14-04-2011, 03:06 PM
[attachment=12199]
Presented By:
K.SATISH (MASTER OF COMPUTER APPLICATIONS)
Under the guidance of T. S. Raja Rajeswari,
DEPARTMENT OF MASTER OF COMPUTER APPLICATIONS
SRI CHUNDI RANGANAYAKULU ENGINEERING COLLEGE
Abstract
Measurement and estimation of packet loss characteristics are challenging due to the relatively rare occurrence and typically short duration of packet loss episodes. While active probe tools are commonly used to measure packet loss on end-to-end paths, there has been little analysis of the accuracy of these tools.
The objective of our study is to understand how to measure packet loss episodes accurately with end-to-end probes. Studies show that the standard Poisson-modulated end-to-end measurement of packet loss accuracy has to be improved. Thus, we introduce a new algorithm for packet loss measurement that is designed to overcome the deficiencies in standard Poisson-based tools. Specifically, our method entails probe experiments that follow a geometric distribution to enable more accurate measurements than standard Poisson probing and other traditional packet loss measurement tools. We also find the transfer rate. We evaluate the capabilities of our methodology experimentally by developing and implementing a prototype tool, called BADABING. The experiments demonstrate the trade-offs between impact on the network and measurement accuracy. BADABING reports loss characteristics are far more accurately than traditional loss measurement tools.
NTRODUCTION
1.1 Scope of the Project:
Measuring and analyzing network traffic dynamics between end hosts has provided the foundation for the development of many different network protocols and systems. Of particular importance is under-standing packet loss behavior since loss can have a significant impact on the performance of both TCP and UDP based applications. Despite efforts of network engineers and operators to limit loss, it will probably never be eliminated due to the intrinsic dynamics and scaling properties of traffic in packet switched network. Network operators have the ability to passively monitor nodes within their network for packet loss on routers using SNMP. End-to-end active measurements using probes provide an equally valuable perspective since they indicate the conditions that application traffic is experiencing on those paths.
Our study involves the empirical evaluation of our new loss measurement methodology. To this end, we developed a one-way active measurement tool called BADABING. BADABING sends fixed-size probes at specified intervals from one measurement host to a collaborating target host. The target system collects the probe packets and reports the loss characteristics after a specified period of time. We also compare BADABING with a standard tool for loss measurement that emits probe packets at Poisson intervals. The results show that our tool reports loss episode estimates much more accurately for the same number of probes. We also show that BADABING estimates converge to the underlying loss episode frequency and duration characteristics. Our observations about the weaknesses in standard Poisson probing motivate the second part of our study: the development of a new approach for end-to-end loss measurement that includes four key elements. First, we design a probe process that is geometrically distributed and that assesses the likelihood of loss experienced by other flows that use the same path, rather than merely reporting its own packet losses. The probe process assumes FIFO queues along the path with a drop-tail policy. Second, we design a new experimental framework with estimation techniques that directly estimate the mean duration of the loss episodes without estimating the duration of any individual loss episode. Our estimators are proved to be consistent, under mild assumptions of the probing process.
2. SYSTEM ANALYSIS
2.1 Existing System:
• In an Existing System, they analyze the usefulness of Poisson Arrivals See Time Averages in the networking context. Of particular relevance to our work is Paxson’s recommendation and use of Poisson- modulated active probe streams to reduce bias in delay and loss measurements.
• Several studies include the use of loss measurements to estimate network properties such as bottleneck buffer size and cross traffic intensity, which is not accurate.
• Network tomography based on using both multicast and unicast probes has also been demonstrated to be in-effective (in some cases) for inferring loss rates on internal links on end-to-end paths.
2.2 Proposed System:
• The purpose of our study was to understand how to measure end-to-end packet loss characteristics accurately with probes and in a way that enables us to specify the impact on the bottleneck queue.
• The goal of our study is to understand how to accurately measure loss characteristics on end-to-end paths with probes.
• Specifically, our method entails probe experiments that follow a geometric distribution to
1) enable an explicit trade-off between accuracy and impact on the network, and
2) enable more accurate measurements than standard Poisson probing at the same rate.
• Our study consists of three parts:
1) empirical evaluation of the currently prevailing approach,
2) development of estimation techniques that are based on novel experimental design, novel probing techniques, and simple validation tests, and
3) empirical evaluation of this new methodology.
Poisson modulated process:
We begin by using our capabilities to evaluate the simple Poisson-modulated loss probe measurements using the ZING tool. ZING measures packet loss in one direction on an end-to-end path. The ZING sender emits packets at Poisson-modulated intervals with timestamps and unique sequence numbers and the receiver logs the probe packet arrivals. We specify the mean probe rate, the probe packet size, and the number of packets in a “flight.”
BADABING:
We implemented this method in a new tool, BADABING, which we tested in our laboratory. Our tests demonstrate that BADABING, in most cases, accurately estimates loss frequencies and durations over a range of cross traffic conditions. For the same overall packet rate, our results show that BADABING is significantly more accurate than Poisson probing for measuring loss episode characteristics.
Our methodology involves dispatching a sequence of probes, each consisting of one or more very closely spaced packets. The aim of a probe is to obtain a snapshot of the state of the network at the instant of probing. As such, the record for each probe indicates whether or not it encountered a loss episode, as evidenced by either the loss or sufficient delay of any of the packets within a probe.
The probes themselves are organized into what we term basic experiments, each of which comprises a number of packets sent in rapid succession. The aim of the basic experiment is to determine the dynamics of transitions between the congested and un-congested state of the network, i.e., beginnings and endings of loss episodes. Below we show how this enables us to estimate the duration of loss episodes.
A full experiment comprises a sequence of basic experiments generated according to some rule. The sequence may be terminated after some specified number of basic experiments, or after a given duration, or in an open-ended adaptive fashion, e.g., until estimates of desired accuracy for a loss characteristic have been obtained, or until such accuracy is determined impossible.
Advantage:
The advantage of our study is to understand how to accurately measure loss characteristics on end-to-end paths with probes. We are interested in two specific characteristics of packet loss: loss episode frequency, and loss episode duration. Thus we improve the accuracy in measuring the packet loss. This is the major advantage of our work.
2.2.1 Modules Description
2.2.1.1 User Interface Design:
In this module we design the user interface window. The window is designed in order to display all the processes in this project. We use the Swing package available in Java to design the User Interface. Swing is a widget toolkit for Java. It is part of Sun Microsystems' Java Foundation Classes (JFC) — an API for providing a graphical user interface (GUI) for Java programs. We design the user interface Window by using Swing package available in Java.
2.2.1.2 Packet Separation:
In this module we use the browse button to load an input text file. This process is done by using the File Dialog class available in Java. After loading the file we read all the characters inside the input file. After that we separate the total characters available into blocks of equal numbers. This process is known as packet separation.
2.2.1.3 Designing the Queue:
The Queue is designed in order to create the packet loss due to bottleneck and network traffic. We create packet loss in this module voluntarily in order to measure it. The packets from the sender are received here and loss is created. Then the remaining packet which passes the Queue is sent to the Receiver.
2.2.1.4 Packet Receiver:
In this module we design a Receiver, which is used to receive the packets. The packets which are remaining after the loss in the Queue are received here. These packets are displayed in this window. Thus we can know the packet loss in the Receiver window. After that we can use the parameters, Badabing and Poisson modulated process to calculate the packet loss in next module.
2.2.1.5 Packet Loss Calculations:
In this module we calculate the packet loss. We calculate the packet loss accurately by using the Badabing. We also calculate the packet loss using the traditional technique known as Poisson modulated process. Finally we show the results in a window in order to compare the measurement of packet loss calculations to prove our accuracy in Badabing over the Poisson modulated process.
2.3 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
ECONOMICAL FEASIBILITY
TECHNICAL FEASIBILITY
OPERATIONAL FEASIBILITY
