GREATFREE: THE JAVA APIS AND IDIOMS TO PROGRAM LARGE-SCALE DISTRIBUTED SYSTEMS

International Journal of Advanced Information Technology (IJAIT) Vol. 6, No. 1, February 2016
DOI : 10.5121/ijait.2016.6101 1
GREATFREE: THE JAVA APIS AND IDIOMS TO
PROGRAM LARGE-SCALE DISTRIBUTED SYSTEMS
Bing Li
Department of Computer Science and Engineering, Xi’An Technological University,
Xi’An, China
ABSTRACT
This paper introduces a series of APIs and idioms in Java SE (Java Standard Edition), GreatFree, to
program large-scale distributed systems from scratch without adopting any third party frameworks. When
programming with GreatFree, developers are required to take care of rather than be invisible to most of
the implementation issues in a distributed system. It not only strengthens developers’ skills to polish a
system but also provides them with the techniques to create brand new and creative systems. However,
taking care of many such issues is a heavy load because of the low-level of Java SE. To alleviate the burden
to program with Java SE directly, GreatFree provides numerous APIs and idioms in Java SE to help
programmers resolve indispensable distributed problems, such as communication programming,
serialization, asynchronous and synchronous programming, resource management, load balancing,
caching, eventing, requesting/responding, multicasting, and so forth. Additionally, as an open source tool
to program, developers are able to strengthen their systems through not only adjusting GreatFree
parameters but also upgrading GreatFree APIs and idioms themselves. According to the current intensive
experiments, it is convenient for developers to program an ordinary or a large-scale distributed system
from scratch with GreatFree.
KEYWORDS
Design Patterns, Distributed Systems, Wireless Network, Idioms, Application Program Interface,
Distributed Programming, Concurrency
1. INTRODUCTION
The paper exhibits an open-source programming tool, GreatFree, to assist developers to program
large-scale distributed systems from scratch. Based on Java SE, GreatFree includes a series of
APIs and idioms to ease the programming procedure. Nowadays most developers avoid
programming a distributed system directly because of many issues to be resolved. In contrast, it is
more convenient to configure mature open sources and commercial frameworks than to program
with generic low-level programming languages like Java SE. However, in many specific cases it
needs to implement a system by programming rather than configuring. Professional developers are
required to be competent with dealing with complicated problems in a distributed computing
environment through programming.
To implement a distributed system by programming, it encounters many implementation barriers,
such as communication [1], serialization [1], asynchronous and synchronous management [2][3],
resource management [4], load balancing [5], caching [6], eventing and requesting/responding
[7], multicasting [8], and so forth. It is tough to implement such a system with generic
fundamental programming languages like Java SE. Nevertheless, programming a distributed
system from scratch is required in many specific cases. First of all, a brand new creative system
requires developers to program the low-level models themselves to construct a self-contained

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system other than to resolve high-level problems only. The goal can never be achieved with the
approaches of configuring and customizing. Second, a system needs to be flexible enough to be
customized for a specific issue by programming. Though it is seldom that all of the distributed
features be implemented from scratch, a limited change is frequently required in a system. It is
convenient for programmers to handle that only if all of the code is open and easy to be changed.
Third, for professional developers, it is required for them to be competent with the task of
programming a complicated system without the assistance of mature frameworks. Fourth, for
security reasons, some systems, such as a creative one in a new environment, are forced to
implement from an initial level. For those considerations, a bunch of APIs and idioms, which is
named GreatFree, are proposed to assist developers to program distributed systems, specially the
large-scale ones.
The methodology GreatFree represents encourages proficient developers to program their own
distributed systems rapidly rather than to configure a mature framework. It provides developers
with a new programming tool that focuses on the large-scale distributed computing environment.
To achieve the goal, it prevents developers from heavy loads of programming a complicated
system with fundamental programming languages, such as Java SE (Java Standard Edition) [9].
For that, it comes up with a bunch of APIs (Application Program Interfaces) and design patterns
as idioms to solve the specific distributed issues such as client/server [10] and peer-to-peer
models [10], distributing eventing [7] and polling [7], resource management [4], distributed
concurrency and synchronization [2][3], distributed multicasting [8], distributed clusters [10] for
computing and memorizing, and so forth. With the support of the GreatFree APIs and idioms, it is
convenient for developers to program a large-scale distributed system from scratch. In brief,
GreatFree is a programming environment that resides between the mature configurable systems
and the naked generic programming languages. Using it, a high quality distributed system can be
conveniently programmed from scratch.
When using GreatFree, it is required for developers to keep most of the distributed issues in mind.
That is different from the traditional opinions of software engineering, which emphasizes the goal
to hide all of the details of low-level systems such that developers are invisible to the specific
computing environments. They need only to change limited parameters or configurations
following tutorials and guiding books without worrying about what happens underlying high-
level applications. On the contrary, the methodology of GreatFree proposes that most important
techniques should be discernible to developers such that they need to resolve them by
programming with relevant APIs and patterns. Developers are required to compose those stuffs to
form an arbitrary application. Although mature systems minimize developers’ effort, they also
lower developers’ abilities such that they can hardly deal with changes if they do not program for
a long-term. Such developers without sufficient knowledge might be able to customize a high
quality system by configuring mature systems. But they will never be able to implement a
creative system in a new domain. Therefore, using GreatFree, developers always need to learn
and keep in mind the relevant technologies. And then, when they would like to implement a brand
new system, the methodology of GreatFree become their potential silver bullet.
On the other hand, GreatFree is different from the naked generic programming languages like
Java SE. First of all, it contains a series of domain-specific APIs and idioms, which focus on the
area of distributed systems, especially the large-scale ones whereas a generic programming
language is required to deal with all of the issues in any domains. The convenience of GreatFree
is that developers’ programming loads are reduced magnificently for its well-defined APIs and
idioms. Although they need to be aware of the issues of distributed systems, they are not required
to implement them using Java SE. On the contrary, they just need to program in a composing
manner with the APIs and idioms from GreatFree. More important, proficient programmers are
encouraged to revise the code of the APIs and idioms because all of the source code of GreatFree
is open [11].

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For the distinguished character of GreatFree, developers of the programming tool need to learn
the distributed issues at first. To be invisible to the problems, they must have no idea which APIs
and idioms should be chosen to program with a composing style. Fortunately, the programming
procedure looks like constructing a tower with high quality building blocks rather than sands and
stones directly.
The main contributions of the paper are summarized as follows.
As a methodology, a developer needs to learn the core techniques of the system they are
programming rather than to configure a mature system without knowing anything about its
internal principles. This is especially important to design a brand new and creative system.
To lower the burden of developers’ effort, open source based APIs and idioms are effective in
terms of its high quality and high changeability.
According to the above opinions, a series of APIs and idioms are proposed to help developers
program a large-scale distributed system.
The sections of the paper are organized as follows. Section 2 gives an introduction to the related
work, including configuring mature frameworks, design patterns and programming scratch.
Section 3 addresses the primary methodologies of GreatFree. Section 4 presents GreatFree APIs
to support distributed system programming. Section 5 discusses GreatFree idioms that compose
GreatFree APIs. Section 6 explains the experiment environment of GreatFree APIs and idioms.
Section 7 describes the future work.
2. RELATED WORK
In the domain of software engineering, to lower the burden to implement a distributed system,
many mature frameworks are put forward as open sources [12][13][14][15] or commercial
products [16] for specific application environments. Thus, developers are not required to program
but configure those systems to fulfill their requirements. In addition, although some generic
programming languages [9] are strengthened gradually to lower the cost to program, their
distances to a distributed system are still too long to be reached by coding directly for most
programmers. Finally, design patterns [17][18][19][20][21][22] are also believed to be one
approach to implement a complicated system like a distributed one. However, until now, most
design patterns are not the code in specific languages to be reused conveniently. Although idioms,
as small-scale design patterns, intend to resolve the issue, a comprehensive solution of idioms is
not available in the domain of distributed systems.
2.1. Configuring Mature Systems
Nowadays when implementing a distributed system, a common phenomenon is that developers
get accustomed to downloading existing mature open sources or commercial systems rather than
programming themselves line by line. No matter whether a system is complicated enough like a
so-called cloud [15] or even a fundamental one like a client/server model [10], most developers
give up implementing a system through programming. Although each of them must take the
relevant classes or trainings in universities or other schools, they become scared about
programming those systems themselves.
The primary reason is due to the fact that those mature systems could fulfill their requirements.
For example, to set up a Web site, Tomcat [12] is one of the popular choices as a Web server.
Even for a gaming system, a Web server like Tomcat is suitable to support its centralized

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information exchanging among users. To initiate an e-commerce online system, developers prefer
the Java EE (Java Enterprise Edition) [13] to the generic programming language, Java SE (Java
Standard Edition) [9], since the former one hides most details of distributed techniques for
enterprises from developers.
In addition, it is difficult to program high quality distributed systems by programming from
scratch. Developers have to be aware of numerous technical details, such as network
communication [1], serialization [1], asynchronous and synchronous programming [2][3], caching
[6], eventing and requesting/responding [7], resource management [4], load balancing [5],
multicasting [8], and so forth. All of the issues turn out to be a heavy workload to program.
Finally, most mature systems are adaptable to subtle changes in their specific environments.
Those systems allow developers to configure in the event that some requirements cannot be
fulfilled by default. They provide developers with configuration files in an XML [23] format such
that they can set up their own preferences through modifying relevant parameters. For example, to
update the Solr [24] configuration file, developers are able to construct a tree-like structure to
distribute searching loads. When using JBoss [25], WebSphere [26] or other application servers,
business transactions [6] are required to be specified in configuration files.
In short, when the requirements are clear and the relevant mature frameworks are available to the
specific environment, it is reasonable to select an appropriate commercial framework or
download an open source system to accomplish the goal rapidly.
2.2. Disadvantages of Configuring Mature Systems
However, besides the advantages of mature frameworks, developers should be aware of the
disadvantages when using them to implement their systems. First of all, all of those mature
systems are specific to their respective particular domains and their adaptability is restricted to
narrow scopes. Although most existing frameworks claim they are adjustable to different
environments, it is always an impossible mission when using the systems to a domain out of their
ranges. For example, no one ever believes Java EE based enterprise systems could be used to take
the load of sharing online videos. If a conventional Web server, like Tomcat [12], could work
efficiently to transmit high-volume data, why some browser plug-in systems, like FlashGet [27],
exist? Although online Web chatting systems were popular in 1990s, they were completely
replaced by instant messaging [28]. Even though instant messaging systems dominate the market
of lightweight information exchanging, they are almost beat by socialized systems, like Twitter
[29], eventually. Therefore, once if a framework is designed, its adaptation is usually constrained
tightly within its domain. In brief, mature frameworks can hardly adapt to the updates in their
preferred computing environment.
Second, even though within the preferred specific domain for their designs, their adaptability is
limited in terms of the obvious differences between configuring and programming. For example,
in Solr, using its configuration files, developers are offered the privilege to customize a
hierarchical distributed structure to achieve the goal of load balancing. Indexed data is transmitted
among the nodes by the protocol of HTTP [30]. Each node in the structure has to pull its parent
node periodically to obtain latest data. However, the drawback is apparent in the case. First,
developers are not free enough to construct an arbitrary topology other than the hierarchical one.
Moreover, only the protocol of HTTP is used between the nodes. The periodically pulling is not
efficient as an eventing and streaming based protocol [10], especially when the timing issue is
critical in a particular application. Because of the limitations of the Solr, developers can do
nothing. Only programming can achieve the goal to upgrade underlying protocols and algorithms.
Mature systems are not adaptable enough to fulfill the requirements. Compared with
programming, the flexibility upon configuring is believed to be extremely limited.

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Third, developers probably rely on those systems such that it is difficult for them to keep and
improve their skills. Using them, developers need only to describe requirements and change
configurations. It can be achieved with few programming experiences. For example, to implement
an e-commerce system with a Java EE application server [13][26], only business logic is required
for developers to write code in the object-oriented model [17]. It is unnecessary for them to worry
about threading [2][3], data transmission [1], synchronizing [6], resource management [4], state
management [6], transaction management [6] and so forth. From the perspective of software
engineering, it lowers developers’ cost and speed up the system development. However, those
developers always lose the indispensable expertise during the procedure. For some beginners,
they even ignore the required knowledge and skills. After a period, it is impossible for them to be
competent with implementing a creative system from scratch.
Fourth, sometimes those mature systems are easily misused and excessively utilized in
inappropriate environments. The problem often happens when developers deploy those systems to
unsuitable domains. Even though they become aware of the potential problems, the convenience
of deploying and configuring attracts them to avoid the troubles of programming. Sometimes
such a misusing might work, but that is usually a high cost and cumbersome solution. For
example, to set up a peer-to-peer (P2P) [10] model in a distributed environment, developers
would rather install Tomcat [12] on each node as the server to receive data and then find a HTTP
client [30] for each of them to send data. The solution works since data can be sent from the client
to the server by the HTTP request and any nodes are able to play the role of a server to receive
remote requests and notifications. However, Tomcat is a huge stuff itself. It contains many other
irrelevant components that are never used in the P2P system, such as JavaServer Pages [31].
Moreover, Tomcat consumes a large portion of resources as a Web based application platform on
a server that has rich computing resources. Thus, it influences the quality of the P2P system,
especially when it is deployed to the devices whose resources are limited. In most time, a P2P
system needs to deal with a large number of heterogeneous computing devices. Most of them lack
computing resources. Therefore, Tomcat is unsuitable to the case. To some extent, the above
solution is not bad although it is far from perfect. The most typical mistake is that developers
would rather use Web servers anywhere for communications. The problem is often seen in the
case that developers lack rich experiences, especially who scares about the TCP programming and
the concurrency programming.
Finally, there are always new domains and novel ideas that are not covered by those mature
systems. A mature system emerges only when the specific application environment becomes
dominant and its primary requirements keep steady for a long period. For example, the so-called
application server is usually suitable to the centralized mission-critical enterprise distributed
environment to support e-commerce. Hadoop is proposed for the high concurrency and large-
scale distributed clusters that primarily deal with high volume read-only data. Fortunately, with
the progress of the computing world, new domains or computing environments always come out.
The mobile Internet is apparently such an instance. Many fancy applications are to be
implemented to accommodate the specific context. For example, when developers are designing
an application to present Web pages on a smart phone, they have to take into account to
accomplish the task with programming rather than simply to embed a browser into their
applications. Thus, many Web sites nowadays design their own clients on smart phones and
encourage users to access their sites using the specially customized clients rather than using
traditional generic browsers. Because of the specific designs, the clients provide users with high
quality accessing experiences. In addition, when new ideas are available about a specific topic,
traditional approaches must be out of date. It is required for developers to keep programming to
implement additional modules and integrate them with others. For instance, when a new routing
algorithm [32] is available for a P2P system [33], it is impossible to exploit it by configuring
existing systems. Developers have to implement the algorithm by programming.

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In brief, although mature software systems are convenient for developers to deal with
requirements in a particular computing environment, for a software developer, it is required to
keep in mind that programming instead of configuring is the only way to raise their ability to deal
with all kinds of difficult problems. In some specific scenarios, programming line-by-line is
mandatory to the success of a system, especially when it is a brand new one in terms of scientific
or business creations.
2.3. Design Patterns
For the problem of programming languages, some senior researchers and engineers propose
design patterns [17][18][19][20][21][22] to assist junior programmers. Design patterns aim to
provide developers with a bunch of mature solutions written in pattern languages [21][22] to
resolve recurring problems in various domains, such as the object-oriented (OO) programming
context [17], the distributed computing [18] and the enterprise systems [18]. The patterns are
proved to be effective. For example, to the issues of OO programming, patterns like creational,
structural and behavioral [17] ones lower developers’ effort to reinvent them themselves through
a long-term practice. Unfortunately, those stuffs retain to be generic in all of the above domains.
Thus, design patterns are not regarded as the final solution that can be reused rapidly to solve the
problems of specific computing environments. Instead, they need to spend high effort to be aware
the contexts where those abstract patterns are suitable. After that, those patterns have to be
transformed into specific code. During the process, programmers are never isolated from the
implementation details. It is still a tough job. In the domain of distributed systems, the burden
becomes severely heavier.
For a particular type of patterns, idioms are defined as the ones written in specific languages. If
so, the effort to program with them must be low. However, a comprehensive solution to the topic
of distributed systems is not available to the best of my knowledge. In the Java world, such a
solution is also almost unavailable. Even though some exists, it is still fine for developers to taste
the solution of GreatFree and make a better choice.
2.4. Programming from Scratch
Most developers who lack rich experiences are scared about programming directly, especially
when implementing a distributed system from scratch. Programming from scratch is designated as
the developing approach through which developers take into account most of the specific issues
of a particular application domain and solve them by coding themselves rather than importing
existing modules from third parties. It seems that the approach violates the convention of software
engineering that encourages developers to reuse mature legacy code and systems. In fact, that is
not the truth all the time.
In the following three cases, programming is still required. First, reusable legacy resources are
unavailable in a specific domain. Second, changes are required to fit a particular situation. Third,
new ideas or new environments emerge such that it is required to come up with a relatively
independent system. To deal with the issues of the above cases, programming becomes the
indispensible skills for developers.
The primary reason that developers dislike programming from scratch is that most languages,
such as Java SE, are generic to various application domains such that it is short of effective
modules, fully grown APIs and compelling patterns to deal with specific issues. Even though
developers grasp all of the details of a language and learn the requirements and solutions of the
domain, it still takes them a high cost to program a high quality system in a specific area directly.

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3. THE INTENTIONS OF GREATFREE
GreatFree is made up with a series of APIs and idioms to overcome the difficulties of
programming large-scale distributed systems. It believes the reusable code is the most convenient
tool to speed up the implementation procedure. For the domain of distributed environment, the
APIs and idioms play the role of building blocks to deal with most common problems. Those
components are neither conceptual models like regular design patterns for developers to refer to
nor black boxes like mature frameworks for configuring and customizing. Rather, the solution of
GreatFree turns out to be constructible and revisable high quality resources for programming. For
that, developers are required to be familiar with the relevant knowledge to come up with a
distributed system rather than to follow a tutorial only and forget about the critical technical
issues. A high quality, creative and flexible system cannot be implemented unless programmers
understand the essences and details. Meanwhile, developers’ effort is still lowered with the APIs
and idioms. Inspired by developers’ knowledge, the components can take the role to program a
system conveniently in terms of their high readability, constructability and changeability.
3.1. Specific Code is the Silver Bullet
Specific code is the silver bullet to speed up the development of a self-contained system
compared with other resources like design patterns and frameworks. Although they cover
common solutions in either the manner of object-oriented programming [17] or even in
distributed environments [18], design patterns belong to conceptual models for references rather
than the ones that can be embedded into an existing system directly for execution. On the other
hand, a mature framework is an established system for customization instead of a self-contained
one in which each detail is visible to developers.
Specific code is the reusable program that is proved to resolve one typical issue in a high quality
state in a particular computing environment. Once if a bunch of such code is available, it is
convenient for developers to either weave them to their own programs following straightforward
patterns or reconstruct them to implement other systems further. During the procedure, developers
are only required to transform the code to their own particular programs in accordance with the
specific code as samples. For the maturity of the specific code, the transforming is similar to a
mapping process rather than the conventional one to learn requirements, propose solutions, design
and test programs. Developers take the task without considering the details of code themselves in
most cases such that the effort is apparently lower than programming from scratch.
3.2. Knowledge of Distributed Systems is Required
GreatFree provides a series of APIs and idioms to assist developers to implement a self-contained
distributed system rapidly. Different from the transparency perspective of the traditional software
engineering, GreatFree developers are required to learn the specific issues of the system they are
implementing. Although it is unnecessary to resolve the problems by programming themselves,
they ought to be aware of what components are needed, where to place them in the program and
what effects the components take. As a common sense, it is unreasonable that developers know
little about the principles of the system they are implementing. As a matter of fact, the
programming environment supported by GreatFree is not a black box to hide details from
programmers. Instead, it is the composing-enabled open source for them to follow and speed up
the distributed system development. Only then, they are able to understand what they are doing
such that it is possible for them to raise the quality of their systems and improve GreatFree
through revising the open source.

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3.3. Changeable APIs and Idioms
GreatFree is an open source development tool that consists of a series of APIs and idioms.
Although it is tested, it does not mean that the exact solutions are not replaceable and changeable.
With the support of the idioms, the overall structure of the system is stable and developer-
friendly. However, it allows programmers to update the internal algorithms, i.e., the APIs,
without ruining the code organization upon GreatFree idioms. For example, developers could
upgrade the issues like resource management, thread management, multicasting approaches,
remote eventing and pulling and so forth. In addition, although the system implemented by
GreatFree is scalable, it does not guarantee that it fits in all of the heterogeneous distributed
environments. It encourages developers to follow the current version and make changes for their
own requirements in either the APIs or the idioms.
4. THE GREAT FREE APIS
GreatFree is comprised of two portions, the APIs and the design patterns as idioms. GreatFree
APIs attempt to provide developers with numerous mature encapsulated programs to resolve most
distributed problems. The portion of GreatFree APIs covers five areas, including remote
interactions, resource reusing, concurrency implementation and control, multicasting and some
other utilities. It helps developers construct their systems without taking care about the details of
each solution.
4.1. Remote Interactions
The APIs for remote interactions aim to support developers to accomplish four types of remote
interactions tasks, including establishing remote connections, sending synchronous/asynchronous
notifications, performing remote reading and managing remote IO resources. The APIs for remote
interactions consist of 15 classes, as shown in Table 1.
Table 1. Great Free Remote Interactions.
API Description
AsyncRemoteEven
ter
This class aims to send notifications to a remote server asynchronously
without waiting for responses. The sending methods are nonblocking.
Eventer This is a thread derived from NotificationObjectQueue. It keeps working
until no objects are available in the queue. The thread keeps alive unless it
is shutdown by a manager outside.
EventerIdleChecke
r
The class works with AsyncRemoteEventer to check whether an instance
of Eventer is idle long enough so that it should be disposed.
FreeClient This is a TCP client that encloses some details of TCP APIs such that it is
convenient for developers to interact with remote servers. Moreover, the
client is upgraded to fit the caching management.
FreeClientCreator The class contains the method to create an instance of FreeClient by its IP
address and port number. It extends the interface of Creatable and it is
used as the resource creator in the RetrievablePool.
FreeClientDisposer The class implements the interface of Disposable and aims to invoke the
dispose method of an instance of FreeClient to collect the resource. It is
used a resource disposer in RetrievablePool.
FreeClientPool The pool, RetrievablePool, is mainly used to manage the resource of
FreeClient. Some problems exist when instances of FreeClient are
exposed outside since they might be disposed inside in the pool. It is a

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better solution to wrap the instances of FreeClient and the management on
them. The stuffs should be invisible to outside. For that, a new pool,
FreeClientPool, is proposed.
IPNotification This is an object to contain the instance of IPPort and the message to be
sent to it. It is used by the class of Eventer in most time.
IPPort The class consists of all of the values to create an instance of FreeClient.
For example, it is the source that is used to initialize resources in
RetrievablePool.
OutMessageStream The class consists of the output stream that responds a client. The lock is
used to keep responding operations atomic. The request is any message
that extends ServerMessage.
RemoteReader The class is responsible for sending a request to the remote end, waiting
for the response and returning it to the local end which sends the request.
ServerIO The class encloses all the IO required details to receive and respond a
client's requests.
ServerIORegistry The class is used to keep all of the ServerIOs, which are assigned to each
client for the interactions between the server and the corresponding client.
This is a management approach for those instances of ServerIOs.
ServerListener The class acts as the listener to wait for a client's connection. To be more
efficient, it involves a thread pool and the concurrency control mechanism
in its internal mechanism.
SyncRemoteEvent
er
The eventer sends notifications to remote servers in a synchronous
manner without waiting for responses. The sending methods are blocking.
4.2. Resource Reusing
Resource reusing is a critical topic since a distributed system consists of some critical resources to
be saved. In Java SE, it claims that memory is maintained by the underlying platform. However,
other resources have to be taken care by developers themselves. GreatFree includes a bunch of
APIs in 16 classes to ease the task, as listed in Table 2. They are roughly divided into three
categories, i.e., pooling, caching and relevant interfaces.
Table 2. GreatFree Resource Reusing.
API Description
Creatable This is an interface to define a resource creator that initiates an
instance of the resource in the resource pool, such as
RetrievablePool. The resource derives from FreeObject.
Disposable The interface defines a method for the disposer that collects the
resource in the resource pool, such as RetrievablePool. The resource
derives from FreeObject.
FreeReaderIdleChecker The class is used to call back the method of checkIdle of the instance
of FreeReaderPool.
FreeReaderPool This class is similar to RetrievablePool. In fact, it is a specific
version of RetrievablePool. First, the resource managed by the pool
is FreeClient. Second, it aims to initialize instances of FreeClient for
not only output but also for input.
HashCreatable The interface defines the method to create instances that extend
HashFreeObject.
HashDisposable The interface defines the method to dispose the objects that are
derived from HashFreeObject.
IdleChecker The idle checker works with the ResourcePool to check the idle

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states of resources.
MulticastMessageDispo
ser
This is an implementation of the MessageBindable interface. It is
usually used by threads that share the same multicast messages.
QueuedIdleChecker The class aims to check periodically whether a resource is idle for a
long enough period. If so, the resource needs to be disposed.
QueuedPool The pool aims to manage resources that are scheduled by their idle
lengths. The one that is idle longer has the higher probability to be
reused than the one that is idle for a shorter period.
ResourceCache This class is a cache to save the resources that are used in a high
probability. It is designed since the total amount of data is too large
to be loaded into the memory. Therefore, only the ones that are used
frequently are loaded into the cache. It is possible that some loaded
ones are obsolete. It is necessary to load new ones that are used
frequently into the cached and save the ones that are out of date into
the database or the file system persistently.
ResourcePool The pool aims to manage resources that are scheduled by their idle
lengths. The one that is idle longer has the higher probability to be
reused than the one that is idle for a shorter period. Different from
QueuedPool, this pool does not care about the type of resources. It
assumes that all of resources in the pool are classified in the same
type.
RetrievableIdleChecker The class runs periodically to check whether a resource being
managed in a resource pool, such as RetrievablePool, is idle enough
time. If so, it collects the resource.
RetrievablePool The class is a resource pool that aims to utilize the resources
sufficiently with a lower cost. The pool is usually used for the
resource of FreeClient. For each remote end, multiple FreeClients are
initialized and managed by the pool. When it is necessary to interact
with one remote end, it is convenient to obtain a FreeClient by the
key or the initial values, i.e., the IP address and the port, which
represent the remote end uniquely. That is why the pool is named the
RetrievablePool.
4.3. Concurrency Implementation and Control
The issue of concurrency implementation and control is particularly critical to a distributed
system since it is required to deal with potential accessing from a huge number of users and other
remote clients. GreatFree contains rich solutions to the problem. 35 classes are designed to
provide developers with a convenient environment to program a high concurrency and low cost
distributed system. Table 3 lists the APIs and simple descriptions of them.
To implement the concurrency, two primary situations need to be dealt with. The first one is to
receive concurrent notifications and the second one is to receive concurrent requests and then
generate responses concurrently. In details, each of them needs to take into account the issue of
multicasting. That is, the notifications and requests/responses are performed within a large-scale
environment, which consists of numerous nodes, rather than between two nodes. It proposes a
couple of threading queues that encloses incoming messages and dispatchers to manage those
threads for specific cases.
For the issue of concurrency control, first of all, it is embedded into the concurrency APIs.
Moreover, a new class, Collaborator, which encloses locking and waiting/notifying mechanisms
of Java SE, is proposed for developers to use conveniently.

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Table 3. GreatFree Concurrency Implementation and Control.
API Description
AnycastRequestDispatcher This is a class that enqueues requests and creates anycast
queue threads to respond concurrently. If the current host does
not contain the requested data, it is necessary to forward the
request to the host's children.
AnycastRequestQueue This is a thread that possesses a request queue and other
remote communication resources. It is the base one to support
implementing anycast requests in a concurrent way.
AnycastRequestThreadCreata
ble
This is an interface to define the method to create a thread for
processing anycast requests concurrently.
BoundBroadcastRequestDispa
tcher
This is a dispatcher to manage broadcast request threads that
need to share requests. The threads must be synchronized by a
binder.
BoundBroadcastRequestQueu
e
When processing broadcast requests, no matter whether the
current host contains the matched data, it is required to
forward the request to its children. That is the difference
between the anycast and the broadcast. However, it is
suggested that the retrieval and data forwarding can be done
concurrently and they do not affect with one another. The
thread is designed for the goal since they are synchronized
once after both of them finish their critical tasks. Therefore,
they do not affect each other.
BoundBroadcastRequestThrea
dCreatable
The interface defines the method to create the bound broadcast
request thread.
BoundNotificationDispatcher This is a dispatcher to manage threads that need to share
notifications.
BoundNotificationQueue The thread is different from NotificationQueue in the sense
that it deals with the case when a notification is shared by
multiple threads rather than just one. Therefore, it is necessary
to implement a synchronization mechanism among those
threads.
BoundNotificationThreadCrea
table
The interface defines the method to create the instance of
BoundNotificationQueue. It is managed by
BoundNotificationDispatcher.
BroadcastRequestDispatcher This is a class that enqueues requests and creates broadcast
queue threads to respond them concurrently. If the current host
does not contain the requested data, it is necessary to forward
the request to the host's children.
BroadcastRequestQueue This is a thread that possesses a request queue and other
remote communication resources. It is the base one to support
implementing broadcast requests in a concurrent way.
BroadcastRequestThreadCreat
able
This is an interface to define the method to create a thread for
processing broadcast requests concurrently.
CheckIdleable This is an interface to define the signatures of two methods for
a thread's idle checking.
Collaborator The class encloses locking and notify/wait APIs to help
developers control concurrency.
Consumable The interface defines the method for a consumer in the
producer/consumer pattern.
ConsumerThread This is an implementation of the pattern of

12
producer/consumer.
Dispatchable It defines some interfaces that are needed in
ServerMessageDispatcher.
Interactable The interface defines a few method signatures for the
interaction between a caller and a callee in a concurrent
environment. The caller notifies the callee by calling the
methods provided by the callee such that the callee can
respond to the caller. The caller does that when its running
circumstance is changed in a certain situation.
InteractiveDispatcher This is a task dispatcher that schedules tasks to the special type
of threads derived from InteractiveQueue. For the distinct
designs, instances of managed threads can interact with the
dispatcher for high quality management.
InteractiveQueue This is the base class that must be derived to implement a
thread that holds the methods that can be called to notify the
interactive dispatcher.
InteractiveThreadCreatable In general, a pool needs to have the ability to create instances
of managed resources. The Creatable interface is responsible
for that. The interface defines the method to create instances of
InteractiveThread, which is derived from InteractiveQueue.
MapReduce This is a high concurrency processing class. Numerous threads
that take task queues are the input of the class. It is able to
execute those threads concurrently and merge the results from
them together.
MapReduceQueue This is a thread to implement the mechanism of map/reduce.
MessageBindable Some behaviors, such as disposing, on the messages must be
synchronized among threads. If no synchronization, it is
possible that a message is disposed while it is consumed in
another one. The interface defines the relevant method
signatures.
MessageProducer This is a producer/consumer pattern class to input received
messages into a concurrency mechanism, the server
dispatcher, smoothly.
NotificationDispatcher This is a class that enqueues notifications and creates threads
to process them concurrently. It works in the way like a
dispatcher. That is why it is named.
NotificationObjectQueue This is a fundamental thread that receives and processes
notifications as an object concurrently. Notifications are put
into a queue and prepare for further processing. It must be
derived by sub classes to process specific notifications.
NotificationQueue This is a fundamental thread that receives and processes
notifications in the form of messages concurrently.
Notifications are put into a queue and prepare for further
processing. It must be derived by sub classes to process
specific notifications.
NotificationThreadCreatable This is an interface defines the method signature to create the
instances of NotificationQueue.
RequestDispatcher This is a class that enqueues requests and creates threads to
respond them concurrently. It works in the way like a
dispatcher. That is why it is named.
RequestQueue This is a thread that receives requests from a client, puts those
messages into a queue and prepares for further processing. It

13
must be derived by sub classes to provide the real responses
for the requests.
RequestThreadCreatable This is the interface to define a method signature that creates a
thread to respond users' requests.
Runner This is a class to simplify the procedure to invoke a single
thread that implements the interface of Runnable of Java SE.
ServerMessageDispatcher This is the base of a server message dispatcher. All of the
messages sent to the server are dispatched by the class
concurrently.
Threader This is a class to simplify the procedure to invoke a single
thread that is derived from the class of Thread of Java SE.
ThreadIdleChecker This is a callback thread that runs periodically to call the idle
checking method of the thread being monitored.
4.4. GreatFree Multicasting
To implement a large-scale distributed system, it is indispensible to employ high efficient
multicasting mechanisms. GreatFree provides developers with 23 classes to achieve the goals.
Table 4 lists all of the APIs. In the current status, GreatFree supports a tree-based multicasting.
All of the nodes are organized as a tree to transmit data as messages or objects. The multicasting
is divided into the notification one and the request/response one. For the later case, it is further
categorized into the one of broadcast request/response and the one of anycast request/response.
Table 4. GreatFree Concurrency Implementation and Control.
API Description
AnycastRequest The request is a multicast one that is sent to all of the nodes in a
cluster. However, once if one node at least responds the request
positively, the multicast requesting is terminated. That is the
difference from the broadcast requesting.
AnycastResponse The message is an anycast response to be responded to the
initial requester after retrieving the required data.
BroadcastRequest The message is a broadcast request to be sent through all of the
distributed nodes to retrieve required data. For multicasting is
required, it extends ServerMulticastMessage.
BroadcastResponse The message is a broadcast response to be responded to the
initial requester after retrieving the required data.
ChildMessageCreatable The interface defines the method that returns the message
creator to generate multicast messages to children nodes in the
multicasting topology. It is used to define the instance of
children multicastor source.
ChildMulticastMessageCreat
able
The interface defines the method to create a multicast message
on a child node rather than the root one.
ChildMulticastor This is the multicasting class to run on a child node in the
multicasting topology.
ChildMulticastorSource The class contains all of the initial values to create an instance
of ChildMulticastor. That is why it is named
ChildMulticastorSource. It is used by the resource pool to
manage resources efficiently.
ObjectMulticastCreatable The interface defines the methods to create multicast messages
to be sent.
RootAnycastReaderSource This class assists the resource pool to create instances of anycast
readers. Thus, it contains all of required arguments to do that.

14
RootAnycastRequestCreatab
le
The interface defines the methods to create requests in the
anycastor.
RootAnyRequestCreatable The interface returns an instance of the anycast request creator.
It is employed by the instance of RootAnycastReaderSource to
provide the method for the resource pool to manage anycastors.
RootBroadcastReaderSource This class provides the resource pool with initial values to create
instances of broadcast readers. That is, it is a class that contains
all of required arguments to do that.
RootBroadcastRequestCreat
able
The interface defines the methods to create requests in the
broadcast requestor.
RootBroadRequestCreatable The interface returns the broadcast request creator. It is
employed by the instance of RootBroadcastReaderSource to
provide the method for the resource pool to manage
broadcastors.
RootMessageCreatable The interface defines the method that returns the message
creator to generate multicast messages. It is used to define the
instance of multicastor source.
RootMulticastorSource The class contains all of the initial values that are required to
create an instance of RootObjectMulticastor. The source is
needed in the multicastor pool.
RootObjectMulticastor The code is a core component to achieve the multicasting
among a bunch of nodes. The nodes are usually organized into a
particular topology to raise the multicast efficiency. In the case,
a tree is constructed for the nodes. For different situations, a
more appropriate topology can be selected by programmers.
RootRequestAnycastor This class is the implementation to send an anycast request to all
of the nodes in a particular cluster to retrieve data on each of
them. It is also required to collect the results and then form a
response to return the root. However, only one response is good
enough for anycast.
RootRequestBroadcastor This class is the implementation to send a broadcast request to
all of the nodes in a particular cluster to retrieve data on each of
them. It is also required to collect the results and then form a
response to return the root.
ServerMessage The class is the base for all messages transmitted between
remote clients/servers.
ServerMulticastMessage This is the base class to implement the message that can be
multicast among a bunch of nodes.
Tree It aims to construct a tree to raise the quality of multicasting.
The tree in the case is simple, such as each node having an equal
number of children. It is acceptable when all of the nodes have
the similar computing capacity and most of them run within a
stable computing environment. For a heterogeneous
environment, a more complicated tree or other topologies must
be applied.
4.4. GreatFree Utilities
GreatFree provides some utilities to assist the programming for sorting, file input/output, timing,
XML and so forth. Although those APIs do not contribute to the distributed programming
directly, it is useful in their specific cases, as shown in Table 5.

15
Table 5. GreatFree Utilities.
API Description
CollectionSorter The class aims to sort a collection, a list or a map, in the ascending or
descending manner. It is also able to select the maximum or minimum value
from the collection.
FileManager The class provides some fundamental file operations based on the API from
File of JDK.
FreeObject The class is designed in the system to fit object reusing, caching and so on.
HashFreeObject This is another general object that defines some fundamental information
that is required to manage in a pool. Different from the one, FreeObject, this
object is managed by the hash key rather than the object key.
NodeID This singleton is used to save a node's unique ID only.
NullObject The class represents nothing. It is used when an object needs to fill the
placeholder of generics, but it does not matter what should be put there.
Prompts It contains prompting messages on screen.
Rand This code is used to generate different types of random number in integer,
float and double by enclosing the one, Random, in JDK.
StringObj This is an object that can be compared by its key in String.
Symbols The class defines some frequently-used symbols.
TerminateSignal The class is a flag that represents whether the node process is set to be
terminated or not. For some long running threads, they can check the flag to
stop their tasks immediately.
Time The class consists of some common constants and methods to process timing
values.
Tools The class contains some methods that provide other classes with some
generic services.
UtilConfig The class keeps relevant configurations and constants of the solution.
5. THE GREATFREE IDIOMS
Another portion of GreatFree is the design patterns as idioms. The design patterns help
developers compose those APIs together in a certain structure to handle specific problems. In
essence, they are regarded as the sample code for developers to follow. Because of the maturity of
idioms, programming distributed systems become a lower cost task than doing that from scratch.
5.1. Terminator
The idiom of Terminator, which is implemented by a singleton, encloses a flag that represents
whether an entire process is terminated or not. It is usually embedded into a long running thread,
which detects the flag within a loop or periodically. When the flag is set to the state of being
terminated, the thread is ended before the next step of the loop.
At the entry of the entire process, a while loop is designed in the way similar to the above
manner. The loop does nothing but sleeps for a certain period in each step. The loop exits after the
flag is set to the state of being terminated. The primary API the idiom uses is the TerminateSignal
in the GreatFree utility.

16
5.2. Coordinator
For a large-scale distributed system, a centralized Coordinator is required to manage the entire
system for the system initialization, the resource registry, the task and memory distribution, the
message bus, the resource disposal and so forth. It is also implemented in a singleton. It contains
two primary methods, start() and stop(). Thus, the coordinator is the entry and the exit of a
distributed node.
5.3. Server Listener
The idiom of Server Listener is a component that plays the role the remote connection listener as
the TCP (Transmission Control Protocol) [1] and accomplishes relevant initialization tasks. For
the various goals, it contains the TCP ServerSocket [1], an instance of ThreadPool, ServerIO and
its registry, ServerIORegistry. The ServerSocket is used to accept remote TCP connections. When
a new connection is constructed and the upper limit is not reached, an instance of ServerIO is
created and kept in the registry. Then, the ThreadPool starts a thread to run the newly created
ServerIO such that the remote client is served with a concurrency mechanism. In addition, if a
peer-to-peer distributed model needs to be implemented, a remote client pool, FreeClientPool, is
also taken into account to put in the listener. It needs to initialize an instance of FreeClient for the
incoming remote node. For a large-scale distributed system, a peer-to-peer architecture is flexible
in terms of implementing highly efficient interactions. Incidentally, each listener maps to one
particular port number uniquely. To raise the performance, multiple threads are required to
monitor the port. It is initialized and disposed in the Coordinator.
5.4. Remote Eventer
The idiom of Remote Eventer is responsible for sending notifications to a remote node at any
moment. As a general eventing mechanism, the notification processes are performed in either a
synchronous or an asynchronous manner such that both of the APIs, SyncRemoteEventer and
AsyncRemoteEventer, need to be enclosed. In a specific application, all of the eventers are
collected in a singleton for convenience. An instance of ThreadPool is also required in the
singleton to initialize the asynchronous eventers. It is initialized and disposed in the Coordinator.
5.5. Remote Reader
The idiom of Remote Reader supports another manner to interact with a remote node. It sends a
request and waits until a response is received. As a generic module, a singleton API,
RemoteReader, is implemented in GreatFree APIs to accomplish the task. For a specific
distributed node, it is suggested to enclose all of requests and their calls into a singleton for
convenience. It is initialized and disposed in the Coordinator.
5.6. Server IO
Each distributed node needs to derive the API of ServerIO to establish the fundamental
input/output streams as a server. A while-loop is needed in the derived idiom and the loop is
exited until the remote node closes the connection. In the loop, remote messages are received and
then they are assembled with the output stream and the locking for further processing. In the
procedure, it employs the API of OutMessageStream.

17
5.7. Message Producer
The idiom of Message Producer is responsible for delivering messages to the idiom of Server
Message Dispatcher for concurrently processing. It is derived from general class,
MessageProducer, in GreatFree APIs. Additionally, it needs to be implemented with an instance
of ServerMessageDispatcher. To keep concurrent, an instance of Threader is also needed to start
the idiom. As required by the Threader, a disposer is defined for the instance of
ServerMessageDispatcher. For any distributed applications, only one message producer is needed
to dispatch received message for further processing. Thus, a singleton is required to wrap the
instance of MessageProducer. When a message is received by the Server IO, it is forwarded to the
Message Producer after being assembled with the output stream and the locking using
OutMessageStream. The idiom is initialized and disposed in the Coordinator.
5.8. Server Message Dispatcher
Derived from the API, ServerMessageDispatcher, the idiom of Server Message Dispatcher
consists of all of the thread management pools to deal with incoming messages concurrently.
According to the types of those messages, the thread management pools are divided into different
groups for notifications, requests, multicast notifications, anycast requests, broadcast requests and
so forth. For each type of messages, it is possible to define some exactly specific sub types of
messages, such as the request for sign-in or the notification for online. If the sub types of
incoming messages are rich, it is allowed to define multiple Server Message Dispatchers and
initialized in the Message Producer. Furthermore, it needs to create a specific thread management
idiom for each sub type message. All of them are enclosed, initialized and shutdown in one
particular Server Message Dispatcher. The typical structure for the idiom is a switch statement
that dispatches incoming messages to each of the thread management idiom according to the
message identifications.
5.9. Notification Queue
The idiom of Notification Queue is derived from the API, NotificationQueue, with a specific
notification message. In the idiom, one two-level nested while-loop is needed to enclose the
operations, which are executed immediately after receiving one notification. The outer while-loop
detects whether the thread is shutdown. The inner while-loop detects whether the queue to keep
notifications in the idiom is empty or not. If the queue is empty, the thread does not terminate
immediately. It needs to be waiting for a period such that it avoids high CPU usages for the long-
running loop and it also reduces the cost to create a thread when new notifications are received.
Usually, it is a high cost solution to collect the thread immediately when the queue is empty. To
save memory, it is also suggested to dispose notifications after it is processed. For that,
developers are required to invoke the disposing method.
5.10. Notification Object Queue
The idiom of Notification Object Queue is derived from the API, NotificationObjectQueue. Its
structure is identical to that of the Notification Queue. The only difference is that data in the
queue is derived from the Java base class, object, whereas the queue in the Notification Queue
keeps data derived from ServerMessage of GreatFree. For that, the Notification Object Queue is
usually not used as remote concurrent processing unless the object implements the interface of
serializable of Java.

18
5.11. Bound Notification Queue
When a notification needs to be handled by multiple threads concurrently after it is received, it
needs to use the idiom of Bound Notification Queue to define them if the notification is probably
updated in any one of them. The idiom is derived from the API, BoundNotificationQueue, in
which an interface, MessageBindable, should be implemented to enclose the operations to be
synchronized.
5.12. Request Queue
As a child of the API, RequestQueue, the idiom of Request Queue has the similar structure to that
of Notification Queue, such as the two-level nested while-loop, the message queue, the waiting
control to save resources and the message disposing methods. Besides those, a responding method
needs to be invoked in the inner loop immediately after the response is created.
5.13. Bound Broadcast Request Queue
The idiom of Bound Broadcast Request Queue is another case to send a request and receive a
response from a remote system that is made up with a numerous distributed nodes. It is derived
from the API, BoundBroadcastRequestQueue. Different from that of the Request Queue, the
request is transmitted to each node in the system rather than to a single one. Additionally, since
the request is handled by two concurrent threads, i.e., one for generating the response and another
for forwarding it continually, and both of them might change it in some cases if applicable, such
as disposing the request, it is necessary to synchronize the relevant operations. That is why the
idiom is named Bound. The synchronized operations are enclosed in the unique instance of a class
implementing the interface of MessageBindable.
5.14. Anycast Request Queue
Different from that of Bound Broadcast Request Queue, it is not required to get responses from
each of them when a request is sent to a large number of potential distributed nodes. If anyone of
them responds, the entire procedure is terminated. Derived from the API, AnycastRequestQueue,
the idiom of Anycast Request Queue takes the task. It notes that it is impossible that the request is
handled by the two operations concurrently, i.e., the one forwarding it and the one responding it.
Because the forwarding is probably terminated in anycast, it is not necessary to synchronize the
above two threads since the two operations are performed in a sequential order in the idiom. Thus,
the idiom is not named as Bound as what it does on that of Bound Broadcast Request Queue.
5.15. Root Multicastor
Being situated at the coordinator node of a distributed system, the Root Multicastor is an idiom
for multicasting in the system that consists of numerous distributed nodes. By default, the
multicasting is performed within a tree topology and the coordinator plays the role of the root. It
is a singleton that includes a bunch of multicastor resource pools, which are implemented by the
API of ResourcePool. Each of the pools is able to create instances of specific multicastors derived
from RootObjectMulticastor. They send data to all of the nodes in the tree. Each of the methods
of the Root Multicastor contains four components, i.e., data to be transmitted, the initialization of
a multicastor, the transmission invocation and the collection of the multicastor for reuse. The
idiom is responsible for sending notifications only.

19
5.16. Child Multicastor
The partner of the Root Multicastor is the Child Multicastor, which is located at the children
nodes of the multicasting tree. Each of the child multicastors is derived from ChildMulticastor to
send data its immediate children to keep the multicasting go ahead. Except that, the structure of
the Child Multicastor is identical to the root one. The multicastors are also managed by the
instances of ResourcePool.
5.17. Root Anycast Eventer
Sometimes it is necessary to send notifications to some of the nodes in a system instead of all of
them. In this case, the Root Anycast Eventer takes the responsibility. It is also situated at the root
of the multicasting tree. There are no any differences from the structure of the Root Multicastor.
The RootObjectMulticastor is the parent API of each multicastor. It only needs to send
notifications to its immediate children.
5.18. Child Anycast Eventer
As the partner of the Root Anycast Eventer, the Child Anycast Eventer keeps sending
notifications to its immediate children. But the operation is terminated if the notification is needed
by the local node. The judgment is carried out by the idiom of Notification Queue, which receives
the notification. The structure of the idiom is identical to that of the Child Multicastor. Each
multicastor is also derived from ChildMulticastor and managed by the instances of ResourcePool.
5.19. Root Broadcast Reader
Sometimes, it is required to query all of the distributed nodes in a system. In the case, the idiom
of Root Broadcast Reader is competent with the task. Different from those in the Root
Multicastor, the multicastors are derived from RootRequestBroadcastor. Besides the multicastor
pools implemented by ResourcePool, the idiom needs to implement a mechanism to collect the
responses. That is, until the number of responses reaches to that of the total of the distributed
nodes, the method of the broadcast requesting is blocked.
5.20. Child Broadcast Reader
As the partner of the Root Broadcast Reader, the Child Broadcast Reader is easier to be
implemented since it is unnecessary to take into account any additional issues. Its primary task is
the same as the Child Multicastor. The change happens in the relevant threads, i.e., the Bound
Notification Queue and the Bound Broadcast Request Queue, to process the broadcast requests.
The first queue forwards the requests to its immediate children and the second one creates the
relevant response to send it back to the root.
5.21. Root Anycast Reader
Different from the Root Broadcast Reader, another multicast requesting is accomplished by the
idiom of Root Anycast Reader if one response at least is received by the root. It is unnecessary to
send the request to all of the nodes in the distributed environment if one response at least is
received. Similar to the Root Broadcast Reader, the idiom needs to implement a response
collecting mechanism besides the multicastor pools. But it only needs to obtain one response and
then returns it to the requestor instead of waiting for all of the requests from each node in the
system.

20
5.22. Child Anycast Reader
The partner of the Root Anycast Reader is the Child Anycast Reader. The request from the root
needs to be forwarded to the children of the current local node if the local node cannot generate
the required response. The implementation of the idiom is identical to the Child Multicastor. The
difference happens in the threads that invoke the methods of the idiom. In the case, the
corresponding thread idiom is the Anycast Request Queue.
5.23. Interactive Queue
Different from traditional threads, the idiom of Interactive Queue contains a number of callback
methods such that the running threads are able to interact with the pool that manages them. The
interactions are realized through invoking the callbacks. According to the interactions, it is
possible for the pool to notice the status of those threads such that it can make a proper decision
to manage those threads effectively and efficiently.
5.24. Map Reducer
The idiom of Map Reducer is an important idiom to implement the high concurrency mechanism.
When a large number of parallel tasks are available to be processed and their results need to be
merged, it is highly efficient to put them into the Map Reducer if they do not need to synchronize
during the procedure to accomplish each of them. The Map Reducer is made up with the pools to
manage the resources of MapReduce. Each of its public methods takes numerous tasks as input
and the merged results as return values. Inside the methods, it needs to specify the reducer,
initialize the algorithm object and invoke the concurrent mapping and reducing.
6. THE EXPERIMENTAL ENVIRONMENT
GreatFree APIs and idioms were proposed during the procedure to implement a new
infrastructure of World Wide Web [11][2]. It believes the social capital [36] is the driver to
associate the human capital [37] over the Web. For the understanding, it needs to take a heavy
load to program from scratch, including the issues of routing, multicasting, persisting, presenting
and so forth. It attempts to build a brand new large-scale heterogeneous information system such
that users are able to perform various information accessing behaviors, such as publishing,
forwarding, commenting, browsing, navigating, searching and following.
The current version is implemented with Java SE 7. It reaches the total lines of code, 373,181, at
the server side. In addition, its client side has 11,662 lines of code, which are implemented on
Android 5.0. All of APIs and idioms were proposed during the procedure to implement the server
side.
The server side is made up with the coordinator, the crawlers, the data rankers, the publisher
rankers, the memory nodes and the interactive nodes. In the current version, except that the
coordinator is implemented with a single computer, other types nodes are comprised of multiple
computers without the upper limit. As the center of the system, the coordinator is responsible for
integrating all of the nodes together, such as distributing tasks, disseminating data, forwarding
queries and so forth. Each of the crawlers receives tasks from the coordinator, collects Web pages
from the Web and then injects them into the system for sharing in a concurrent manner. The data
rankers evaluate and sort data in a certain order such that it can be presented to users in a high
quality form. The node rankers are responsible for sorting publishers of data and the
consequences are useful to construct the graphs between publishing organizations and
individuals. The memory nodes achieve the goal to keep important data in memory as cache and

21
the rest data on disk for persistence with a distributed cluster. The interactive nodes connect with
users’ devices directly and respond to users’ requests. Each of the above nodes is implemented
from scratch with Java SE initially for the proposals of new distributed models and data
transmission protocols. For that, the relevant APIs and idioms are proposed after a long-term
accumulation. Nowadays the backend of the system is accomplished, but its clients are still under
construction. It will be launched as a commercial system in one or two years.
7. FUTURE WORK
As a comprehensive solution to the large-scale distributed system, it can hardly claim the APIs
and idioms discussed in the paper cover all of the issues. With the progress of the development of
the system, more APIs and idioms must be put forward. In accordance with the domain of the
social computing, it is expected to invent some patterns that fit in the unstable environment. In
addition, it needs to improve the thread management approaches in the current version since it
notes that the number of raised threads is high. One reason for that is due to that each dispatcher
has a parent thread that is always alive. For such dispatchers are numerously utilized, it leads to
the growth of the number of threads. It can be solved through the lazy initialization and the parent
thread is killed after a certain period. Finally, all of the algorithms in each APIs and idioms need
to be improved. For the limited time, the algorithms are still rough. For example, the caching and
pooling algorithms are to be optimized. Potential developers are able to strengthen those
algorithms with the open source.
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