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Reliable Distributed Systems. RPC and Client-Server Computing. Remote Procedure Call. Basic concepts Implementation issues, usual optimizations Where are the costs? Reliability and consistency Multithreading debate. A brief history of RPC. Introduced by Birrell and Nelson in 1985 - PowerPoint PPT Presentation
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Reliable Distributed Systems
RPC and Client-Server Computing
Remote Procedure Call
Basic concepts Implementation issues, usual
optimizations Where are the costs? Reliability and consistency Multithreading debate
A brief history of RPC Introduced by Birrell and Nelson in 1985 Pre-RPC: Most applications were built
directly over the Internet primitives Their idea: mask distributed computing
system using a “transparent” abstraction Looks like normal procedure call Hides all aspects of distributed interaction Supports an easy programming model
Today, RPC is the core of many distributed systems
More history Early focus was on RPC “environments” Culminated in DCE (Distributed Computing
Environment), standardizes many aspects of RPC
Then emphasis shifted to performance, many systems improved by a factor of 10 to 20
Today, RPC often used from object-oriented systems employing CORBA or COM standards. Reliability issues are more evident than in the past.
The basic RPC protocol
client server“binds” to
serverregisters with name service
The basic RPC protocol
client server“binds” to
server
prepares, sends request
registers with name service
receives request
The basic RPC protocol
client server“binds” to
server
prepares, sends request
registers with name service
receives requestinvokes handler
The basic RPC protocol
client server“binds” to
server
prepares, sends request
registers with name service
receives requestinvokes handlersends reply
The basic RPC protocol
client server“binds” to
server
prepares, sends request
unpacks reply
registers with name service
receives requestinvokes handlersends reply
Compilation stage Server defines and “exports” a header file
giving interfaces it supports and arguments expected. Uses “interface definition language” (IDL)
Client includes this information Client invokes server procedures through
“stubs” provides interface identical to the server version responsible for building the messages and
interpreting the reply messages passes arguments by value, never by reference may limit total size of arguments, in bytes
Binding stage Occurs when client and server program
first start execution Server registers its network address
with name directory, perhaps with other information
Client scans directory to find appropriate server
Depending on how RPC protocol is implemented, may make a “connection” to the server, but this is not mandatory
Data in messages We say that data is “marshalled” into a
message and “demarshalled” from it Representation needs to deal with byte
ordering issues (big-endian versus little endian), strings (some CPUs require padding), alignment, etc
Goal is to be as fast as possible on the most common architectures, yet must also be very general
Request marshalling Client builds a message containing arguments,
indicates what procedure to invoke Due to the need for generality, data
representation a potentially costly issue! Performs a send I/O operation to send the
message Performs a receive I/O operation to accept the
reply Unpacks the reply from the reply message Returns result to the client program
Costs in basic protocol? Allocation and marshalling data into
message (can reduce costs if you are certain client, server have identical data representations)
Two system calls, one to send, one to receive, hence context switching
Much copying all through the O/S: application to UDP, UDP to IP, IP to ethernet interface, and back up to application
Schroeder and Burroughs Studied RPC performance in O/S
kernel Suggested a series of major
optimizations Resulted in performance
improvments of about 10-fold for Xerox firefly workstation (from 10ms to below 1ms)
Typical optimizations? Compile the stub “inline” to put arguments
directly into message Two versions of stub; if (at bind time) sender
and dest. found to have same data representations, use host-specific rep.
Use a special “send, then receive” system call (requires O/S extension)
Optimize the O/S kernel path itself to eliminate copying – treat RPC as the most important task the kernel will do
Fancy argument passing RPC is transparent for simple calls with a small
amount of data passed “Transparent” in the sense that the interface to the
procedure is unchanged But exceptions thrown will include new exceptions
associated with network What about complex structures, pointers, big
arrays? These will be very costly, and perhaps impractical to pass as arguments
Most implementations limit size, types of RPC arguments. Very general systems less limited but much more costly.
Overcoming lost packets
client serversends request
Overcoming lost packets
client serversends request
retransmit
ack for request duplicate request: ignored
Timeout!
Overcoming lost packets
client serversends request
retransmit
ack for request
reply
Timeout!
Overcoming lost packets
client serversends request
retransmit
ack for request
reply
ack for reply
Timeout!
Costs in fault-tolerant version? Acks are expensive. Try and avoid
them, e.g. if the reply will be sent quickly supress the initial ack
Retransmission is costly. Try and tune the delay to be “optimal”
For big messages, send packets in bursts and ack a burst at a time, not one by one
Big packets
client serversends request as a burst
ack entire burst
reply
ack for reply
RPC “semantics” At most once: request is processed 0 or
1 times Exactly once: request is always
processed 1 time At least once: request processed 1 or
more times... but exactly once is impossible because
we can’t distinguish packet loss from true failures! In both cases, RPC protocol simply times out.
Implementing at most/least once Use a timer (clock) value and a unique id, plus
sender address Server remembers recent id’s and replies with
same data if a request is repeated Also uses id to identify duplicates and reject
them Very old requests detected and ignored by
checking time Assumes that the clocks are working In particular, requires “synchronized” clocks
RPC versus local procedure call Restrictions on argument sizes and
types New error cases:
Bind operation failed Request timed out Argument “too large” can occur if, e.g., a
table grows Costs may be very high ... so RPC is actually not very
transparent!
RPC costs in case of local destination process
Often, the destination is right on the caller’s machine!
Caller builds message Issues send system call, blocks, context switch Message copied into kernel, then out to dest. Dest is blocked... wake it up, context switch Dest computes result Entire sequence repeated in reverse direction If scheduler is a process, context switch 6 times!
RPC example
Source does
xyz(a, b, c)
Dest on same site
O/S
RPC in normal case
Source does
xyz(a, b, c)
Dest on same site
O/S
Destination and O/S are blocked
RPC in normal case
Source does
xyz(a, b, c)
Dest on same site
O/S
Source, dest both block. O/S runs its scheduler, copies message from source out-
queue to dest in-queue
RPC in normal case
Source does
xyz(a, b, c)
Dest on same site
O/S
Dest runs, copies in message
Same sequence needed to return results
Broad comments on RPC RPC is not very transparent Failure handling is not evident at all: if an RPC
times out, what should the developer do? Reissuing the request only makes sense if there is
another server available Anyhow, what if the request was finished but the
reply was lost? Do it twice? Try to duplicate the lost reply?
Performance work is producing enormous gains: from the old 75ms RPC to RPC over U/Net with a 75usec round-trip time: a factor of 1000!
Contents of an RPC environment
Standards for data representation Stub compilers, IDL databases Services to manage server directory,
clock synchronization Tools for visualizing system state
and managing servers and applications
Closely Related Topic Multithreading is a common
performance-enhancing technique Idea is that server is often idle while
doing I/O for one client, so use extra threads to allow concurrent request processing
In the limit, leads to database transactional concurrency model, but many non-transactional servers use threads for enhanced performance
Multithreading debate Three major options:
Single-threaded server: only does one thing at a time, uses send/recv system calls and blocks while waiting
Multi-threaded server: internally concurrent, each request spawns a new thread to handle it
Upcalls: event dispatch loop does a procedure call for each incoming event, like for X11 or PC’s running Windows.
Single threading: drawbacks Applications can deadlock if a request cycle
forms: I’m waiting for you and you send me a request, which I can’t handle
Much of system may be idle waiting for replies to pending requests
Harder to implement RPC protocol itself (need to use a timer interrupt to trigger acks, retransmission, which is awkward)
Multithreading Idea is to support internal concurrency as
if each process was really multiple processes that share one address space
Thread scheduler uses timer interrupts and context switching to mimic a physical multiprocessor using the smaller number of CPU’s actually available
Multithreaded RPC Each incoming request is handled by
spawning a new thread Designer must implement appropriate
mutual exclusion to guard against “race conditions” and other concurrency problems
Ideally, server is more active because it can process new requests while waiting for its own RPC’s to complete on other pending requests
Negatives to multithreading Users may have little experience with
concurrency and will then make mistakes Concurrency bugs are very hard to find due to
non-reproducible scheduling orders Reentrancy can come as an undesired surprise Threads need stacks hence consumption of
memory can be very high Deadlock remains a risk, now associated with
concurrency control Stacks for threads must be finite and can
overflow, corrupting the address space
Threads: can spawn too many
SCHED
event
Threads: can spawn too many
SCHED
event
Thread spawned, but blocks
Threads: can spawn too many
SCHED
eventEventually, application becomes bloated, begins to thrash. Performance drops and clients may think the server has failed
Upcall model Common in windowing systems Each incoming “event” is encoded as a
small descriptive data structure User registers event handling
procedures Dispatch loop calls the procedures as
new events arrive, waits for the call to finish, then dispatches a new event
Upcalls combined with threads
Perhaps the best model for RPC programming
Each handler can be tagged: needs thread, or can be executed “unthreaded”
Developer must still be very careful where threads are used
Recent RPC history RPC was once touted as the transparent
answer to distributed computing Today the protocol is very widely used ... but it isn’t very transparent, and
reliability issues can be a major problem Today the strongest interest is in Web
Services and CORBA, which use RPC as the mechanism to implement object invocation