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Distributed Systems

CSE 380

Lecture Note 13

Insup Lee

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University of PennsylvaniaIntroduction to Distributed Systems

Why do we develop distributed systems?

• availability of powerful yet cheap microprocessors (PCs, workstations), continuing advances in communication technology,

What is a distributed system?

A distributed system is a collection of independent computers that appear to the users of the system as a single system.

Examples:

• Network of workstations

• Distributed manufacturing system (e.g., automated assembly line)

• Network of branch office computers

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University of PennsylvaniaAdvantages of Distributed Systems over Centralized Systems

• Economics: a collection of microprocessors offer a better price/performance than mainframes. Low price/performance ratio: cost effective way to increase computing power.

• Speed: a distributed system may have more total computing power than a mainframe. Ex. 10,000 CPU chips, each running at 50 MIPS. Not possible to build 500,000 MIPS single processor since it would require 0.002 nsec instruction cycle. Enhanced performance through load distributing.

• Inherent distribution: Some applications are inherently distributed. Ex. a supermarket chain.

• Reliability: If one machine crashes, the system as a whole can still survive. Higher availability and improved reliability.

• Incremental growth: Computing power can be added in small increments. Modular expandability

• Another deriving force: the existence of large number of personal computers, the need for people to collaborate and share information.

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University of PennsylvaniaAdvantages of Distributed Systems over Independent PCs

• Data sharing: allow many users to access to a common data base

• Resource Sharing: expensive peripherals like color printers

• Communication: enhance human-to-human communication, e.g., email, chat

• Flexibility: spread the workload over the available machines

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University of PennsylvaniaDisadvantages of Distributed Systems

• Software: difficult to develop software for distributed systems

• Network: saturation, lossy transmissions

• Security: easy access also applies to secrete data

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Hardware Concepts

Taxonomy (Fig. 9-4)

MIMD (Multiple-Instruction Multiple-Data)

Tightly Coupled versus Loosely Coupled Tightly coupled systems (multiprocessors)

o shared memory

o intermachine delay short, data rate high

Loosely coupled systems (multicomputers)

o private memory

o intermachine delay long, data rate low

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Bus versus Switched MIMD

• Bus: a single network, backplane, bus, cable or other medium that connects all machines. E.g., cable TV

• Switched: individual wires from machine to machine, with many different wiring patterns in use.

Multiprocessors (shared memory)

– Bus

– Switched

Multicomputers (private memory)

– Bus

– Switched

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Switched Multiprocessors

Switched Multiprocessors (Fig. 9-6) for connecting large number (say over 64) of

processors crossbar switch: n**2 switch points omega network: 2x2 switches for n CPUs and n

memories, log n switching stages, each with n/2 switches,

total (n log n)/2 switches delay problem: E.g., n=1024, 10 switching stages from

CPU to memory. a total of 20 switching stages. 100 MIPS 10 nsec instruction execution time need 0.5 nsec switching time

NUMA (Non-Uniform Memory Access): placement of program and data

building a large, tightly-coupled, shared memory multiprocessor is possible, but is difficult and expensive

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Multicomputers

Bus-Based Multicomputers (Fig. 9-7) easy to build communication volume much smaller relatively slow speed LAN (10-100 MIPS, compared to

300 MIPS and up for a backplane bus)

Switched Multicomputers (Fig. 9-8) interconnection networks: E.g., grid, hypercube hypercube: n-dimensional cube

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Software Concepts

• Software more important for users

• Three types:

1. Network Operating Systems

2. (True) Distributed Systems

3. Multiprocessor Time Sharing

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Network Operating Systems

loosely-coupled software on loosely-coupled hardware

A network of workstations connected by LAN each machine has a high degree of autonomy

o rlogin machineo rcp machine1:file1 machine2:file2

Files servers: client and server model Clients mount directories on file servers Best known network OS:

o Sun’s NFS (network file servers) for shared file systems (Fig. 9-11)

a few system-wide requirements: format and meaning of all the messages exchanged

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NFS

NFS Architecture

• Server exports directories

• Clients mount exported directories

NSF Protocols

• For handling mounting

• For read/write: no open/close, stateless

NSF Implementation

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(True) Distributed Systems

tightly-coupled software on loosely-coupled hardware

provide a single-system image or a virtual uniprocessor

a single, global interprocess communication mechanism, process management, file system; the same system call interface everywhere

Ideal definition:

“ A distributed system runs on a collection of computers that do not have shared memory, yet looks like a single computer to its users.”

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University of PennsylvaniaMultiprocessor Operating Systems

(Fig. 9-12)

Tightly-coupled software on tightly-coupled hardware

Examples: high-performance servers

shared memory

single run queue

traditional file system as on a single-processor system: central block cache

Fig. 9-13 for comparisons

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Design Issues of Distributed Systems

• Transparency

• Flexibility

• Reliability

• Performance

• Scalability

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1. Transparency

• How to achieve the single-system image, i.e., how to make a collection of computers appear as a single computer.

• Hiding all the distribution from the users as well as the application programs can be achieved at two levels:

1) hide the distribution from users

2) at a lower level, make the system look transparent to programs.

1) and 2) requires uniform interfaces such as access to files, communication.

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Types of transparency

– Location Transparency: users cannot tell where hardware and software resources such as CPUs, printers, files, data bases are located.

– Migration Transparency: resources must be free to move from one location to another without their names changed.E.g., /usr/lee, /central/usr/lee

– Replication Transparency: OS can make additional copies of files and resources without users noticing.

– Concurrency Transparency: The users are not aware of the existence of other users. Need to allow multiple users to concurrently access the same resource. Lock and unlock for mutual exclusion.

– Parallelism Transparency: Automatic use of parallelism without having to program explicitly. The holy grail for distributed and parallel system designers.

Users do not always want complete transparency: a fancy printer 1000 miles away

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2. Flexibility

• Make it easier to change

• Monolithic Kernel: systems calls are trapped and executed by the kernel. All system calls are served by the kernel, e.g., UNIX.

• Microkernel: provides minimal services. (Fig 9-15)1) IPC 2) some memory management 3) some low-level process management and scheduling 4) low-level i/oE.g., Mach can support multiple file systems, multiple system interfaces.

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3. Reliability

• Distributed system should be more reliable than single system. Example: 3 machines with .95 probability of being up. 1-.05**3 probability of being up.– Availability: fraction of time the system is usable.

Redundancy improves it.– Need to maintain consistency– Need to be secure– Fault tolerance: need to mask failures, recover from

errors.

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4. Performance

• Without gain on this, why bother with distributed systems.

• Performance loss due to communication delays:– fine-grain parallelism: high degree of interaction– coarse-grain parallelism

• Performance loss due to making the system fault tolerant.

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5. Scalability

• Systems grow with time or become obsolete. Techniques that require resources linearly in terms of the size of the system are not scalable. e.g., broadcast based query won't work for large distributed systems.

• Examples of bottleneckso Centralized components: a single mail server

o Centralized tables: a single URL address book

o Centralized algorithms: routing based on complete information