-level process management and
scheduling.
 Low-level input/output.
Design Issues
• All other OS services are implemented as user-level
services:




file system
directory system
full process management
system call handling
 This leads to a highly modular approach in
which new services are easily added without
stopping the system and booting a new kernel.
 Services can also be substituted with
customized services.
Design Issues
 In a uniprocessor system, the monolithic kernel
may have a performance advantage (no context
switches). However in a distributed system, the
advantage is less.
 Another design goal for distributed systems is
reliability.
• One aspect of reliability is availability, or uptime.
This can be improved by exploiting redundancy.
• Another aspect is security. This issue is even more
difficult in distributed systems then in uniprocessor
systems.
Design Issues
• A final issue of reliability is fault tolerance.
 Performance in distributed systems is a critical
issue due to the slow communication times.
• We can attempt to increase the performance by
selecting the correct grain size of computations.
• Jobs which involve fine-grained parallelism will
necessarily spend a large amount of time on
message passing. They are poor candidates for
distributed systems.
• Jobs that involve large computations and low
interaction - coarse-grained parallelism - are better
bets for distributed systems.
Design Issues
 Scalability is another critical issue.
 Scalability can be achieved by avoiding:
• Centralized components
• Centralized tables
• Centralized algorithms
 Decentralized algorithms have the following
characteristics:
• No machine has complete information about the
system state.
• Machines make decisions based only on local
information.
Design Issues
• Failure of one machine does not ruin the algorithm.
• There is no implicit assumption that a global clock
exists.
Scalability Problems
Concept
Example
Centralized services
A single server for all users
Centralized data
A single on-line telephone book
Centralized algorithms
Doing routing based on complete information
Examples of scalability limitations.
Scaling Techniques (1)
1.4
The difference between letting:
a) a server or
b) a client check forms as they are being filled
Scaling Techniques (2)
1.5
An example of dividing the DNS name space into zones.
New Distributed Systems
Architectures
•
•
•
•
•
•
Cluster Computing
Grid Computing
Cloud Computing
Mobile Ad Hoc Network (MANET)
Wireless Sensor Network
Peer-to-Peer Network