COMP 150-IDS: Internet Scale Distributed Systems (Spring 2015)
Models of Distributed Computing
Noah Mendelsohn Tufts University Email:
[email protected] Web: http://www.cs.tufts.edu/~noah
Architecting a universal Web
Identification: URIs Interaction: HTTP Data formats: HTML, JPEG, GIF, etc.
© 2010 Noah Mendelsohn
Goals Introduce basics of distributed system design Explore some traditional models of distributed computing Prepare for discussion of REST: the Web’s model
3
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Communicating systems
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Communicating systems
CPU Memory Storage
CPU Memory Storage
We have multiple programs, running asynchronously, sending messages Reference: http://www.usingcsp.com/cspbook.pdf (very theoretical) © 2010 Noah Mendelsohn
Communicating Sequential Processes We’ve got pretty clean higher level abstractions for use on a single machine
CPU Memory Storage
CPU Memory Storage
We have multiple programs, running asynchronously, sending messages Reference: http://www.usingcsp.com/cspbook.pdf (very theoretical) © 2010 Noah Mendelsohn
Communicating systems How can we get a clean model of two communicating machines?
CPU Memory Storage
CPU Memory Storage
We have multiple programs, running asynchronously, sending messages Reference: http://www.usingcsp.com/cspbook.pdf (very theoretical) © 2010 Noah Mendelsohn
Large scale systems
How can we get a clean model of a worldwide network of communicating machines?
Internet What are the clean abstractions on this scale? © 2010 Noah Mendelsohn
WARNING!! This is a very big topic… …many important approaches have been studied and used… …there is lots of operational experience, and also formalisms…
This presentation does not attempt to be either comprehensive or balanced…the goal is to introduce some key concepts
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Traditional Models of Distributed Computing Message Passing
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Message passing
CPU Memory Storage
CPU Memory Storage
Programs send messages to and from each others’ memories
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Half duplex: one way at a time
CPU Memory Storage
CPU Memory Storage
Programs send messages to and from each others’ memories
© 2010 Noah Mendelsohn
Full duplex: both ways at the same time
CPU Memory Storage
CPU Memory Storage
Programs send messages to and from each others’ memories
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Message passing Data abstraction: – Low level: bytes (octets) – Sometimes: agreed metaformat (XML, C struct, etc.)
Synchronization – Wait for message – Timeout
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Interaction Patterns
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Between pairs of machines
CPU Memory Storage
CPU Memory Storage
Request Response
Message passing: no constraints Common pattern: request/response
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Traditional Models of Distributed Computing Client Server
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Client / server
CPU Memory Storage
CPU Memory Storage
Request service Response
Request / response is a traffic pattern Client / server describes the roles of the nodes Server provides service for client
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Client / server Probably the most common dist. sys. architecture Simple – well understood Doesn’t explain: – How to exploit more than 2 machines – How to make programming easier – How to prove correctness: though the simple model helps
Most client/server systems are request/response
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Traditional Models of Distributed Computing N-Tier
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N-tier – also called Multilevel Client/Server
CPU Memory Storage
CPU Memory Storage
Request
CPU Memory Storage Request
Response
Response
Layered Each tier provides services for next higher level Reasons: – Information hiding – Management – Scalability
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Typical N-tier system: airline reservation
Reservation Records
iPhone or Android Reservation Application Flight Reservation Logic
Browser or Phone App
Application - logic
Application - logic
Many commercial applications work this way © 2010 Noah Mendelsohn
The Web itself is a 2 or 3 Tier system
Web Server
Browser Proxy Cache (optional!)
E.g. Firefox
E.g. Squid
E.g. Apache
Many commercial applications work this way © 2010 Noah Mendelsohn
Web Reservation System Reservation Records Web-Base Reservation Application Flight Reservation Logic
Proxy Cache (optional!)
HTTP
Browser or Phone App
HTTP
E.g. Squid
RPC? ODBC? Proprietary?
Application - logic
Application - logic
Many commercial applications work this way © 2010 Noah Mendelsohn
Web Publishing System Content Management System Web-Base Reservation Application Content Distribution Network
Browser or Phone App
E.g. Akamia
Content Web Site
E.g. cnn.com
Database or CMS
Many commercial applications work this way © 2010 Noah Mendelsohn
Advantages of n-tier system Separation of concerns – each layer has own role Parallism and performance? – If done right: multiple mid-tier servers work in parallel – Back end systems centralize mainly data requiring sharing & synchronization – Mid tier can provide shared, scalable caching
Information hiding – Mid-tier apps shielded from data layout
Security – Credit card numbers etc. not stored at mid-tier
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Other patterns Spanning tree Broadcast (send to many nodes at once) Flood Various P2P Etc.
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Traditional Models of Distributed Computing Remote Procedure Call
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Remote Procedure Call The term RPC was coined by the late Bruce Nelson in his 1981 CMU PhD thesis Key idea: an ordinary function call executes remotely The trick: the language runtime or helper code must automatically generate code to send parameters and results For languages like C: proxies and stubs are generated – Not needed in dynamic languages like Ruby, JavaScript, etc.
RPC is often (erroneously IMO) used to describe any request / response system
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RPC: Call remote functions automatically
x = sqrt(4) float sqrt(float n) { send n; read s; return s; } proxy
CPU Memory Storage Request
invoke sqrt(4)
float sqrt(float n) { …compute sqrt… return result; }
CPU Memory Storage
result=2 (no exception thrown) Response
void doMsg(Msg m) { s = sqrt(m.s); send s; } stub
Interface definition: float sqrt(float n); Proxies and stubs generated automatically RPC provides transparent remote invocation
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RPC: Pros and Cons Pros: – Transparency is very appealing – Simple programming model – Useful as organizing principle even when not fully automated
Cons – Getting language details right is tricky (e.g. exceptions) – No client/server overlap: doesn’t work well for long-running operations – May not optimize large transfers well – Not all APIs make sense to remote: e.g. answer = search(tree) – Versioning can be a problem: client and server need to agree exactly on interface (or have rules for dealing with differences)
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Traditional Models of Distributed Computing Distributed Object Systems
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How do you build an RPC for this? Class int int int }
Point { x,y getx() {return x;} gety() {return y;}
Class Rectangle { …members and constructs not shown… Point getUpperLeft() {…}; Point getLowerRight {…}; }
Call method on remoted object
int area (Rectangle r) { width=r.getLowerRight().getx() – r.getUpperLeft.getx(); width=r.getLowerRight().gety() – r.getUpperLeft.gety(); }
myRect = new Rectangle; …assume position set here.. int a = area(myRect); // REMOTE THIS CALL!
Pass object to remote method
Distributed Object systems make this work! © 2010 Noah Mendelsohn
Distributed object systems In the 1990s, seemed like a great idea Advantages of OO encapsulation & inheritance + RPC Examples – CORBA (Industry standard) – DCOM (Microsoft)
Still quite widely used within enterprises Complicated – – – – –
Marshalling object references Distributed object lifetime management Brokering: which object provides the service today Remote “new”: creating objects on remote systems All the pros & cons of RPC, plus the above
Generally not appropriate at Internet scale
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Traditional Models of Distributed Computing Some Other Options
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Special Purpose Models Remote File System – Network provides transparent access to remote files – Examples: NFS, CIFS
Remote Database – Examples: ODBJ, JDBC
Remote Device – Remote printing, disk drive etc.
Virtual terminal – One computer simulates an interactive terminal to another
© 2010 Noah Mendelsohn
Some other interesting models Broadcast / multicast – Send messages to everyone (broadcast) / named group (multicast)
Publish / subscribe (pub/sub) – Subscribe to named events or based on query filter – Call me whenever Pepsi’s stock price changes – Implements a distributed associative memory
Reliable queuing – – – –
Examples: IBM MQSeries, Java Message Service (JMS) Model: queued messages, preserved across hardware crashes Widely used for bank machine transactions; long-running (multi-day) eCommerce transactions; Depends on disk-based transaction systems at each node to keep queues
Tuple spaces – Pioneered by Gelernter at Yale (Linda kernel), picked up by Jini (Sun), and TSpaces (IBM) – Network-scale shared variable space, with synchronization – Good for queues of work to do: some cloud architectures use a related model to distribute work to servers
© 2010 Noah Mendelsohn
Stateful and Stateless Protocols
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Stateful and Stateless Protocols Stateful: server knows which step (state) has been reached Stateless: – Client remembers the state, sends to server each time – Server processes each request independently
Can vary with level – Many systems like Web run stateless protocols (e.g. HTTP) over streams…at the packet level, TCP streams are stateful – HTTP itself is mostly stateless, but many HTTP requests (typically POSTs) update persistent state at the server
© 2010 Noah Mendelsohn
Advantages of stateless protocols Protocol usually simpler Server processes each request independently Load balancing and restart easier Typically easier to scale and make fault-tolerant Visibility: individual requests more self-describing
© 2010 Noah Mendelsohn
Advantages of stateful protocols Individual messages carry less data Server does not have to re-establish context each time There’s usually some changing state at the server at some level, except for completely static publishing systems
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Text vs. Binary Protocols
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Protocols can be text or binary on the wire Text: messages are encoded characters Binary: any bit patterns Pros and cons quite similar to those for text vs. binary file formats When sending between compatible machines, binary can be much faster because no conversion needed Most Internet-scale application protocols (HTTP, SMTP) use text for protocol elements and for all content except photo/audio/video HTTP 2.0 moving to binary (for msg size and parsing speed)
© 2010 Noah Mendelsohn
Summary
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Summary The machine-level model is complex: multiple CPUs, memories A number of abstractions are widely used for limited-scale distribution RPC is among the most interesting and successful Statefulness / statelessness is a key design tradeoff We’ll see next time why a new model was needed for the Web
© 2010 Noah Mendelsohn
