Intelligent Information Systems
6. Software Composition
Gio Wiederhold
EPFL,
Research performed jointly with Dorothea Beringer
April-June 2000, at 14:15 - 15:15, room INJ 218
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Schedule
Presentations in English -- but I'll try to manage discussions in French and/or German.
• I plan to cover the material in an integrating fashion, drawing from concepts in
databases, artificial intelligence, software engineering, and business principles.
1. 13/4 Historical background, enabling technology:ARPA, Internet, DB, OO, AI., IR
2. 27/4 Search engines and methods (recall, precision, overload, semantic problems).
3. 4/5 Digital libraries, information resources. Value of services, copyright.
4. 11/5 E-commerce. Client-servers. Portals. Payment mechanisms, dynamic pricing.
5. 19/5 Mediated systems. Functions, interfaces, and standards. Intelligence in
processing. Role of humans and automation, maintenance.
6. 26/5 Software composition. Distribution of functions. Parallelism. [ww D.Beringer]
7. 31/5 Application to Bioinformatics.
8. 15/6 Educational challenges. Expected changes in teaching and learning.
9. 22/6 Privacy protection and security. Security mediation.
10.29/6 Summary and projection for the future.
• Feedback and comments are appreciated.
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Software Composition
CHAIMS:
Compiling High-level Access
Interfaces for Multi-site Software
CHAIMS
Gio Wiederhold and Dorothea Beringer
Stanford University
www-db.stanford.edu/CHAIMS
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Introduction
CHAIMS
Objective: Investigate revolutionary approaches to
large-scale software composition.
Approach: Develop & validate a
composition-only language.
Contributions and plans:
• Hardware and software platform independence.
• Asynchrony by splitting up CALL-statement.
• Performance optimization by invocation scheduling.
• Potential for multi-site dataflow optimization.
Apply technologies from Distributed Database paradigm
to Software Distribution
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Participants
• Support
–
–
–
–
–
DARPA ISO EDCS program (1996-1999)
Siemens Corporate Research (1996-1998)
DoD AFOSR AASERT student support (1997-1999)
Sloan Foundation - computer industry study (1996-97)
CIA fellowship (2000-2002)
• People
Gio Wiederhold (Prof. Res) PI - Marianne Siroker (Administration)
Dorothea Beringer (Postdoc, Res. Ass.; PhD EPF Lausanne) Dec.1997-Dec.1999
Neil Sample (CS PhD Student) -- Laurence Melloul (CS MS, PhD student)
Prasenjit Mitra (EE MS, now EE PhD student SKC project)
graduated: Ron Burback (PhD.); Joshua Hui, Gaurav Bhatia, Kirti Kwatra,
Prasanna Ramaswami, Pankaj Jain, Mehul Bastawala, Catherine Tornabene (MS
CS); Wayne Lim (MS, I.E.), Connan King (E.E. BS), Woody Pollack (CS MS).
– Louis Perrochon (postdoc ETH Zurich) Fall quarter 1996
–
–
–
–
–
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Observed Shift in Programming Tasks
Integration
Coding
1970
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1990
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2010?
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Hypotheses
• After the Y2K effort no large software applications
will be written from the ground up. They will always
be composed using existing legacy code.
• Composition requires functionalities not available
in current mainstream programming languages.
• Large-scale distributed systems enable & require
optimizations that differ from code optimizers.
• Composition programmers will use different tools
from base programmers. (type A versus type B -- [Belady]
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Languages & Interfaces
• Large languages intended to support coding as
well as composition have not been adopted
–
–
–
–
Algol 68
PL/1
Ada
CLOS
in use:
C, C++, Fortran, Java
• Databases are being successfully composed,
using Client-server, Mediator architectures
–
–
–
–
distribution -- exploit network capabilities
heterogeneity -- autonomy creates heterogneity
simple schemas -- some human interpretation
service model -- public and commercial sources
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Typical Scenario: Logistics
A general has to ship troops and/or various material
from San Diego NOSC to Washington DC:
–
–
–
–
–
different kind of material: criteria for preferred transport differ
not every airport equally suited
congestion, prices
actual weather
certain due or ready dates
Today:
calling different companies, looking up
information on the web, reservations by hand
Tomorrow: system proposes possibilities that take
into account various conditions
• hand-coded systems
• composition of processes
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Scaling alternatives
?
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CHAIMS
(:
Client program for composition,
written by domain specialist
Compiler & templates automate
generation of client code for
distributed system
CHAIMS
Services
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Transport protocols
access services
Services & Interfaces
megamodules provided
by various suppliers
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Megamodules - Definition
Megamodules are large, autonomous, distributed,
heterogeneous services or processes.
• large: computation intensive, data intensive, ongoing processes
(example: monitoring services)
• distributed: to be used by more than one client
• heterogeneous: accessible by various distribution protocols
(not only different languages and systems)
• autonomous: maintenance and control over recourses remains with
provider, differing ontologies ( ==> SKC project)
Examples:
– logistics: “find best transportation route from A to B”, reservation systems
– genomics: easier framework for composing various processing tools than
ad-hoc coding
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Mega-programming Process
megaprogrammer
Mega-program
Text
Module / platform
descriptions
Feedback
CHAIMS
compiler
customer
Native Module
Wrapper / API
LegacyWrapper
Module / API
to be composed
Modules
to be/ API
Wrapper
composed
Legacy Module
to be composed
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Megaprogram
IO
module
Transport
mechanisms
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Integrated systems: Fat Clients
Domain expert
Control &
Computation
Services
I/O
Client computer
a
b
c
d
e
I/O
Wrappers
to resolve
differences
Data
Resources
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Desired: Modest Clients
Domain expert
IO module
IO module
Client workstation
C
Computation
Services
MEGA modules
a
Sites
e
b
R
T
S
c
U d
T
Data
Resources
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Valuable Services
Services are not free for a client:
– execution time of a service
– transfer time for data
– fees for services
What the client applications need:
 monitoring progress of a service
 possibility to choose among equivalent services
based on estimated waiting time and fees
 high performance due to parallelism among services
 preliminary overview results, choosing level of
accuracy / number of results for complex processes
 effective optimization techniques
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Empower Non-technical
Domain Experts
Company providing services:
– domain experts of domain of service (e.g. weather)
– technical experts for programming for distribution
protocols, setting up servers in a middleware system
– marketing experts
“Composer of Megaprograms”:
– is domain expert of domain that uses these services
– is not technical expert of middleware system or
experienced programmer,
– wants to focus on problem at hand (=results of using
megaprogram)
e.g., scientist, logistics officer, . . .
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A purely compositional language?
Which languages did succeed?
– Algol, ADA: integrated composition and computation
– C, C++ focus on computation
Why a new language?
– complexity: not all facilities of a common language
(compare to approach of Java),
– inhibiting traditional computational programming (compare
C++ and Smalltalk concerning object-oriented programming)
– focus on issue of composition, parallelism by natural
asynchrony, and novel optimizations
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CHAIMS Physical Architecture
complex calls
Megaprogram
Clients in CHAIMS
Network
CORBA, JAVA RMI,
DCE, DCOM...
Megamodules (wrapped, native) each supporting
setup, estimate, invoke, examine, extract, and terminate.
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Decomposing CALL statements
CALL gained
functionality
progress
in
scale of
computing
Copying
Code sharing
Parameterized computation
CHAIMS
decomposes
CALL
functions
Objects with overloaded method names
Remote procedure calls to distributed modules
Constrained (black box) access to encapsulated data
Setup
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Estimate
Invoke
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Examine
Extract
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CHAIMS Megaprogr. Language
Purely compositional:
– only variety of CALLs and control flow
– no primitives for input/output ==> instead use general and
problem-specific I/O megamodules
– no primitives for arithmetic ==> use math megamodules
Splitting up CALL-statement:
– parallelism by asynchrony in sequential program
– novel possibilities for optimizations
– reduction of complexity of integrated invoke statements
• Very high-level language
just as: assembler  HLLs,
HLLs  composition/megamodule paradigm
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CHAIMS Primitives
Pre-invocation:
SETUP: set up the connection to a megamodule
SET-, GETATTRIBUTES: set global parameters in a megamodule
ESTIMATE: get estimate of execution time for optimization
Invocation and result gathering:
INVOKE: start a specific method
EXAMINE: test status of an invoked method
EXTRACT: extract results from an invoked method
Termination:
TERMINATE: terminate a method invocation or a connection to
a megamodule
Control:
WHILE, IF
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Utility:
GETPARAM: get default parameters
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Megaprogram Example:
General I/O-megamodule
InputOutput
- Input
- Output
• Input function takes as parameter a
default data structure containing names,
types and default values for expected
input
Travel information:
RouteInfo
AirGround
- AllRoutes
- CityPairList
- ...
- CostForGround
- CostForAir
- ...
• Computing all possible routes between
two cities
• Computing the air and ground cost for
each leg given a list of city-pairs and data
about the goods to be transported
Two megamodules that offer equivalent
functions for calculating optimal routes
Routing
RouteOptimizer
- BestRoute
- ...
- Optimum
- ...
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• Optimum and BestRoute both calculate
the optimum route given routes and costs
• Global variables: Optimization can be
done for cost or for time
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Megaprogram Example: Code
io_mmh = SETUP ("InputOutput")
route_mmh = SETUP ("RouteInfo")
...
best2_mmh.SETATTRIBUTES (criterion = "cost")
// Setup connections to megamodules.
// Set global variables valid for all invocations
// of this client.
// Get information from the megaprogram user
cities_default = route_mmh.GETPARAM(Pair_of_Cities)
input_cities_ih = io_mmh.INVOKE ("input”, cities_default) // about the goods to be transported and about
WHILE (input_cities_ih.EXAMINE() != DONE) {}
// the two desired cities.
cities = input_cities_ih.EXTRACT()
// Get all routes between the two cities.
...
route_ih = route_mmh.INVOKE ("AllRoutes", Pair_of_Cities = cities)
WHILE (route_ih.EXAMINE() != DONE) {}
//Get all city pairs in these routes.
routes = route_ih.EXTRACT()
//Calculate the costs of all the routes.
…
// Figure out the optimal megamodule for
// picking the best route.
IF (best1_mmh.ESTIMATE("Best_Route")
< best2_mmh.ESTIMATE("Optimum") )
THEN {best_ih = best1_mmh.INVOKE ("Best_Route", Goods = info_goods,
Pair_of_Cities = cities, List_of_Routes = routes,
// Pick the best route and
Cost_Ground = cost_list_ground, Cost_Air = cost_list_air)}
/ /display the result.
ELSE {best_ih = best2_mmh.INVOKE ("Optimum", Goods = info_goods,
…
...
// Terminate all invocations
best2_mmh.TERMINATE()
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Operation of one Megamodule
• SETUP
M handle
• SETATTRIBUTES
provides context
M handle
M handle
• ESTIMATE serves
scheduling
M handle
• INVOKE initiates remote
computation
I handle
• EXAMINE checks for
completion
I handle
• EXTRACT obtains results
I handle
• TERMINATE I / ALL
I handle
M handle
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Creation Process
Megamodule
Provider
provides native or
wraps legacy megamodules
Repository
Browser
Wrapper
Templates
adds
information to
CHAIMS
Repository
b
d
a
e
c
MEGA Modules
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Composition Process
Composer
reads
information
Composition
Wizard
Repository
Browser
writes
Composition
Wizard
Megaprogram
starts
CHAIMS
Compiler
CHAIMS
Repository
generates
CSRT
(compiled megaprogram)
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Runtime Architecture
Client
End-user
b
CSRT
(compiled megaprogram)
a
d e
c
MEGA modules
IO module(s)
Distribution System (CORBA, RMI…)
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Architecture: Overview
Composer
writes
Megaprogram
Megamodule
Provider
adds
information
to
Wrapper
Templates
CHAIMS
Repository
Client
End-user
CHAIMS
Compiler
b
generates
CSRT
a
d e
c
MEGA modules
IO module(s)
Distribution System
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Multiple Transport Protocols
The CHAIMS API defines
interface between the composer
and the client megaprogram; the
megaprogram is written in the
Composer
CHAIMS - language
CHAIMS language.
The CHAIMS protocols
define the calls the megamodules have to understand.
These protocols are slightly
different for the different
distribution protocols, and
are defined by an idl for
CORBA, another idl for DCE,
and a Java class for RMI.
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Megaprogram
CHAIMS-protocols
CORBA-idl
DCE-idl
Java-class
M e g a m o d u l e s
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Data objects: Blobs
Minimal Typing within CHAIMS:
Integer, boolean only for control
All else is placed into Binary Large OBjects (Blobs),
transparent to compiler :
Alternatives
• ASN.1, with conversion routines (now)
• XML, with interpretation
(next)
Example: Person_Information
Name of Person complex
First Name
string
Personal Data complex
Joe
Date of Birth
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Last Name
date
6/21/54
string
Smith
Soc.Sec.No
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Address
string
345-34-345
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Wrapper: CHAIMS Compliance
CHAIMS protocol - support all CHAIMS primitives
– if not native, achieved by wrapping legacy codes
• State management and asynchrony:
• clientId (megamodule handle in CHAIMS language)
• callId (invocation handle in CHAIMS language)
• results must be stored for possible extraction(s) until
termination of the invocation
• Data transformation:
• all parameters of type blob (BER-encoded Gentype)
must be converted into the megamodule specific
data types (coding/decoding routines)
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Architecture: Three Views
Composition View
(megaprogram)
CHAIMS Layer
- composition of
megamodules
- directing of opaque
data blobs
Transport View
Distribution Layer
Objective:
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moving around data blobs
and CHAIMS messages
Clear separation between composition of services,
computation over data, and transport
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time
time
time
Scheduler: Decomposed Execution
s,i
s,i
e
e
synchronous
asynchronous
s
i
e
decomposed
(no benefit for one module)
execution of a remote method
available for other methods
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s
i
e
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setup / set attributes
invoke a method
extract results
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Optimized Execution of Modules
i2
e1
M1
time
M2
i3
e2
time
i1
M3
i4
e3
e4
i5
e5
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M1
M3
M4
(>M1+M2)
(<M1+M2)
M2
M5
optimized by scheduler
M4
according to estimates
data dependencies
execution of a module
M5
non-optimized
i3
i1 i4
e1
i2
e4
e3
e2
i5
e5
i
e
invoke a method
extract results
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Decomposed Parallel Execution
M1
optimized by scheduler
according to estimates
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M4
M3
<M1+M2)
M2
(<M1+M2)
time
Long setup times
occur, for instance,
when a subset of a
large database has
to be loaded for a
simple search, say
Transatlantic fights
for an optimal arrival.
M5
set up / set attributes
invoke a method
extract results
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Scheduling: Simple Example
1
cost_ground_ih = cost_mmh.INVOKE ("Cost_for_Ground",
List_of_City_Pairs = city_pairs,Goods = info_goods)
1
2
WHILE (cost_ground_ih.EXAMINE() != DONE) {}
cost_list_ground = cost_ground_ih.EXTRACT()
3
3
cost_air_ih = cost_mmh.INVOKE ("Cost_for_Air",
List_of_City_Pairs = city_pairs,Goods = info_good)
2
4
WHILE (cost_air_ih.EXAMINE() != DONE) {}
cost_list_air = cost_air_ih.EXTRACT()
4
order in
automatically
prescheduled
megaprogram
order in
unscheduled
megaprogram
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M2
M3
(>M1+M2)
M3
M4
M4
(<M1+M2)
(<M1+M2)
(>M1+M2)
M2
prior time
M1
M1
time
Decomposed Optimized Execution
M5
M5
optimized by scheduler
according to estimates
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set up / set attributes
invoke a method
extract results
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Iterated Invocations
M6.1
M6.3
M6.2
M6.3
time
M6.2
prior time
M6.1
Avoid
repeated
setups
M6.4
M6.5
M6.4
set up / set attributes
invoke a method
extract results
M6.5
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M6.1
M6.1
M6.5
M6.4
time, shared setup
M6.4
M6.3
prior time, disibct invoctions
M6.2
M6.2
M6.3
M6.1
M6.2
M6.3
M6.4
M6.5
M6.5
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time,
shared setup &
partial extract
& Repeated Extractions
Avoid
large
exacts
until
satisfied
set up / set attributes
invoke a method
extract results
partial for iterating
full for presentation
40
Scheduling: Heuristics
INVOKES: call INVOKE’s as soon as possible
• may depend on other data
• moving it outside of an if-block: depending on cost-function
(ESTIMATE of this and following functions concerning execution time,
dataflow and fees (resources).
EXTRACT: move EXTRACT’s to where the result is actually needed
• no sense of checking/waiting for results before they are needed
• instead of waiting, polling all invocations and issue next possible
invocation as soon as data could be extracted
TERMINATE: terminate invocations that are no longer needed
(save resources)
• not every method invocation has an extract (e.g. print-like functions)
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Compiling into a Network?
current CHAIMS system
Mega Program
Program
Mega
Module B
Module F
Module D
Module A
Module C
Module E
with distribution
dataflow optimization
Mega Program
Module B
Module F
Module D
Module A
Module C
Module E
control flow
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data flow
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CHAIMS Implementation
• Specify minimal language
– minimal functions: CALLs, While, If *
– minimal typing {boolean, integer, string, handles, object}
• objects encapsulated using ASN.1 standard
– type conversion in wrappers, service modules*
•
•
•
•
•
•
•
•
Compiler for multiple protocols (one-at-time, mixed 2, all*)
Wrapper generation for multiple protocols
Native modules for I/O, simple mathematics, other
Implement API for CORBA, Java RMI, DCE, DCOM * usage
Wrap / construct several programs for simple demos
Schedule optimization *
Demonstrate use in heterogeneous setting
Full-scale demonstration
* in process
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Research Questions
CHAIMS
• Is a Megaprogramming language focusing only
on composition feasible?
• Can it exploit on-going progress in client-server
models and be protocol independent?
• Can natural parallelism for distributed services
be effectively scheduled?
• Can high-level dataflow among distributed
modules be optimized?
• Can CHAIMS express clearly a high-level
distributed SW architecture?
• Can the approach affect SW process concepts
and practice?
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Questions not addressed
• Will one Client/Server protocol subsume all others?
– distributed optimization remains an issue
• Synchronization / Concurrency Control
– autonomy of sources negates current concepts
– if modules share databases, then database locks may span
setup/terminate all for a megaprogram handle.
• Will software vendors consider moving to a service
paradigm?
– need CHAIMS demonstration for evaluation
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CHAIMS proves that...
CHAIMS
• We can do composition in a high-level language.
• same language for Java-RMI-invocations and CORBA-invocations (and
DCE, DCOM, TCP/IP protocols)
• (single megaprogram can deal with multiple protocols simultaneously)
• multiple megamodules can run in parallel
• Large-scale composition can be automated.
• in contrast to manual non-software composition (e.g. telephone,
cut&paste)
• in contrast to fixed programs for one specific problem (e.g. transporting
military goods within US)
• We can do schedulings of programs in a way right now only
smart logistics officers can do, avoiding unnecessary waits.
• Scheduling of invocations can be optimized.
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CHAIMS
Backup slides
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Status
CHAIMS
• Definition of architecture for Megaprogramming
– bottom up assessment of code to be generated
– examples: room reservation, shipping
– primitives
– handles for parallel operation
– heterogeneity -- common features of distribution protocols
• Minimal language that can generate the code
– no versus very few types -- ASN.1 for complex types
– natural parallelism -- still a major research issue
• Awareness of novel optimizations
– information flow constraints -- scheduling
– direct data flow between megamodules
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Focus for Future
CHAIMS
• Augment CHAIMS compiler to generate multiple
feasible and effective paths for execution
• Create CHAIMS interpreter to complement compiler
and execute scheduling decisions.
• Dynamic scheduling of invocations and extractions.
• Flexible interaction with megamodules; extracting
and handling overview results.
• Direct dataflows between megamodules (planned).
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Composition of Processes...
CHAIMS
• versus composition and integration of Data
• data-warehouses
• wrapping data available on web
• versus composition of Components
• reusing small components via copy/paste or shared
libraries locally installed
• large distributed components within same “domain” as
composition, e.g. within one bank or airline
CHAIMS:
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» processed information
» composing autonomous execution threads
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CHAIMS “Logical” Architecture
Customer
Megaprogram clients
(in CHAIMS)
Network/Transport
(DCE, CORBA,...)
Megamodules
(Wrapped or Native)
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Summary
CHAIMS
• CHAIMS requires rethinking of many common
assumptions
– gain understanding via simple examples
• Work focused on CALL statement decomposition
– to accomplish integration of large services
– exploit inherent asynchrony
• First version of architecture and language drafts are
completed; basic infrastructure partially available
(compiler, wrapper templates).
• More demos will come soon.
Half-way through a four year project, but funding
resources were drastically reduced.
 http://www-db.stanford.edu/CHAIMS
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Long-term Objectives
CHAIMS
1 Implementing a system for a simple and purely
compositional language hiding differences of
diverse protocols
2 Automatic optimized scheduling of invocations
(taking advantage of inherent parallelism and
estimate-capabilities of megamodules, hence splitting
up of CALL-statement)
3 Decision-making support (direct) interaction with
megamodules, based on overview and incremental
results (fixed flow, not yet interactive changes to
megaprogram)
4 Automatic dataflow
optimization (direct dataflows 53
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Assumptions, Additional
Constraints
CHAIMS
•
Heterogenous legacy modules ==> wrapping of modules, mixing
protocols on client side or in wrappers.
• Parallelism of megamodule-methods not through multithreading on
client side but through splitting up CALL-statement (==> sequential
program on client side); this leads to useful parallelism because we
deal with coarse-grain parallelism.
• CHAIMS-compliancy for megamodules is achieved by wrappertemplates, for new native megamodules as well as for legacy ones
(CHAIMS-compliancy is more than just knowing CHAIMS-protocol!).
• No reliance on existence of one specific higher level protocol like
CORBA, DCOM, RMI ==> implementing an independent data-encoding
and marshalling with ASN.1, instead of using one of them and then
having converters in the wrappers.
• Interfaces of megamodules match <==> no investigation into opaque
datablobs on client side necessary.
• Thin client, client shouldEPFL6S
be able
tospring
run anywhere
(not quite fulfilled 54
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- Gio
2000
Non- (not yet)-Objectives
CHAIMS
• No commercial product.
• No specific controls over ilities (security, name-serving, etc.)
that they are normally present in distributed systems.
• No sophisticated front-end, no graphical
programming/composition, no browser for repository, no higherlevel language as input (not yet).
• Not solving all problems of megamodule composition that are
mentioned in the various CHAIMS-papers (e.g. differing
ontologies, non-matching interfaces of megamodules), only
the ones mentioned in objectives and additional conditions.
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Proposed Changes to Architecture:
Other Approach to Heterogeneity
client
site
Client (megaprogram)
TCP/IP sockets
CHAIMS protocol
sites of servers
different
wrapper site
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RMI
wrapper
CORBA
wrapper
RMI
wrapper
CORBA
RMI
server-specific protocols
native
server
1
native
server
2
native
server
3
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chaims
compliant
module
chaims
I/O
module
56
Open Source for Composition?
• Gnu
• Gnutella
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Reasons for an Alternative Architecture
Overall:
• Simpler architecture: fewer wrappers, just one protocol on client side
Server-side:
• No direct linking with legacy code also for CORBA-wrappers, different sites for
wrapper and legacy megamodule possible
• All native CHAIMS-megamodules will be built using wrapper templates ==> no reason
for several protocols, they can all use TCP/IP.
• Dataflow-optimization: direct messages between megamodules/their wrappers
necessary (without bridges)
Client-side:
• Thin client that could run everywhere (TCP/IP is available everywhere, but not CORBA
or DCE, RMI also is easily available everywhere).
• CSRT could be implemented by interpreter instead of compiler, maybe also possible
with current architecture, but more complex.
• We use just transport-facility (really true? what about native CHAIMS-types like string,
integer, boolean?) of CORBA, RMI, DCE (for data we have ASN.1); this is already
offered by TCP/IP ==> no unnecessary overkill
Drawback: missing one of the current funding objectives (heterogeinity on client side).
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