1.1 ABSTRACT:
Characterized by the increasing arrival rate of live content, the emergency applications pose a great challenge: how to disseminate large-scale live content to interested users in a scalable and reliable manner. The publish/subscribe (pub/sub) model is widely used for data dissemination because of its capacity of seamlessly expanding the system to massive size. However, most event matching services of existing pub/sub systems either lead to low matching throughput when matching a large number of skewed subscriptions, or interrupt dissemination when a large number of servers fail. The cloud computing provides great opportunities for the requirements of complex computing and reliable communication.
In this paper, we propose SREM, a scalable and reliable event matching service for content-based pub/sub systems in cloud computing environment. To achieve low routing latency and reliable links among servers, we propose a distributed overlay Skip Cloud to organize servers of SREM. Through a hybrid space partitioning technique HPartition, large-scale skewed subscriptions are mapped into multiple subspaces, which ensures high matching throughput and provides multiple candidate servers for each event.
Moreover, a series of dynamics
maintenance mechanisms are extensively studied. To evaluate the performance of
SREM, 64 servers are deployed and millions of live content items are tested in
a Cloud Stack testbed. Under various parameter settings, the experimental
results demonstrate that the traffic overhead of routing events in SkipCloud is
at least 60 percent smaller than in Chord overlay, the matching rate in SREM is
at least 3.7 times and at most 40.4 times larger than the single-dimensional
partitioning technique of BlueDove. Besides, SREM enables the event loss rate
to drop back to 0 in tens of seconds even if a large number of servers fail
simultaneously.
1.2 INTRODUCTION
Because of the importance in helping users to make realtime decisions, data dissemination has become dramatically significant in many large-scale emergency applications, such as earthquake monitoring, disaster weather warning and status update in social networks. Recently, data dissemination in these emergency applications presents a number of fresh trends. One is the rapid growth of live content. For instance, Facebook users publish over 600,000 pieces of content and Twitter users send over 100,000 tweets on average per minute. The other is the highly dynamic network environment. For instance, the measurement studies indicate that most users’ sessions in social networks only last several minutes. In emergency scenarios, the sudden disasters like earthquake or bad weather may lead to the failure of a large number of users instantaneously.
These characteristics require the data dissemination system to be scalable and reliable. Firstly, the system must be scalable to support the large amount of live content. The key is to offer a scalable event matching service to filter out irrelevant users. Otherwise, the content may have to traverse a large number of uninterested users before they reach interested users. Secondly, with the dynamic network environment, it’s quite necessary to provide reliable schemes to keep continuous data dissemination capacity. Otherwise, the system interruption may cause the live content becomes obsolete content. Driven by these requirements, publish/subscribe (pub/ sub) pattern is widely used to disseminate data due to its flexibility, scalability, and efficient support of complex event processing. In pub/sub systems (pub/subs), a receiver (subscriber) registers its interest in the form of a subscription. Events are published by senders to the pub/ sub system.
The system matches events against subscriptions and disseminates them to interested subscribers.
In traditional data dissemination applications, the live content are generated by publishers at a low speed, which makes many pub/subs adopt the multi-hop routing techniques to disseminate events. A large body of broker-based pub/subs forward events and subscriptions through organizing nodes into diverse distributed overlays, such as treebased design cluster-based design and DHT-based design. However, the multihop routing techniques in these broker-based systems lead to a low matching throughput, which is inadequate to apply to current high arrival rate of live content.
Recently, cloud computing provides great opportunities for the applications of complex computing and high speed communication where the servers are connected by high speed networks, and have powerful computing and storage capacities. A number of pub/sub services based on the cloud computing environment have been proposed, such as Move BlueDove and SEMAS. However, most of them can not completely meet the requirements of both scalability and reliability when matching large-scale live content under highly dynamic environments.
This mainly stems from the following facts:
1) Most of them are inappropriate to the matching of live content with high data dimensionality due to the limitation of their subscription space partitioning techniques, which bring either low matching throughput or high memory overhead.
2) These systems adopt the one-hop lookup technique among servers to reduce routing latency. In spite of its high efficiency, it requires each dispatching server to have the same view of matching servers. Otherwise, the subscriptions or events may be assigned to the wrong matching server, which brings the availability problem in the face of current joining or crash of matching servers. A number of schemes can be used to keep the consistent view, like periodically sending heartbeat messages to dispatching servers or exchanging messages among matching servers. However, these extra schemes may bring a large traffic overhead or the interruption of event matching service.
1.3 LITRATURE SURVEY
RELIABLE AND HIGHLY AVAILABLE DISTRIBUTED PUBLISH/SUBSCRIBE SERVICE
PUBLICATION: Proc. 28th IEEE Int. Symp. Reliable Distrib. Syst., 2009, pp. 41–50.
AUTHORS: R. S. Kazemzadeh and H.-A Jacobsen
EXPLANATION:
This paper develops
reliable distributed publish/subscribe algorithms with service availability in
the face of concurrent crash failure of up to delta brokers. The reliability of
service in our context refers to per-source in-order and exactly-once delivery
of publications to matching subscribers. To handle failures, brokers maintain
data structures that enable them to reconnect the topology and compute new
forwarding paths on the fly. This enables fast reaction to failures and
improves the system’s availability. Moreover, we present a recovery procedure
that recovering brokers execute in order to re-enter the system, and
synchronize their routing information.
BUILDING A RELIABLE AND HIGH-PERFORMANCE CONTENT-BASED PUBLISH/SUBSCRIBE SYSTEM
PUBLICATION: J. Parallel Distrib. Comput., vol. 73, no. 4, pp. 371–382, 2013.
AUTHORS: Y. Zhao and J. Wu
EXPLANATION:
Provisioning
reliability in a high-performance content-based publish/subscribe system is a
challenging problem. The inherent complexity of content-based routing makes
message loss detection and recovery, and network state recovery extremely
complicated. Existing proposals either try to reduce the complexity of handling
failures in a traditional network architecture, which only partially address
the problem, or rely on robust network architectures that can gracefully
tolerate failures, but perform less efficiently than the traditional
architectures. In this paper, we present a hybrid network architecture for
reliable and high-performance content-based publish/subscribe. Two overlay
networks, a high-performance one with moderate fault tolerance and a highly-robust
one with sufficient performance, work together to guarantee the performance of
normal operations and reliability in the presence of failures. Our design
exploits the fact that, in a high-performance content-based publish/subscribe
system, subscriptions are broadcast to all brokers, to facilitate efficient
backup routing when failures occur, which incurs a minimal overhead. Per-hop
reliability is used to gracefully detect and recover lost messages that are
caused by transit errors. Two backup routing methods based on DHT routing are
proposed. Extensive simulation experiments are conducted. The results
demonstrate the superior performance of our system compared to other
state-of-the-art proposals.
SCALABLE AND ELASTIC EVENT MATCHING FOR ATTRIBUTE-BASED PUBLISH/SUBSCRIBE SYSTEMS
PUBLICATION: Future Gener. Comput. Syst., vol. 36, pp. 102–119, 2013.
AUTHORS: X. Ma, Y. Wang, Q. Qiu, W. Sun, and X. Pei
EXPLANATION:
Due to the sudden change of the
arrival live content rate and the skewness of the large-scale subscriptions,
the rapid growth of emergency applications presents a new challenge to the
current publish/subscribe systems: providing a scalable and elastic event
matching service. However, most existing event matching services cannot adapt
to the sudden change of the arrival live content rate, and generate a
non-uniform distribution of load on the servers because of the skewness of the
large-scale subscriptions. To this end, we propose SEMAS, a scalable and
elastic event matching service for attribute-based pub/sub systems in the cloud
computing environment. SEMAS uses one-hop lookup overlay to reduce the routing
latency. Through ahierarchical multi-attribute
space partition technique, SEMAS
adaptively partitions the skewed subscriptions and maps them into balanced
clusters to achieve high matching throughput. The performance-aware
detection scheme in SEMAS adaptively adjusts the scale of servers
according to the churn of workloads, leading to high performance–price ratio. A
prototype system on an OpenStack-based platform demonstrates that SEMAS has a
linear increasing matching capacity as the number of servers and the
partitioning granularity increase. It is able to elastically adjust the scale
of servers and tolerate a large number of server failures with low latency and
traffic overhead. Compared with existing cloud based pub/sub systems, SEMAS
achieves higher throughput in various workloads.
CHAPTER 2
2.0 SYSTEM ANALYSIS
2.1 EXISTING SYSTEM:
Characterized by the increasing arrival rate of live content, the emergency applications pose a great challenge: how to disseminate large-scale live content to interested users in a scalable and reliable manner. The publish/subscribe (pub/sub) model is widely used for data dissemination because of its capacity of seamlessly expanding the system to massive size. However, most event matching services of existing pub/sub systems either lead to low matching throughput when matching a large number of skewed subscriptions, or interrupt dissemination when a large number of servers fail.
However, most existing event matching services cannot adapt to the sudden change of the arrival live content rate, and generate a non-uniform distribution of load on the servers because of the skewness of the large-scale subscriptions. To this end SEMAS, a scalable and elastic event matching service for attribute-based pub/sub systems in the cloud computing environment. SEMAS uses one-hop lookup overlay to reduce the routing latency. Through ahierarchical multi-attribute space partition technique, SEMAS adaptively partitions the skewed subscriptions and maps them into balanced clusters to achieve high matching throughput.
The performance-aware
detection scheme in SEMAS adaptively adjusts the scale of servers
according to the churn of workloads, leading to high performance–price ratio. A
prototype system on an OpenStack-based platform demonstrates that SEMAS has a
linear increasing matching capacity as the number of servers and the
partitioning granularity increase. It is able to elastically adjust the scale
of servers and tolerate a large number of server failures with low latency and
traffic overhead.
2.1.1 DISADVANTAGES:
Publish/Subscribe (pub/sub) is a commonly used asynchronous communication pattern among application components. Senders and receivers of messages are decoupled from each other and interact with an intermediary— a pub/sub system.
A receiver registers its interest in certain kinds of messages with the pub/sub system in the form of a subscription. Messages are published by senders to the pub/sub system. The system matches messages (i.e., publications) to subscriptions and delivers messages to interested subscribers using a notification mechanism.
There are several ways for subscriptions to specify messages of interest. In its simplest form messages are associated with topic strings and subscriptions are defined as patterns of the topic string. A more expressive form is attribute-based pub/sub where messages are further annotated with various attributes.
Subscriptions are expressed as predicates on the message topic and attributes. An even more general form is content based pub/sub where subscriptions can be arbitrary Boolean functions on the entire content of messages (e.g., XML documents), limited to attributes1.
Attribute based pub/sub strikes a
balance between the simplicity and performance of topic-based pub/sub and the
expressiveness of content-based pub/sub. Many large-scale and loosely coupled
applications including stock quote distribution, network management, and
environmental monitoring can be structured around a pub/sub messaging paradigm.
2.2 PROPOSED SYSTEM:
We propose a scalable and reliable matching service for content-based pub/sub service in cloud computing environments, called SREM. Specifically, we mainly focus on two problems: one is how to organize servers in the cloud computing environment to achieve scalable and reliable routing. The other is how to manage subscriptions and events to achieve parallel matching among these servers. Generally speaking, we provide the following contributions:
We propose a distributed overlay protocol, called SkipCloud, to organize servers in the cloud computing environment. SkipCloud enables subscriptions and events to be forwarded among brokers in a scalable and reliable manner. Also it is easy to implement and maintain.
2.2.1 ADVANTAGES:
To achieve reliable connectivity and low routing latency, these brokers are connected through a distributed overlay, called SkipCloud. The entire content space is partitioned into disjoint subspaces, each of which is managed by a number of brokers. Subscriptions and events are dispatched to the subspaces that are overlapping with them through SkipCloud.
Since the pub/sub system needs to find all the matched subscribers, it requires each event to be matched in all datacenters, which leads to large traffic overhead with the increasing number of datacenters and the increasing arrival rate of live content.
Besides, it’s hard to achieve workload balance among the servers of all datacenters due to the various skewed distributions of users’ interests. Another question is that why we need a distributed overlay like SkipCloud to ensure reliable logical connectivity in datacenter environment where servers are more stable than the peers in P2P networks.
This is because as the number of servers
increases in datacenters, the node failure becomes normal, but not rare
exception. The node failure may lead to unreliable and inefficient routing
among servers. To this end, we try to organize servers into SkipCloud to reduce
the routing latency in a scalable and reliable manner.
2.3.1 HARDWARE REQUIREMENT:
CHAPTER 3
3.0 SYSTEM DESIGN:
Data Flow Diagram / Use Case Diagram / Flow Diagram:
External sources or destinations, which may be people or organizations or other entities
Here the data referenced by a process is stored and retrieved.
People, procedures or devices that produce data’s in the physical component is not identified.
Data moves in a specific direction from an origin to
a destination. The data flow is a “packet” of data.
MODELING RULES:
There are several common modeling rules when creating DFDs:
3.1
ARCHITECTURE DIAGRAM
3.2 DATAFLOW DIAGRAM
PUBLISHER:
SUBSCRIBER:
UML DIAGRAMS:
3.2 USE CASE DIAGRAM:
PUBLISHER:
SUBSCRIBER:
3.3 CLASS DIAGRAM:
PUBLISHER:
SUBSCRIBER:
3.4 SEQUENCE DIAGRAM:
PUBLISHER:
SUBSCRIBER:
3.5 ACTIVITY DIAGRAM:
PUBLISHER:
SUBSCRIBER:
CHAPTER 4
4.0 IMPLEMENTATION:
HPARTITION & SREM
To evaluate the performance of SkipCloud, we implement both SkipCloud and Chord to forward subscriptions and messages. To evaluate the performance of HPartition, the prototype supports different space partitioning policies. Moreover, the prototype provides three different message forwarding strategies, i.e, least subscription amount forwarding, random forwarding, and probability In order to take advantage of multiple distributed brokers, SREM divides the entire content space among the top clusters of SkipCloud, so that each top cluster only handles a subset of the entire space and searches a small number of candidate subscriptions.
SREM employs a hybrid multidimensional space partitioning technique, called HPartition, to achieve scalable and reliable event matching. Generally speaking, HPartition divides the entire content space into disjoint subspaces (Section 4.1). Subscriptions and events with overlapping subspaces are dispatched and matched on the same top cluster of SkipCloud (Sections 4.2 and 4.3). To keep workload balance among servers, HPartition divides the hot spots into multiple cold spots in an adaptive manner (Section 4.4). Table 2 shows key notations used in this section.
SREM
In SREM, there are mainly three roles:
clients, brokers, and clusters. Brokers are responsible for managing all of
them. Since the joining or leaving of these roles may lead to inefficient and
unreliable data dissemination, we will discuss the dynamics maintenance mechanisms
used by brokers in this section.
SUBSCRIBER DYNAMICS
To detect the status of subscribers, each subscriber establishes affinity with a broker (called home broker), and periodically sends its subscription as a heartbeat message to its home broker. The home broker maintains a timer for its every buffered subscription. If the broker has not received a heartbeat message from a subscriber over Tout time, the subscriber is supposed to be offline. Next, the home broker removes this subscription from its buffer and notifies the brokers containing the failed subscription to remove it.
BROKER DYNAMICS
Broker dynamics may lead to new clusters joining or old clusters leaving. In this section, we mainly consider the brokers joining/leaving from existing clusters, rather than the changing of the cluster size. When a new broker is generated by its datacenter management service, it firstly sends a “Broker Join” message to the leader broker in its top cluster. The leader broker returns back its top cluster identifier, neighbor lists of all levels of SkipCloud, and all subspaces including the corresponding subscriptions. The new broker generates its own identifier by adding a b-ary number to its top cluster identifier and takes the received items of each level as its initial neighbors.
There is no particular mechanism to handle broker departure from a cluster. In the top cluster, its leader broker can easily monitor the status of other brokers. For the clusters of the rest levels, the sampling service guarantees that the older items of each neighbor list are prior to be replaced by fresh ones during the view shuffling operation, which makes the failed brokers be removed from the system quickly. From the perspective of event matching, all brokers in the same top cluster have the same subspaces of subscriptions, which indicates that broker failure would not interrupt the event matching operation if there is at least one broker alive in each cluster.
CLUSTER DYNAMICS
Broker’s dynamics may lead to new clusters joining or old clusters leaving. Since each subspace is managed by the top cluster whose identifier is closest to that of the subspace, it’s necessary to adaptively migrate a number of old clusters to the new joining clusters. Specifically, the leader broker of the new cluster delivers its top ClusterID carried on a “Cluster Join” message to other clusters. The leader brokers in all other clusters find out the subspaces whose identifiers are closer to the new ClusterID than their own cluster identifiers, and migrate them to the new cluster.
Since each subspace is stored in one cluster, the cluster departure incurs subscription loss. The peer sampling service of SkipCloud can be used to detect failed clusters. To recover lost subscriptions, a simple method is to redirect the lost subscriptions by their owners’ heartbeat messages. Due to the unreliable links between subscribers and brokers, this approach may lead to long repair latency. To this end, we store all subscriptions into a number of well-known servers
of the datacenters. When these servers
obtain the failed clusters, they dispatch the subscriptions in these failed
clusters to the corresponding live clusters.
4.1 ALGORITHM
PREFIX ROUTING ALGORITHM
Prefix routing in SkipCloud is mainly
used to efficiently route subscriptions and events to the top clusters. Note
that the cluster identifiers at level i þ 1 are generated by appending one
b-ary to the corresponding clusters at level i. The relation of identifiers
between clusters is the foundation of routing to target clusters. Briefly, when
receiving a routing request to a specific cluster, a broker examines its
neighbor lists of all levels and chooses the neighbor which shares the longest
common prefix with the target ClusterID as the next hop. The routing operation
repeats until a broker can not find a neighbor whose identifier is more closer
than itself. Algorithm 2 describes the prefix routing algorithm in pseudo-code.
4.2 MODULES:
CONTENT-BASED (PUB/SUB):
KEY GENERATION (PUB/SUB):
CONTENT SPACE PARTITIONING:
SREM
SCALABILITY/RELIABILITY:
4.3 MODULE DESCRIPTION:
CONTENT-BASED (PUB/SUB):
Content-based pub/sub systems in cloud computing environment SREM connects the brokers through a distributed overlay SkipCloud, which ensures reliable connectivity among brokers through its multi-level clusters and brings a low routing latency through a prefix routing algorithm. Through a hybrid multi-dimensional space partitioning technique, SREM reaches scalable and balanced clustering of high dimensional skewed subscriptions, and each event is allowed to be matched on any of its candidate servers routing of events from publishers to the relevant subscribers, we use the content-based data model. We consider pub/sub in a setting where there exists no dedicated broker infrastructure. Publishers and subscribers contribute as peers to the maintenance of a self-organizing overlay structure. To authenticate publishers, we use the concept of advertisements in which a publisher announces beforehand the set of events which it intends to publish.
KEY GENERATION (PUB/SUB):
Recently, a number of cloud providers
have offered a series of pub/sub services. For instance, provides high
available key-value storage and matching respectively based on one-hop lookup
adopts a single-dimensional partitioning technique to divide the entire spare
and a performance-aware forwarding scheme to select candidate matcher for each
event. Publisher keys: Before starting to publish
events, a publisher contacts the key server along with the credentials for each
attribute in its advertisement. If the publisher is allowed to publish events
according to its credentials, the key server will generate separate private
keys for each credential. The public key of a publisher p for credential is
generated. Subscriber keys: Similarly, to receive events matching its
subscription, a subscriber should contact the key server and receive the
private keys for the credentials associated with each attribute A.
CONTENT SPACE PARTITIONING:
To achieve scalable and reliable event matching among multiple servers, we propose a hybrid multidimensional space partitioning technique, called HPartition. It allows similar subscriptions to be divided into the same server and provides multiple candidate matching servers for each event. Moreover, it adaptively alleviates hot spots and keeps workload balance among all servers utilizes distributed multiple clusters, a better solution is to balance the workloads among clusters through partitioning and migrating hot spots. The gain of the partitioning technique is greatly affected by the distribution of subscriptions of the hot spot. To this end, HPartition divides each hot spot into a number of cold spots through two partitioning techniques: hierarchical subspace partitioning and subscription set partitioning. The first aims to partition the hot spots where the subscriptions are diffused among the whole space, and the second aims to partition the hot spots where the subscriptions fall into a narrow space.
SREM SCALABILITY/RELIABILITY:
SREM scalability and reliability when matching large-scale live content under highly dynamic environments in this mainly stems from the following facts:
1) Most of them are inappropriate to the matching of live content with high data dimensionality due to the limitation of their subscription space partitioning techniques, which bring either low matching throughput or high memory overhead.
2) These systems adopt the one-hop lookup technique among servers to reduce routing latency. In spite of its high efficiency, it requires each dispatching server to have the same view of matching servers. Otherwise, the subscriptions or events may be assigned to the wrong matching servers, which bring the availability problem in the face of current joining or crash of matching servers. A number of schemes can be used to keep the consistent view, like periodically sending heartbeat messages to dispatching servers or exchanging messages among matching servers. However, these extra schemes may bring a large traffic overhead or the interruption of event matching service.
CHAPTER 5
5.0 SYSTEM STUDY:
5.1 FEASIBILITY STUDY:
The feasibility of the project is analyzed in this phase and business proposal is put forth with a very general plan for the project and some cost estimates. During system analysis the feasibility study of the proposed system is to be carried out. This is to ensure that the proposed system is not a burden to the company. For feasibility analysis, some understanding of the major requirements for the system is essential.
Three key considerations involved in the feasibility analysis are
5.1.1 ECONOMICAL FEASIBILITY:
This study is carried out to check the economic impact that the system will have on the organization. The amount of fund that the company can pour into the research and development of the system is limited. The expenditures must be justified. Thus the developed system as well within the budget and this was achieved because most of the technologies used are freely available. Only the customized products had to be purchased.
This study is carried out to check the technical feasibility, that is, the technical requirements of the system. Any system developed must not have a high demand on the available technical resources. This will lead to high demands on the available technical resources. This will lead to high demands being placed on the client. The developed system must have a modest requirement, as only minimal or null changes are required for implementing this system.
5.1.3 SOCIAL FEASIBILITY:
The aspect of study is to check the level of acceptance of the system by the user. This includes the process of training the user to use the system efficiently. The user must not feel threatened by the system, instead must accept it as a necessity. The level of acceptance by the users solely depends on the methods that are employed to educate the user about the system and to make him familiar with it. His level of confidence must be raised so that he is also able to make some constructive criticism, which is welcomed, as he is the final user of the system.
5.2 SYSTEM TESTING:
Testing is a process of checking whether the developed system is working according to the original objectives and requirements. It is a set of activities that can be planned in advance and conducted systematically. Testing is vital to the success of the system. System testing makes a logical assumption that if all the parts of the system are correct, the global will be successfully achieved. In adequate testing if not testing leads to errors that may not appear even many months.
This creates two problems, the time lag
between the cause and the appearance of the problem and the effect of the
system errors on the files and records within the system. A small system error
can conceivably explode into a much larger Problem. Effective testing early in
the purpose translates directly into long term cost savings from a reduced
number of errors. Another reason for system testing is its utility, as a
user-oriented vehicle before implementation. The best programs are worthless if
it produces the correct outputs.
5.2.1 UNIT TESTING:
| Description | Expected result |
| Test for application window properties. | All the properties of the windows are to be properly aligned and displayed. |
| Test for mouse operations. | All the mouse operations like click, drag, etc. must perform the necessary operations without any exceptions. |
A program
represents the logical elements of a system. For a program to run
satisfactorily, it must compile and test data correctly and tie in properly
with other programs. Achieving an error free program is the responsibility of
the programmer. Program testing checks
for two types
of errors: syntax
and logical. Syntax error is a
program statement that violates one or more rules of the language in which it
is written. An improperly defined field dimension or omitted keywords are
common syntax errors. These errors are shown through error message generated by
the computer. For Logic errors the programmer must examine the output
carefully.
5.1.2 FUNCTIONAL TESTING:
Functional testing of an application is used to prove the application delivers correct results, using enough inputs to give an adequate level of confidence that will work correctly for all sets of inputs. The functional testing will need to prove that the application works for each client type and that personalization function work correctly.When a program is tested, the actual output is compared with the expected output. When there is a discrepancy the sequence of instructions must be traced to determine the problem. The process is facilitated by breaking the program into self-contained portions, each of which can be checked at certain key points. The idea is to compare program values against desk-calculated values to isolate the problems.
| Description | Expected result |
| Test for all modules. | All peers should communicate in the group. |
| Test for various peer in a distributed network framework as it display all users available in the group. | The result after execution should give the accurate result. |
5.1. 3 NON-FUNCTIONAL TESTING:
The Non Functional software testing encompasses a rich spectrum of testing strategies, describing the expected results for every test case. It uses symbolic analysis techniques. This testing used to check that an application will work in the operational environment. Non-functional testing includes:
5.1.4 LOAD TESTING:
An important tool for implementing system tests is a Load generator. A Load generator is essential for testing quality requirements such as performance and stress. A load can be a real load, that is, the system can be put under test to real usage by having actual telephone users connected to it. They will generate test input data for system test.
| Description | Expected result |
| It is necessary to ascertain that the application behaves correctly under loads when ‘Server busy’ response is received. | Should designate another active node as a Server. |
5.1.5 PERFORMANCE TESTING:
Performance tests are utilized in order to determine the widely defined performance of the software system such as execution time associated with various parts of the code, response time and device utilization. The intent of this testing is to identify weak points of the software system and quantify its shortcomings.
| Description | Expected result |
| This is required to assure that an application perforce adequately, having the capability to handle many peers, delivering its results in expected time and using an acceptable level of resource and it is an aspect of operational management. | Should handle large input values, and produce accurate result in a expected time. |
5.1.6 RELIABILITY TESTING:
The software reliability is the ability of a system or component to perform its required functions under stated conditions for a specified period of time and it is being ensured in this testing. Reliability can be expressed as the ability of the software to reveal defects under testing conditions, according to the specified requirements. It the portability that a software system will operate without failure under given conditions for a given time interval and it focuses on the behavior of the software element. It forms a part of the software quality control team.
| Description | Expected result |
| This is to check that the server is rugged and reliable and can handle the failure of any of the components involved in provide the application. | In case of failure of the server an alternate server should take over the job. |
5.1.7 SECURITY TESTING:
Security testing evaluates system characteristics that relate to the availability, integrity and confidentiality of the system data and services. Users/Clients should be encouraged to make sure their security needs are very clearly known at requirements time, so that the security issues can be addressed by the designers and testers.
| Description | Expected result |
| Checking that the user identification is authenticated. | In case failure it should not be connected in the framework. |
| Check whether group keys in a tree are shared by all peers. | The peers should know group key in the same group. |
5.1.8 WHITE BOX TESTING:
White box testing, sometimes called glass-box testing is a test case design method that uses the control structure of the procedural design to derive test cases. Using white box testing method, the software engineer can derive test cases. The White box testing focuses on the inner structure of the software structure to be tested.
| Description | Expected result |
| Exercise all logical decisions on their true and false sides. | All the logical decisions must be valid. |
| Execute all loops at their boundaries and within their operational bounds. | All the loops must be finite. |
| Exercise internal data structures to ensure their validity. | All the data structures must be valid. |
5.1.9 BLACK BOX TESTING:
Black box testing, also called behavioral testing, focuses on the functional requirements of the software. That is, black testing enables the software engineer to derive sets of input conditions that will fully exercise all functional requirements for a program. Black box testing is not alternative to white box techniques. Rather it is a complementary approach that is likely to uncover a different class of errors than white box methods. Black box testing attempts to find errors which focuses on inputs, outputs, and principle function of a software module. The starting point of the black box testing is either a specification or code. The contents of the box are hidden and the stimulated software should produce the desired results.
| Description | Expected result |
| To check for incorrect or missing functions. | All the functions must be valid. |
| To check for interface errors. | The entire interface must function normally. |
| To check for errors in a data structures or external data base access. | The database updation and retrieval must be done. |
| To check for initialization and termination errors. | All the functions and data structures must be initialized properly and terminated normally. |
All
the above system testing strategies are carried out in as the development,
documentation and institutionalization of the proposed goals and related
policies is essential.
CHAPTER 6
6.0 SOFTWARE DESCRIPTION:
Java technology is both a programming language and a platform.
With most programming languages, you either compile or interpret a program so that you can run it on your computer. The Java programming language is unusual in that a program is both compiled and interpreted. With the compiler, first you translate a program into an intermediate language called Java byte codes —the platform-independent codes interpreted by the interpreter on the Java platform. The interpreter parses and runs each Java byte code instruction on the computer. Compilation happens just once; interpretation occurs each time the program is executed. The following figure illustrates how this works.
You can think of Java byte codes as the machine code instructions for the Java Virtual Machine (Java VM). Every Java interpreter, whether it’s a development tool or a Web browser that can run applets, is an implementation of the Java VM. Java byte codes help make “write once, run anywhere” possible. You can compile your program into byte codes on any platform that has a Java compiler. The byte codes can then be run on any implementation of the Java VM. That means that as long as a computer has a Java VM, the same program written in the Java programming language can run on Windows 2000, a Solaris workstation, or on an iMac.
A platform is the hardware or software environment in which a program runs. We’ve already mentioned some of the most popular platforms like Windows 2000, Linux, Solaris, and MacOS. Most platforms can be described as a combination of the operating system and hardware. The Java platform differs from most other platforms in that it’s a software-only platform that runs on top of other hardware-based platforms.
The Java platform has two components:
You’ve already been introduced to the Java VM. It’s the base for the Java platform and is ported onto various hardware-based platforms.
The Java API is a large collection of ready-made software components that provide many useful capabilities, such as graphical user interface (GUI) widgets. The Java API is grouped into libraries of related classes and interfaces; these libraries are known as packages. The next section, What Can Java Technology Do? Highlights what functionality some of the packages in the Java API provide.
The following figure depicts a program that’s running on the Java platform. As the figure shows, the Java API and the virtual machine insulate the program from the hardware.
Native code is code that after you compile it, the compiled code runs on a specific hardware platform. As a platform-independent environment, the Java platform can be a bit slower than native code. However, smart compilers, well-tuned interpreters, and just-in-time byte code compilers can bring performance close to that of native code without threatening portability.
The most common types of programs written in the Java programming language are applets and applications. If you’ve surfed the Web, you’re probably already familiar with applets. An applet is a program that adheres to certain conventions that allow it to run within a Java-enabled browser.
However, the Java programming language is not just for writing cute, entertaining applets for the Web. The general-purpose, high-level Java programming language is also a powerful software platform. Using the generous API, you can write many types of programs.
An application is a standalone program that runs directly on the Java platform. A special kind of application known as a server serves and supports clients on a network. Examples of servers are Web servers, proxy servers, mail servers, and print servers. Another specialized program is a servlet.
A servlet can almost be thought of as an applet that runs on the server side. Java Servlets are a popular choice for building interactive web applications, replacing the use of CGI scripts. Servlets are similar to applets in that they are runtime extensions of applications. Instead of working in browsers, though, servlets run within Java Web servers, configuring or tailoring the server.
How does the API support all these kinds of programs? It does so with packages of software components that provides a wide range of functionality. Every full implementation of the Java platform gives you the following features:
The Java platform also has APIs for 2D and 3D graphics, accessibility, servers, collaboration, telephony, speech, animation, and more. The following figure depicts what is included in the Java 2 SDK.
We can’t promise you fame, fortune, or even a job if you learn the Java programming language. Still, it is likely to make your programs better and requires less effort than other languages. We believe that Java technology will help you do the following:
Microsoft Open Database Connectivity (ODBC) is a standard programming interface for application developers and database systems providers. Before ODBC became a de facto standard for Windows programs to interface with database systems, programmers had to use proprietary languages for each database they wanted to connect to. Now, ODBC has made the choice of the database system almost irrelevant from a coding perspective, which is as it should be. Application developers have much more important things to worry about than the syntax that is needed to port their program from one database to another when business needs suddenly change.
Through the ODBC Administrator in Control Panel, you can specify the particular database that is associated with a data source that an ODBC application program is written to use. Think of an ODBC data source as a door with a name on it. Each door will lead you to a particular database. For example, the data source named Sales Figures might be a SQL Server database, whereas the Accounts Payable data source could refer to an Access database. The physical database referred to by a data source can reside anywhere on the LAN.
The ODBC system files are not installed on your system by Windows 95. Rather, they are installed when you setup a separate database application, such as SQL Server Client or Visual Basic 4.0. When the ODBC icon is installed in Control Panel, it uses a file called ODBCINST.DLL. It is also possible to administer your ODBC data sources through a stand-alone program called ODBCADM.EXE. There is a 16-bit and a 32-bit version of this program and each maintains a separate list of ODBC data sources.
From a programming perspective, the beauty of ODBC is that the application can be written to use the same set of function calls to interface with any data source, regardless of the database vendor. The source code of the application doesn’t change whether it talks to Oracle or SQL Server. We only mention these two as an example. There are ODBC drivers available for several dozen popular database systems. Even Excel spreadsheets and plain text files can be turned into data sources. The operating system uses the Registry information written by ODBC Administrator to determine which low-level ODBC drivers are needed to talk to the data source (such as the interface to Oracle or SQL Server). The loading of the ODBC drivers is transparent to the ODBC application program. In a client/server environment, the ODBC API even handles many of the network issues for the application programmer.
The advantages
of this scheme are so numerous that you are probably thinking there must be
some catch. The only disadvantage of ODBC is that it isn’t as efficient as
talking directly to the native database interface. ODBC has had many detractors
make the charge that it is too slow. Microsoft has always claimed that the
critical factor in performance is the quality of the driver software that is
used. In our humble opinion, this is true. The availability of good ODBC
drivers has improved a great deal recently. And anyway, the criticism about
performance is somewhat analogous to those who said that compilers would never
match the speed of pure assembly language. Maybe not, but the compiler (or
ODBC) gives you the opportunity to write cleaner programs, which means you
finish sooner. Meanwhile, computers get faster every year.
6.6 JDBC:
In an effort to set an independent database standard API for Java; Sun Microsystems developed Java Database Connectivity, or JDBC. JDBC offers a generic SQL database access mechanism that provides a consistent interface to a variety of RDBMSs. This consistent interface is achieved through the use of “plug-in” database connectivity modules, or drivers. If a database vendor wishes to have JDBC support, he or she must provide the driver for each platform that the database and Java run on.
To gain a wider acceptance of JDBC, Sun based JDBC’s framework on ODBC. As you discovered earlier in this chapter, ODBC has widespread support on a variety of platforms. Basing JDBC on ODBC will allow vendors to bring JDBC drivers to market much faster than developing a completely new connectivity solution.
JDBC was announced in March of 1996. It was released for a 90 day public review that ended June 8, 1996. Because of user input, the final JDBC v1.0 specification was released soon after.
The remainder of this section will cover enough information about JDBC for you to know what it is about and how to use it effectively. This is by no means a complete overview of JDBC. That would fill an entire book.
Few software packages are designed without goals in mind. JDBC is one that, because of its many goals, drove the development of the API. These goals, in conjunction with early reviewer feedback, have finalized the JDBC class library into a solid framework for building database applications in Java.
The goals that were set for JDBC are important. They will give you some insight as to why certain classes and functionalities behave the way they do. The eight design goals for JDBC are as follows:
SQL Level API
The designers felt that their main goal was to define a SQL interface for Java. Although not the lowest database interface level possible, it is at a low enough level for higher-level tools and APIs to be created. Conversely, it is at a high enough level for application programmers to use it confidently. Attaining this goal allows for future tool vendors to “generate” JDBC code and to hide many of JDBC’s complexities from the end user.
SQL Conformance
SQL syntax varies as you move from database vendor to database vendor. In an effort to support a wide variety of vendors, JDBC will allow any query statement to be passed through it to the underlying database driver. This allows the connectivity module to handle non-standard functionality in a manner that is suitable for its users.
JDBC must be implemental on top of common database interfaces
The JDBC SQL API must “sit” on top of other common SQL level APIs. This goal allows JDBC to use existing ODBC level drivers by the use of a software interface. This interface would translate JDBC calls to ODBC and vice versa.
Because of Java’s acceptance in the user community thus far, the designers feel that they should not stray from the current design of the core Java system.
This goal probably appears in all software design goal listings. JDBC is no exception. Sun felt that the design of JDBC should be very simple, allowing for only one method of completing a task per mechanism. Allowing duplicate functionality only serves to confuse the users of the API.
Strong typing allows for more error checking to be done at compile time; also, less error appear at runtime.
Because more often than not, the usual SQL calls
used by the programmer are simple SELECT’s,
INSERT’s,
DELETE’s
and UPDATE’s,
these queries should be simple to perform with JDBC. However, more complex SQL
statements should also be possible.
Finally we decided to precede the implementation using Java Networking.
And for dynamically updating the cache table we go for MS Access database.
Java ha two things: a programming language and a platform.
Java is a high-level programming language that is all of the following
Simple Architecture-neutral
Object-oriented Portable
Distributed High-performance
Interpreted Multithreaded
Robust Dynamic Secure
Java is also unusual in that each Java program is both compiled and interpreted. With a compile you translate a Java program into an intermediate language called Java byte codes the platform-independent code instruction is passed and run on the computer.
Compilation happens just once; interpretation occurs each time the program is executed. The figure illustrates how this works.
| Java Program |
| Compilers |
| Interpreter |
| My Program |
The TCP/IP stack is shorter than the OSI one:
TCP is a connection-oriented protocol; UDP (User Datagram Protocol) is a connectionless protocol.
The IP layer provides a connectionless and unreliable delivery system. It considers each datagram independently of the others. Any association between datagram must be supplied by the higher layers. The IP layer supplies a checksum that includes its own header. The header includes the source and destination addresses. The IP layer handles routing through an Internet. It is also responsible for breaking up large datagram into smaller ones for transmission and reassembling them at the other end.
UDP is also connectionless and unreliable. What it adds to IP is a checksum for the contents of the datagram and port numbers. These are used to give a client/server model – see later.
TCP supplies logic to give a reliable connection-oriented protocol above IP. It provides a virtual circuit that two processes can use to communicate.
In order to use a service, you must be able to find it. The Internet uses an address scheme for machines so that they can be located. The address is a 32 bit integer which gives the IP address.
Class A uses 8 bits for the network address with 24 bits left over for other addressing. Class B uses 16 bit network addressing. Class C uses 24 bit network addressing and class D uses all 32.
Internally, the UNIX network is divided into sub networks. Building 11 is currently on one sub network and uses 10-bit addressing, allowing 1024 different hosts.
8 bits are finally used for host addresses within our subnet. This places a limit of 256 machines that can be on the subnet.
The 32 bit address is usually written as 4 integers separated by dots.
A service exists on a host, and is identified by its port. This is a 16 bit number. To send a message to a server, you send it to the port for that service of the host that it is running on. This is not location transparency! Certain of these ports are “well known”.
A socket is a data structure maintained by the system
to handle network connections. A socket is created using the call socket. It returns an integer that is like a file descriptor.
In fact, under Windows, this handle can be used with Read File and Write File
functions.
#include <sys/types.h>
#include <sys/socket.h>
int socket(int family, int type, int protocol);
Here “family” will be AF_INET for IP communications, protocol will be zero, and type will depend on whether TCP or UDP is used. Two
processes wishing to communicate over a network create a socket each. These are
similar to two ends of a pipe – but the actual pipe does not yet exist.
6.8 JFREE CHART:
JFreeChart is a free 100% Java chart library that makes it easy for developers to display professional quality charts in their applications. JFreeChart’s extensive feature set includes:
A consistent and well-documented API, supporting a wide range of chart types;
A flexible design that is easy to extend, and targets both server-side and client-side applications;
Support for many output types, including Swing components, image files (including PNG and JPEG), and vector graphics file formats (including PDF, EPS and SVG);
JFreeChart is “open source” or, more specifically, free software. It is distributed under the terms of the GNU Lesser General Public Licence (LGPL), which permits use in proprietary applications.
Charts showing values that relate to geographical areas. Some examples include: (a) population density in each state of the United States, (b) income per capita for each country in Europe, (c) life expectancy in each country of the world. The tasks in this project include: Sourcing freely redistributable vector outlines for the countries of the world, states/provinces in particular countries (USA in particular, but also other areas);
Creating an appropriate dataset interface (plus
default implementation), a rendered, and integrating this with the existing
XYPlot class in JFreeChart; Testing, documenting, testing some more,
documenting some more.
Implement a new (to JFreeChart) feature for interactive time series charts — to display a separate control that shows a small version of ALL the time series data, with a sliding “view” rectangle that allows you to select the subset of the time series data to display in the main chart.
There is currently a lot of interest in dashboard displays. Create a flexible dashboard mechanism that supports a subset of JFreeChart chart types (dials, pies, thermometers, bars, and lines/time series) that can be delivered easily via both Java Web Start and an applet.
The property editor mechanism in JFreeChart only
handles a small subset of the properties that can be set for charts. Extend (or
reimplement) this mechanism to provide greater end-user control over the
appearance of the charts.
CHAPTER 8
8.0 CONCLUSION & FUTURE WORK:
This paper introduces SREM, a scalable and reliable event matching service for content-based pub/sub systems in cloud computing environment. SREM connects the brokers through a distributed overlay SkipCloud, which ensures reliable connectivity among brokers through its multi-level clusters and brings a low routing latency through a prefix routing algorithm. Through a hybrid multi-dimensional space partitioning technique, SREM reaches scalable and balanced clustering of high dimensional skewed subscriptions, and each event is allowed to be matched on any of its candidate servers.
Extensive experiments with real deployment based on a CloudStack testbed are conducted, producing results which demonstrate that SREM is effective and practical, and also presents good workload balance, scalability and reliability under various parameter settings. Although our proposed event matching service can efficiently filter out irrelevant users from big data volume, there are still a number of problems we need to solve. Firstly, we do not provide elastic resource provisioning strategies in this paper to obtain a good performance price ratio.
We plan to design and implement the
elastic strategies of adjusting the scale of servers based on the churn
workloads. Secondly, it does not guarantee that the brokers disseminate large
live content with various data sizes to the corresponding subscribers in a
real-time manner. For the dissemination of bulk content, the upload capacity
becomes the main bottleneck. Based on our proposed event matching service, we
will consider utilizing a cloud-assisted technique to realize a general and
scalable data dissemination service over live content with various data sizes.
CHAPTER 9