A Guided Tour of the Coign Automatic Distributed Partitioning System
Galen C. Hunt
Microsoft Research
Michael L. Scott
Department of Computer Science
University of Rochester
July 1998
Technical Report
MSR-TR-98-32
Microsoft Research
Microsoft Corporation
One Microsoft Way
Redmond, WA 98052
Submitted for Publication
A Guided Tour of the Coign Automatic Distributed Partitioning System
Galen C. Hunt
Michael L. Scott
Microsoft Research
One Microsoft Way
Redmond, WA 98052
galenh@microsoft.com
Department of Computer Science
University of Rochester
Rochester, NY 14627
scott@cs.rochester.edu
Abstract
Distributed object systems such as CORBA and DCOM
bring many advances to distributed computing. The distribution process itself, however, has changed little: programmers still manually divide applications into subprograms and assign those sub-programs to machines
with little automated assistance. Often the techniques
used to choose a distribution are ad hoc. Due to high
intellectual cost, applications are seldom repartitioned
even in drastically changing network environments.
We describe Coign, an automatic distributed partitioning
system (ADPS) that significantly facilitates the development of distributed applications. Given an application (in
binary form) built from distributable COM components,
Coign constructs a graph model of the application’s intercomponent communication through scenario-based profiling. Later, Coign applies graph-cutting algorithms to
partition the application across a network and minimize
distribution costs. Using Coign, an end user without
source code can transform a non-distributed application
into an optimized, distributed application.
Through a guided tour of Coign’s architecture and usage,
we present an overview of its features. We describe the
automatic distributed partitioning of three applications:
Microsoft Picture It!, the Octarine word processor, and
the Corporate Benefits Sample program. All are distributed automatically, sometimes with startling results. For
example, Coign makes significant changes to the programmer-assigned distribution of the Corporate Benefits
Sample.
1. Introduction
Distributed object systems such as CORBA and
DCOM bring the advantages of service location transparency, dynamic program instantiation, and object-oriented
programming to distributed applications. Unfortunately,
the process to distribute program components has
changed little: programmers still manually divide applications into sub-programs and manually assign those subprograms to machines. Often the techniques used to
choose a distribution are ad hoc, one-time solutions.
1
Given the effort required, applications are seldom repartitioned even in drastically different network environments. User usage patterns can severely stress a static
distribution of an application. Changes in underlying
network, from ISDN to 100BaseT to ATM, strain static
distributions as bandwidth-to-latency tradeoffs change by
more than an order of magnitude. Nonetheless, programmers resist repartitioning the application because it
often requires extensive modifications to source code and
program structure.
In this paper, we present a guided tour of Coign [5], an
automatic distributed partitioning system (ADPS) that
promises to significantly ease the development of component-based distributed applications. Given an application
built with COM components, Coign uses inexpensive
scenario-based profiling on a single computer to quantify
inter-component communication costs for both singlemachine and multi-machine distributions.
Intercomponent communication is modeled as a graph with
nodes representing components and edges representing
inter-component communication and location constraints.
Using graph-cutting algorithms, Coign selects a distribution of the application that minimizes communication
costs. At run time, Coign manipulates program execution
(with negligible overhead) to produce the desired distribution.
Coign analyzes an application, chooses a distribution,
and produces the desired distribution all without access to
application source. As a corollary, Coign is completely
language neutral; it neither knows nor cares about the
source language of the components in the application.
Moreover, because it operates on binaries, Coign preserves the ability to build applications from reusable,
third-party components.
In the following section we describe related work. In
Section 3, we illustrate how to use Coign to automatically
distribute an application. We describe Coign’s architecture in Section 4. Section 5 contains an experimental
evaluation of Coign’s effectiveness in distributing three
applications. Finally, in Section 6 we conclude and discuss future work.
2. Related Work
2.1. ICOPS
The idea of an ADPS is not new. The Interconnected
Processor System (ICOPS) [8, 14, 16] supported distributed application partitioning in the 1970’s. Under the
direction of Andries van Dam, ICOPS pioneered the use
of compiler-generated stubs for inter-process communication. ICOPS was the first system to use scenario-based
profiling to gather statistics for distributed partitioning;
the first system to support multiple distributions per application based on host-processor load; and the first system to use a maximum-flow-minimum-cut (MAXFLOW/MIN-CUT) algorithm [4] to choose distributions.
ICOPS was used to automatically distribute HUGS, a
two dimensional drafting program developed at Brown
University. HUGS consisted of seven modules. Three of
these—consisting of 20 procedures in all—could be located on either the client or the server.
ICOPS was never intended for shrink-wrapped, commercial applications. Tied to a single language and compiler, it relied on metadata generated by the compiler to
facilitate transfer of data and control between computers.
Modules compiled in another language or by another
compiler could not be distributed because they did not
contain appropriate metadata. ICOPS gave the application the luxury of location transparency, but still required
the programmer or user to explicitly select a distribution
based on machine load.
2.2. IDAP
Kimelman et al. [7] describe the Intelligent Dynamic
Application Partitioning (IDAP) system, an ADPS for
Smalltalk applications. IDAP is an add-on to IBM’s
VisualAge Generator. Using VisualAge Generator’s visual builder, a programmer designs an application by instantiating and connecting components in a graphical environment. The builder emits code for the created application.
The “dynamic” in the IDAP name refers to scenariobased profiling as opposed to static analysis. IDAP first
generates a version of the application with an instrumented message-passing mechanism. IDAP runs the instrumented application under control of a test facility with
the VisualAge system. After the application execution,
the programmer either manually partitions the components or invokes an automatic graph-partitioning algorithm. The algorithm used is an approximation algorithm
capable of multi-way cuts for two or more hosts [3]. After choosing a distribution, VisualAge generates a new
version of the application.
IDAP supports distributed partitioning only for statically allocated components. Although initially based on
Smalltalk, the distributable components are large-grain
2
components, not the fine-grained objects native to Smalltalk. Kimelman’s team has tested their system on a number of real Smalltalk applications, but in each case, the
application had “far fewer than 100” components [7].
The latest version of IDAP generates C++ code for
connecting CORBA components, but still does not support dynamic component instantiation [6]. Moreover, the
use of CORBA restricts IDAP to a distribution granularity
of whole processes because CORBA does not support
loading multiple component servers into the same address
space. The IDAP programmer must be vary aware of
distribution choices. IDAP helps the user to optimize the
distribution, but does not raise the level of abstraction
above the distribution mechanisms. With a full-featured
ADPS, such as Coign, the programmer can focus on component development and leave distribution to the system.
Although it supports multiple languages, IDAP still requires that applications be constructed with a specific
application development toolkit, the VisualAge Generator. Like ICOPS, IDAP supports automatic distributed
partitioning of static application pieces only. In the case
of ICOPS, the application pieces are procedures. In the
case of IDAP, the pieces are CORBA components or
large-grain Smalltalk objects.
2.3. Summary
Prior to Coign, no ADPS allowed distributed partitioning of binary components dynamically instantiated
during the execution of the application. Dynamic component instantiation is an integral feature of modern desktop
applications. One of the major contributions of our work
is a set of dynamic component classification algorithms
that map newly-created components to similar components identified during scenario based profiling.
Our research differs in scope from prior work because
we automatically distribute an existing class of commercial applications. All of the applications in our test suite
were developed by third parties with no knowledge of the
Coign system.
3. A Guided Tour
To solidify the concept of an ADPS, we describe a
detailed example of Coign’s usage to automatically distribute an existing COM application. The application
used in this example is a preliminary version of a future
release of Microsoft Picture It! [12]. (The original, uninstrumented version of Picture It! application is designed
to run on a single computer—it provides no explicit support for distribution.)
3.1. Creating a Distributed Application
Starting with the original binary files for Picture It!,
we use the setcoign utility to insert the Coign profiling
instrumentation package, see Figure 1. Setcoign
makes two modifications to the pi.exe binary file.
First, it inserts an entry to load the Coign Runtime Executive (RTE) Dynamic-Link Library (DLL) into the first
slot in the application’s DLL import table. Second,
setcoign adds a data segment containing configuration
information to the end of pi.exe. The configuration
information tells the Coign RTE how the application
should be profiled and which of several algorithms should
be used to identify components during execution.
Because it occupies the first slot in the application’s
DLL import table, the Coign RTE will always load and
execute before the application or any of its other DLLs. It
therefore has a chance to modify the application’s address
space before the application runs. The Coign RTE takes
advantage of this opportunity to insert binary instrumentation into the image of system libraries in the application’s address space. The instrumentation traps all component instantiation functions in the COM library. Before
returning control to the application, the Coign RTE loads
any additional Coign components (described in Section 4)
as stipulated by the configuration information stored in
the application.
With the Coign runtime configured for profiling, the
application is ready to be run through a set of profiling
scenarios; see Figure 2. Because the binary has been
modified transparently to the user (and to the application
itself), profiling runs behave from the user’s point of view
as if there were no instrumentation in place. The instrumentation gathers profiling information in the background
while the user controls the application. The only visible
effect of profiling is a degradation in application performance of up to 85%. For our simple example, we start
Picture It!, load a file for preview, and exit the application. For more advanced profiling, scenarios can be
driven by an automated testing tool, such as Visual Test
[2].
During profiling, the Coign instrumentation maintains
running summaries of the inter-component communication within the application. Coign quantifies every intercomponent function call through a COM interface. The
instrumentation measures the number of bytes that would
have to be transferred between machines if the two communicating components were distributed. The number of
bytes is calculated by invoking portions of the DCOM
code, including the interface proxy and stub, within the
application’s address space. Coign measurement follows
precisely the deep-copy semantics of DCOM. After calculating communication costs, Coign compresses and
summarizes the data online to keep instrumentation storage requirements at a minimum. If desired, the application may be run through profiling scenarios for days or
even weeks to more accurately track user usage patterns.
In all of our tests, storage overhead for Coign never exceeded 1.5 MB.
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D:\apps\pictureit\bin> setcoign /p pi.exe
Config:
Logger:
Coign Profile Logger
Informer:
Coign NDR Interface Informer
Classifier: Coign EP3C Classifier
Sections: 4
___VAddr ___VSize ___VAEnd ___FAddr ___FSize
.text
1000
10e343
10f343
400
10e400
.rdata
110000
501c3
1601c3
10e800
50200
.data
161000
11224
172224
15ea00
d400
.rsrc
173000
15868
188868
16be00
15a00
.coign
189000
6cd0
18fcd0
181800
6e00
Debug Directories:
0. 00000000 00181800..00181910 -> 00188600..00188710
1. 00000000 00181910..001819c0 -> 00188710..001887c0
2. 00000000 001819c0..001819ea -> 001887c0..001887ea
Extra Data:
512 ( 181a00 181800)
Coign Extra Data:
{9CEEB02F-E415-11D0-98D1-006097B010E3} : 4 bytes.
R
0
0
0
0
0
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0
0
0
0
0
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0
0
Figure 1, Inserting Coign into the Application.
Setcoign rewrites the pi.exe binary to insert Coign
profiling instrumentation into Picture It!.
Figure 2, Executing a Profiling Scenario. With
Coign instrumentation in place, the application is run
through one or more profiling scenarios to measure
inter-component communication. In this scenario,
Picture It! loads and renders a composite image.
At the end of the profiling execution, Coign writes the
summary log of inter-component communication to a file
for later analysis; see Figure 3. In addition to information
about the number and sizes of messages and components
in the application, the profile log also contains information to classify components to determine component location constraints. Log files from multiple profiling executions may be combined and summarized during later
analysis. Alternatively, at the end of each profiling execution, information from the log file may be inserted into
the configuration record in the application executable (the
pi.exe file in this example). The latter approach uses less
storage because summary information in the configuration
record accumulates communication from similar interface
calls into a single entry.
Invoking adpcoign initiates post-profiling analysis,
see Figure 4. Adpcoign examines the system service
libraries to determine any location constraints on application components.
For client-server distributions,
adpcoign recognizes components that must be placed
D:\apps\pictureit\bin> pi.exe
[Coign Runtime Environment: 00000080 636f6900 00000000]
[Coign EP3C Classifier/9999]
[Coign NDR Interface Informer]
[Coign Profiling Logger (16 cycles)]
[CoignRTE: DLL_PROCESS_ATTACH]
[CoignRTE: DLL_THREAD_ATTACH]
[CreateFileMoniker( D:\apps\pictureit\docs\MSR.mix )]
[StgOpenStorage( D:\apps\pictureit\docs\MSR.mix )]
[CoignRTE: DLL_THREAD_DETACH]
[Elapsed time: 26400 ms]
[CoignRTE: DLL_PROCESS_DETACH]
[Inter-component communication:
]
[ Messages : ____16 ____64 ___256 __1024 __4096 __16384 __Totals ]
[ In Counts : 105240
1629
473
1599
66
45
109052 ]
[ Out Counts: 102980
4303
843
783
131
12
109052 ]
[ In Bytes : 782022 57912 49616 815034 157619 237963 2100166 ]
[ Out Bytes : 455207 130140 95473 304592 239239
70019 1294670 ]
Figure 3, Logging Communication. At the conclusion of the profiling scenario, Coign logs the intercomponent communication to a file for later analysis.
D:\apps\pictureit\bin> adpcoign pi.log
Binaries:
pi.exe
mso97d.dll
mfc42d.dll
mfco42d.dll
oleaut32.dll
Dependencies:
01 D:\apps\pictureit\bin\pi.exe
piperf.dll
oleaut32.dll
00 D:\apps\pictureit\bin\piserv.dll
mfc42d.dll
00 D:\apps\pictureit\bin\mfco42d.dll
00 C:\winnt\system32\ole32.dll
Objects:
112
Interfaces:
792
Calls:
38286
Bytes:
743534
Proc. Speed:
200MHz
Figure 4, Post-Profiling Analysis. Adpcoign analyzes the log file to create an abstract model of intercomponent communication.
D:\apps\pictureit\bin> setcoign /f:pi.set pi.exe
Config:
pi.set
Informer:
Coign Light Interface Informer
Classifier: Coign EP3C Classifier
Sections: 5
___VAddr ___VSize ___VAEnd ___FAddr ___FSize
.text
1000
10e343
10f343
400
10e400
.rdata
110000
501c3
1601c3
10e800
50200
.data
161000
11224
172224
15ea00
d400
.rsrc
173000
15868
188868
16be00
15a00
.coign
189000
83f8
1913f8
181800
8400
Debug Directories:
0. 00000000 00189a00..00189b10 -> 00189c00..00189d10
1. 00000000 00189b10..00189bc0 -> 00189d10..00189dc0
2. 00000000 00189bc0..00189bea -> 00189dc0..00189dea
Coign Extra Data:
{9CEEB022-E415-11D0-98D1-006097B010E3} : 4980 bytes.
{9CEEB030-E415-11D0-98D1-006097B010E3} : 904 bytes.
{9CEEB02F-E415-11D0-98D1-006097B010E3} : 4 bytes.
R
0
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0
Figure 5, Inserting the Model into the Application.
An abstract model of inter-component communication
is written into the application binary for distribution.
on the client in order to access the Windows GUI libraries
or that must be placed on the server in order to access
persistent storage directly.
Combining location constraints and information about
inter-component communication, adpcoign creates an
abstract graph model of the application. In the current
implementation, adpcoign combines the abstract graph
model with data about the network configuration to create
a concrete model of the cost of distribution on a real network. Adpcoign then uses a graph-cutting algorithm to
choose a distribution that minimizes communication
costs. In the future, the construction of the concrete
4
model and the graph-cutting algorithm could be performed at application execution time, thus potentially
producing a new distribution tailored to current network
characteristics.
After analysis, the application’s inter-component
communication model is written into the configuration
record in the application binary; see Figure 5. Any residual profiling logs are then removed from the configuration
record. The configuration record is also modified to disable the profiling instrumentation. In its place, a lightweight version of the instrumentation will be loaded to
realize (enforce) the distribution chosen by the graphcutting algorithm.
Aside from the inter-component communication
model, perhaps the most important information written
into the application configuration is data for the component classifier. The component classifier matches components created during distributed executions to components created during the profiling scenarios. The abstract
model of inter-component communication contains nodes
for all known components and edges representing the
communication between components. To determine
where a component should be located in a distributed
execution, the classifier tries to match it to the most similar component in the profiling scenario. The premise of
scenario-based profiling is that profiled executions closely
match post-analysis executions. Therefore, if a component is similar to a component in a profiling execution,
then it will most likely have similar communication patterns. Based on the chosen distribution for a similar profiled component, the classifier decides where new components created during the distributed execution should be
instantiated.
Figure 6 shows the distribution chosen for our profiled
scenario. In this scenario, the user loads and previews an
image in Microsoft Picture It! from a server. Each of the
large black dots in Figure 6 represents a dynamic component in the profiled scenario. Lines between the components represent COM interfaces through which the connected components communicate. In the on-screen version of Figure 6, lines are colored according to the
amount of communication flowing across the interface.
Red lines represent interfaces with large amounts of
communication (communication hot spots) and blue lines
represent interfaces with minimal communication.
Solid, black lines represent interfaces that are not remotable (i.e., pairs of components that must reside on the
same machine). An interface may not be remotable for
any of the following reasons: the interface has no Interface Definition Language (IDL) description to enable
parameter marshaling; one or more of the interface parameters is opaque, such as a “void*”; the client directly
accesses the component’s internal data; or the component
must reside on the client or the server because it directly
accesses system services.
The “pie” slice in the top half of Figure 6 contains
those components that should be located on the server to
minimize network traffic and thus execution time. In our
example, the operating storage services, the document file
component, and three “property set” components are all
located on the server. Note that approximately one dozen
“property set” components (of the “PI.PropSet” class) are
located on the client. In order to achieve optimal performance, a component-based ADPS must be able to place
components of the same class on different machines.
After the abstract distribution model is written into the
binary, the application is prepared for distribution. When
the application user instructs Picture It! to load an image
from the server, the lightweight version of the Coign runtime will trap the related instantiation request and relocate
it to the server. The four components chosen for distribution in Figure 6 are automatically distributed to the server.
Coign distributes the component to the server by starting
a surrogate process on the server. The surrogate acts as a
distributed extension of the application; described components reside in its address space. A distributed version
of the Coign runtime maintains communication links between the original application process on the client and
the surrogate process on the server.
Figure 7 shows the distributed version of Picture It!.
The window in the lower right corner of Figure 7 contains
the surrogate process and the components distributed on
the server. Coign automatically created a distributed version of Microsoft Picture It! without access to the application source code or the programmer’s knowledge of the
application. The automatic distributed application is
customized for the specific network to minimize distributed communication costs.
Figure 6, Choosing a Distribution. Coign cuts the
graph using online network performance parameters to
minimize distributed communication costs. The pie
slice in the upper right contains the components selected for distribution on the server.
3.2. Discussion
We envision two Coign usage models to create distributed applications. In the first model, Coign is used with
other profiling tools as part of the development process.
Coign shows the developer how to distribute the application optimally and provides the developer with feedback
about which interfaces are communication “hot spots.”
The programmer can fine-tune the distribution by inserting custom marshalling and caching on communication
intensive interfaces. The programmer can also enable or
disable specific distributions by inserting or removing
location constraints on specific components and interfaces. Alternatively, the programmer can create a distributed application with minimal effort simply by running
the application through profiling scenarios and writing the
corresponding distribution model into the application binary without modifying application sources.
In the second usage model, Coign is used onsite by the
application user or system administrator. The user enables application profiling through a simple GUI to the
setcoign utility. After “training” the application to the
5
Figure 7, The Distributed Application. Component
instantiation requests are relocated to produce the distributed application. The new, distributed application
is functionally equivalent to the original, nondistributed application. Distribution is achieve by
modifying the application binary at run time, not the
application sources.
user’s usage patterns—by running the application through
representative tasks with profiling—the GUI triggers
post-profiling analysis and writes the distribution model
into the application. In essence, the user has created a
customized version of the distributed application without
any knowledge of the underlying details.
In the future, the Coign could automatically decide
when usage differs significantly from the profiled sce-
narios and silently enable full profiling for a period to reoptimize the distribution. The Coign runtime already
contains sufficient infrastructure to allow “fully automatic” distribution optimization. The lightweight version
of the runtime, which relocates component instantiation
requests to produce the chosen distribution, could count
messages between components with only slight additional
overhead. Run time message counts could be compared
with relative message counts from the profiling scenarios
to recognize changes in application usage.
4. Architecture of the Coign ADPS
The Coign runtime is composed of a small collection
of replaceable COM components; see Figure 8. The most
important components are the Coign Runtime Executive
(RTE), the interface informer, the information logger, the
component classifier, and the component factory. The
RTE provides low-level services to the other components
in the Coign runtime. The interface informer identifies
interfaces by their static type and provides support for
walking the parameters of interface function calls. The
information logger receives detailed information about all
component-related events in the application from the RTE
and the other Coign runtime components. The information logger is responsible for recording relevant events for
post-profiling analysis. The component classifier identifies components with similar communication patterns
across multiple program executions. The component
factory decides where component instantiation requests
should be fulfilled and relocates instantiation requests as
needed to produce a chosen distribution.
4.1. Runtime Executive
The Coign Runtime Executive is the first DLL loaded
into the application address space. As such, the RTE runs
before the application or any of its components. The RTE
patches the COM library and other system services to trap
component instantiation requests. The RTE reads the
configuration information written into the application
binary by the setcoign utility. Based on information
in the configuration record, the RTE loads other components of the Coign runtime.
The RTE provides a number of low-level services to
the other components in the Coign runtime including:
interception of component instantiation requests; wrapping of component interfaces to intercept inter-component
messages; management of thread-local stack storage for
use by other components in the Coign runtime; and access
to configuration information stored in the application binary.
The current Coign runtime contains a single RTE. The
DLLs for profiling and “regular” program execution differ in the choice of components to run on top of the RTE.
6
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Figure 8, Coign’s Component Architecture. Runtime components can be replaced to produce lightweight instrumentation, detailed inter-component
communication trace logs, or remote component instantiation.
4.2. The Interface Informer
The interface informer locates and manages interface
metadata. With assistance from the interface informer,
other components can determine the static type of a COM
interface, and walk both the input and output parameters
of an interface function call. The current Coign runtime
contains two interface informers. The first interface informer operates during scenario-based profiling. The
profiling interface informer uses format strings generated
by the MIDL compiler [13] and interface marshalling
code to analyze all function call parameters and precisely
measure inter-component communication. Profiling currently adds up to 85% to execution run time. Most of this
overhead is incurred by the interface informer.
The second interface informer is used after profiling to
produce the distributed application. The distributed informer only examines enough function call parameters to
locate interface pointers. As a result of aggressive preexecution optimization of interface metadata, the distributed informer imposes an execution overhead of less than
3% on most applications.
4.3. The Information Logger
The information logger summarizes and records data
for automatic distributed partitioning analysis. Under
direction of the RTE, Coign runtime components pass
information about a number of events to the information
logger. The logger is free to process the events as it
wishes. Depending on the implementation, it might ignore the event, write the event to a log file on disk, or
accumulate information about the event into in-memory
data structures. The current implementation of the Coign
runtime contains three separate information loggers. The
profiling logger summarizes data describing intercomponent communication into in-memory data structures. At the end of execution, these data structures are
written to disk for post-profiling analysis. The event logger creates detailed traces of all component-related events
during application execution. One of our colleagues has
used traces generated by the event logger to drive detailed
simulations of the execution of component-based applications. The null logger ignores all events. Use of the null
logger insures that no extra files are generated during
execution of the automatically distributed application.
4.4. Component Classifier
The component classifier identifies components with
similar communication patterns across multiple executions of an application. Coign’s scenario-based approach
to automatic distribution depends on the premise that the
communication behavior of a component during a distributed application can be predicted based on the component’s similarity to another component in a profiling scenario. Since, in the general case, it is impossible to determine a priori the communication behavior of a component, the component classifier groups components with
similar instantiation histories. The classifier operates on
the theory that two components created under similar circumstances will display similar behavior. The output of
the post-profiling graph-cutting algorithm is a mapping of
component classifications to computers in the network.
The current Coign runtime includes eight component
classifiers created for evaluation purposes. Experiments
indicated that the most accurate results are produced (at
reasonable cost) by grouping components of the same
type that are created in the presence of the same stack
backtrace.
4.5. Component Factory
The component factory produces the distributed application. Using output from the component classifier and
the graph-cutting algorithm, the component factory moves
each component instantiation request to the appropriate
computer within the network. During distributed execution, a copy of the component factory is replicated onto
each machine. The component factories act as peers.
Each traps component instantiation requests on its own
machine, forwards them to another machine as appropriate, and fulfills instantiation requests destined for its machine by invoking COM to create the new component
instance. The job of the component factory is very
straightforward since most of the difficult problems in
creating a distributed application are handled either by the
underlying DCOM system or by the component classifier.
Coign currently contains a symbiotic pair of component
factories. Used simultaneously, the first factory handles
communication with peer factories on remote machines
while the second factory interacts with the component
classifier and the interface informer.
7
4.6. Summary
The component structure of the Coign runtime allows
it to be used for a wide range of analysis and adaptation
tasks. The scenario-profiling runtime is converted to the
distribution-realization runtime by changing the interface
informer and information logger components. By changing just the information logger component, one of our
colleagues has generated extensive traces of the activity
of component-based applications. These traces were used
to drive a simulator to analyze the performance of algorithms for a large distributed object system.
5. Evaluation
Because it makes distribution decisions at component
boundaries, Coign depends on programmers to build applications with significant numbers of components. For
our experiments, we use a suite of three applications built
from COM components: Microsoft Picture It!, Octarine,
and the Corporate Benefits Sample. We believe that these
applications represent a wide class of COM applications.
Microsoft Picture It! [12] is a consumer application for
manipulating digitized photographs. Picture It! includes
tools for selecting a subset of an image, applying a set of
transforms to the subset, and inserting the transformed
subset into another image. Picture It! is a non-distributed
application composed of approximately 112 COM component classes in 1.8 million lines of C++ source code.
Designed as a prototype to explore the limits of component granularity, Octarine is a document processing
application similar to Microsoft Word. Octarine contains
approximately 150 classes of components ranging in
granularity from less than 32 bytes to several megabytes.
Octarine’s components range in functionality from userinterface buttons to generic dictionaries to sheet music
editors. Octarine manipulates three major types of documents: word-processing documents, sheet-music documents, and table documents. Fragments of any of the
three document types can be combined into a single
document. Octarine is composed of approximately
120,000 lines of C and 500 lines of x86-assembly source
code.
The Corporate Benefits Sample [11] is an application
distributed by Microsoft Corporation to demonstrate the
use of COM for creating client-server applications. The
Corporate Benefits Sample provides windows for modifying, querying, and creating graphical reports on a database of corporate employees and their benefits. The entire sample application contains two separate client frontends and four distinct server back-ends. For our purposes, we use a client front-end consisting of approximately 5,300 lines of Visual Basic code and a server
back-end of approximately 32,000 lines of C++ source
code with approximately one dozen component classes.
Benefits leverages commercial components (distributed in
binary form only) such as the graphing component from
Microsoft Office [10].
Each of the applications in our test suite is dynamic
and user-driven. The number and type of components
instantiated in a given execution is determined by user
input at run time. For example, a scenario in which a user
inserts a sheet music component into an Octarine document will instantiate different components than a scenario
in which the user inserts a table component into the
document.
5.1. Simple Distributions
For our first set of experiments, we ran each application in the test suite through a simple profiling scenario
consisting of the simplest practical usage of the application. After profiling, Coign partitioned each application
between a client and server of equal computational power
on an isolated 10BaseT Ethernet network. For simplicity,
we assume that there is no contention for the server.
Figure 9 contains a graphical representation of the distribution of Microsoft Picture It!. In the profiling scenario, Picture It! loaded a 3 MB graphical composition
from storage, displayed the image, and exited. Of 295
components in the application, eight were placed on the
server. One of the components located on the server is
the component that reads the document file. The other
seven components are high-level property sets created
directly from data in the file. The property sets are located on the server because their output data set is smaller
than their input data set.
As can be seen in Figure 9, Picture It! contains a large
number of interfaces that can not be distributed; represented by solid black lines. The most important nondistributable interfaces connect the sprite cache components (on the bottom and right) with the user interface
components. Each sprite cache component manages the
pixels for an image in the composition. Most of the data
passed between sprite caches moves through shared
memory regions. These shared-memory regions correspond to non-distributable component interfaces. While
Coign can extract a functional distribution from Picture
It!, most of the distribution granularity in the application
is hidden by non-distributable interfaces. To enable other,
potentially better distributions, either the non-distributable
interfaces in Picture It! must be replaced with distributed
IDL interfaces, or Coign must be extended to support
transparent migration of shared memory regions; in essence leveraging the features of a software distributed
shared memory (DSM) system [1, 15].
Figure 10 contains a graphical representation of the
distribution of the Octarine word processor. In this sce-
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Figure 10, Distribution of Octarine. Of 458 components in the application, two are placed on the server.
nario, Octarine loaded and displayed the first page of a
35-page, text-only document. Only two components of
458 are located on the server. One of the components
reads the document from storage; the other provides information about the properties of the text to the rest of the
application. While Figure 10 contains a number of nondistributable interfaces, these interfaces connect components of the GUI, and are not directly related to the document file. Unlike the other applications in our test suite,
Octarine is composed of literally hundreds of components. It is highly unlikely that these GUI components
would ever be located on the server. Direct documentrelated processing for this scenario is limited to 24 components.
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Figure 11, The Corporate Benefits Sample. Of 196
components in the client and middle tier, Coign locates 135 of the components on the middle tier rather
than the 187 components chosen by the programmer.
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In the original distribution, chosen by a programmer,
187 of 196 components are located in the middle tier.
The programmer’s distribution is a result of two design
choices. First, the middle tier represents a conceptually
clean separation of business logic from the other pieces of
the application. Second, the front-end is written in Visual
Basic, an extremely popular language for rapid development of GUI applications while the business logic is
written in C++.
Coign analysis shows that application performance can
be improved by moving some of the business-logic components from the middle tier into the client. The distribution chosen by Coign is quite surprising. Of 196 components in the client and middle tier, Coign would locate
just 135 on the middle tier. It would be extremely awkward for the programmer to create the distribution chosen
by Coign because it runs counter to the programmer’s
logical separation of the application. The Corporate
Benefits Sample demonstrates that Coign can improve the
distribution of applications designed by experienced client-server programmers.
H
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UDLOH W GQWGW GLIL W GRW
WRF) DLQLQDLLQDLQLQRWDLQLQDLQ
3:3:13:
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Figure 12, Octarine with a Multi-page Table.
Coign locates only a single component on the server
for a document containing five-page table.
Figure 11 plots the distribution for the Corporate Benefits Sample. As shipped, Benefits can be distributed as
either a 2-tier or a 3-tier client-server application. In the
2-tier scenario, the Visual Basic front-end and the business-logic components are located on the client while the
database is located on the server and accessed through
ODBC [9]. In the 3-tier scenario, the Visual Basic frontend is located on the client, the business-logic components are located on the middle tier, and the database is
located on the server. Coign cannot analyze proprietary
connections between the ODBC driver and the database
server. We therefore focus our analysis on the distribution of components in the front end and middle tier.
9
5.2 Changing Scenarios and Distributions
The results in the previous section demonstrate that
Coign can automatically choose a partition and distribute
an application. The Benefits example notwithstanding,
one could argue that an experienced programmer with
appropriate tools could partition the application at least as
well manually. Unfortunately, a programmer’s best-effort
manual distribution is static; it cannot readily adapt to
changes in network performance or user-driven usage
patterns. In the realm of changing environments, Coign
has a distinct advantage as it can repartition and distribute
the application arbitrarily often. In the limit, Coign can
create a new distributed version of the application for
each execution.
The merits of a distribution customized to a particular
usage pattern are not merely theoretical. Figure 12 plots
the optimized distribution for Octarine loading a document containing a single, 5-page table. For this scenario,
Octarine places only a single component out of 480 in the
application on the server. The results are comparable to
those of Octarine loading a document containing strictly
text, see Figure 10. However, if fewer than a dozen small
tables are added to the 5-page text document, the optimal
distribution changes radically. As can be seen in Figure
13, Coign places over 282 component on the server out of
786 created in the scenario. The difference in distribution
is due to the complex negotiations for page placement
between the table components and the text components.
Output from the page-placement negotiation to the rest of
the application is minimal.
In a traditional distributed system, the programmer
would most likely be forced to optimize the application
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lennium will provide automatic migration of live components. We are attempting to create distributed systems
that are self-tuning and self-configuring; automatically
adapting to changes in hardware resources and application
workload.
Creating a higher level of abstraction does not come
without cost in performance. However, Millennium
hopes to gain efficiency by spontaneously optimizing
applications for the current distributed environment and
eliminating unnecessary boundaries between the operating system and programming language runtimes.
Bibliography
[1]
Figure 13, Tables Interspersed Between Text. With a
five-page document containing fewer than a dozen embedded tables, Coign places 282 of 786 application components on the server.
for the most common usage pattern. At best, the programmer could embed a minimal number of distribution
alternatives into the application. With Coign, the programmer need not favor one distribution over another.
The application can be distributed with an intercomponent communication model optimized for the most
common scenarios. Over the installed lifetime of the application, Coign can periodically re-profile the application
and adjust the distribution accordingly. Even without
updating the inter-component communication model,
Coign can adjust to changes in application infrastructure,
such as changes in the relative computation power of the
client or server (through an extension to the graph-cutting
algorithm), or changes in network latency and bandwidth.
[2]
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[4]
[5]
[6]
6. Conclusions and Future Work
We have described the Coign ADPS. Coign is the first
ADPS to automatically distribute binary applications built
from components. Coign has successfully distributed
three commercial-grade applications: Microsoft Picture
It!, the Octarine word processor, and the Corporate Benefits Sample from the Microsoft Developer Network
(MSDN). The largest of these programs is 1.8 million
lines of source code. In terms of complexity, several of
the Octarine scenarios we have tested instantiate over
3,800 components. Coign has even proven effective in
re-optimizing manually distributed applications.
Coign is part of the Millennium project at Microsoft
Research. The project aims to develop distributed systems that provide a new level of abstraction for application programmers and users, managing machines and
network connections for the programmer in the same way
that operating systems today manage pages of memory
and disk sectors. Beyond the capabilities of Coign, Mil-
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