International Journal of Engineering Trends and Technology (IJETT) – Volume 35 Number 2- May 2016
A Modern Dynamic Packet Generation Technique for
Network Architecture
1
Jarugulla Aswani, 2 M.V.B Chandra Sekhar , 3 B.Ramesh
1
M.Tech Scholar, 2 Associate Professor , 3 Assistant Professor
1,2,3
Department of Computer Science And Engineering,AITAM, Tekkali, Srikakulam(India)
Abstract: We are proposing a network model monitoring
system which constructs a small network of nodes and some
switches or routers virtually. Every node runs at some
physical location that is port number and data packet can be
transmitted between the nodes, topology can be defined by
router, so during the data transmission nodes should satisfy
the rules like forward rule, link rule and drop rule, these rules
can be applied verified with respect to packet headers at any
point. Traditional way of command line communication may
not give optimal results and topology construction is the
major factor while transmission of data packets and the main
disadvantages are Monitoring the network with regular
command is complex we are improving the topology work
with parameters of signal strength and channel capacity.
I. INTRODUCTION
Systems are getting bigger and more perplexing, yet
overseers depend on simple devices, for example, ping and
traceroute to investigate issues. We propose a robotized and
precise methodology for testing and investigating systems
called "Programmed Test Packet Generation" (ATPG).
ATPG peruses switch setups and creates a gadget
autonomous model. The model is utilized to create a base
arrangement of test bundles to (negligibly) practice each
connection in the system or (maximally) work out each
principle in the system [1][2]. Test parcels are sent
occasionally, and recognized disappointments trigger a
different system to restrict the deficiency. ATPG can identify
both useful (e.g., off base firewall principle) and execution
issues (e.g., congested line). ATPG supplements yet goes
past prior work in static checking (which can't recognize
liveness or execution blames) or blame confinement (which
just restrict issues given liveness results). We portray our
model ATPG execution and results on two genuine
information sets: Stanford University's spine system and
Internet2. We find that a little number of test parcels suffices
to test all guidelines in these systems: For instance, 4000
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parcels can cover all guidelines in Stanford spine system,
while 54 are sufficient to cover all connections. Sending
4000 test parcels 10 times each second expends under 1% of
connection limit. ATPG code and the information sets are
freely accessible [3].
Test Packet Generator:
The test bundle generator, written in Python,
contains a Cisco IOS arrangement parser and a Juniper Junos
parser. The data plane data, including switch designs, FIBs,
MAC learning tables, and system topologies, is gathered and
parsed through the charge line interface (Cisco IOS) or XML
documents . The generator then uses the Hassel header space
examination library to develop switch and topology
capacities.
All-sets reachability is figured utilizing the parallelpreparing module delivered with Python. Every procedure
considers a subset of the test ports and discovers all the
reachable ports from every one. After reachability tests are
finished, results are gathered, and the expert procedure
executes the Min- Set-Cover calculation. Test parcels and the
arrangement of tried principles are put away in a SQLite
database [4].
The way blast issue has rather gotten more
consideration. Likewise to the hunt heuristics introduced,
look methodologies proposed in the past incorporate Best
First Search, Generational Seek, and Hybrid Concolic
Testing. Orthogonal to inquiry heuristics, specialists have
tended to the way blast problem by testing ways
compositionally and by following the qualities read and
composed by the system.
The long haul objective of our work is to have the
capacity to take a self-assertive program and routinely get
90%+ code scope, pounding it under experiments that
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International Journal of Engineering Trends and Technology (IJETT) – Volume 35 Number 2- May 2016
investigate all fascinating ways with every single intriguing
worth. While there is still a long approach to achieve this
objective, the outcomes in this paper demonstrate that the
methodology can get high code scope over a wide scope of
genuine applications, scope that surpassed that of an
excellent, manual test suites developed incrementally over a
time of 15 years, and also finding bugs that had been around
over 10 years. The strategies we depict ought to function
admirably with different devices and give comparative help
in taking care of a wide class of utilizations[5].
II. RELATED WORK
Even though various traditional approaches
proposed by various researchers from years of research,
every work has its own advantages and disadvantages.
Network monitoring is always a complex task to identify the
issue of the node or data failure, Complex to identify the
issue node, Simple weight metrics may not give the optimal
path and no rule based approach for failure of nodes
We introduce another typical execution apparatus,
KLEE, fit of naturally creating tests that accomplish high
scope on a differing set of complex and naturally serious
projects. We connected KLEE to each of the 90 programs in
the GNU COREUTILS utility suite, which frame the center
client level environment introduced on all Unix frameworks
and, in that capacity, speak to a few of the most intensely
utilized and tried open-source programs in presence. For 84%
of these utilities, KLEE's naturally created tests secured 80–
100% of executable articulations and, in total, fundamentally
beat the scope of the designers' own particular written by
hand test suites. KLEE additionally discovered nine genuine
bugs (counting three that had been missed for more than 15
years!) and created solid inputs that set off the blunders when
run on the uninstrumented code [6][7]. At the point when
connected to MINIX's adaptations of a little choice of the
same applications, KLEE accomplished comparable scope
(alongside two bugs). What's more, we additionally utilized
KLEE to consequently discover various off base contrasts
between a few MINIX and COREUTILS devices. At long
last, we checked the piece of the HISTAR working
framework, producing tests that accomplished 76.4%
(without paging empowered) and 67.1% scope (with paging)
and discovered one imperative security bug.
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We display another typical execution device, KLEE,
which we composed without any preparation to be vigorous
and to profoundly check a wide scope of utilizations. We
utilized quite a long while of lessons from our past apparatus,
EXE. KLEE employments novel imperative unraveling
advancements that enhance execution by over a request of
greatness and let it handle numerous projects that are totally
immovable generally. Its space-productive representation of a
checked way implies it can have tens to a huge number of
such ways dynamic all the while. Its pursuit heuristics
successfully select from these huge arrangements of ways to
get high code scope. Its basic, direct way to deal with taking
care of the earth let it check a wide scope of framework
escalated programs [9].
System Monitor:
The system screens expect there are exceptional test
operators in the system that can send/get test bundles. The
system screen peruses the database and develops test bundles
and trains every specialists to send the suitable bundles.
Presently, test operators separate test parcels by IP Proto field
and TCP/UDP port number, however different fields, for
example, IP alternative, can likewise be utilized. On the off
chance that a portion of the tests fall flat, the screen chooses
extra test parcels from saved bundles to pinpoint the issue.
The process rehashes until the issue has been recognized. The
screen utilizes JSON to speak with the test operators, and
employments SQLite's string coordinating to lookup test
parcels productively [8][10].
III. PROPOSED WORK
We are proposing an empirical simulation model for network
monitoring which shows efficient path construction in terms
of signal strength and channel capacity and verifies three
rules at every node while transmission of packets and updates
the status to log. It in turn we can identify the status of the
path (whether it is passing the packets though node or port
number that is Forwarding rule) and verify the data packet
transmission without header change that is Link rule and
identifies the failure of packets at each node by the
acknowledgement from last node that is drop rule.
The main advantages with proposed system is Routing
implementation through signal strength and channel capacity
gives optimal path , rule based verification gives efficient
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International Journal of Engineering Trends and Technology (IJETT) – Volume 35 Number 2- May 2016
nodes failure acknowledgment and no need to ping the
network multiple times for node failure status
In node communication establishment module we
construct a general node to node communication through the
socket programming, Every node can communicate with each
other .data packet can be transmitted from source node to
destination node, each node acts as server, it can accept the
any connection and receives the data packets from any other
node and transmits the data packets to other nodes
Evolutionary approach for efficient cooperative
communication over nodes in network with the parameters
channel capacity and signal strength, it leads to the
communication cost between the nodes, here our approach
finds the optimal communication cost by applying the
process of path selection operation between the nodes, again
calculate the communication cost between the source and
destination nodes followed by relay nodes
To identify the correct routing and identify the failures while
transmission of data at port ends we are following rules like
forwarding rule, link rule and drop rule.
Forwarding Rule: A forwarding rule is behaving correctly if a
test packet exercises the rule and leaves on the correct port
with the correct header.
Link Rule: A link rule is a special case of a forwarding rule.
It can be tested by making sure a test packet passes correctly
over the link without header modifications.
Drop Rule: Testing drop rules is harder because we must
verify the absence of received test packets. We need to know
which test packets might reach an egress test terminal if a
drop rule was to fail
Input: Forward-rule set {Id, Snode, sport, dnode, dport,
P_size, packets, staus}
Output: Frule-Status, Lrule-Status-list, D-rule_Status
Cost model algorithm:
Step 1: initiate monitor thread
Obtains the optimal path which has the best communication
cost and transmits the data over the path.
Step1: Initially Source node selects the destination to
transmit data packets
Step2:
For (int i=0;i<total.no.ofnodes;i++)
If(Header(Node[i])1=Header(Node[i+1]))
Step2: Request received by the processing module and
generates the paths in topology
Begin
fruleStatus[i]=false;
Step3: The Processing module computes the path with their
signal strength and channel capacity
return Frule-Status[i];
Step4: Compute communication cost with signal strength
and channel capacity for fitness
Communication cost = Signal strength + channel capacity
Step5: select optimal path (optimal communication cost) and
transmits the data.
end
end If
Next
Step3:
For(int i=0;i<frule.length;i++)
If(S==fail)
Rules Verification:
Update node[i] status at monitor
Set Lrule-status-list[i]=true;
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International Journal of Engineering Trends and Technology (IJETT) – Volume 35 Number 2- May 2016
Next;
Monitoring console always maintains the process which
receives the path computed between source node and
destination node in terms of signal strength and channel
capacity, path forwarded to processing console by router and
every port end acknowledgement can be received, no need to
user pass commands to check the status, so even though
failure occurs at particular node, it can be identified in this
console and all rule verification status can be acknowledged
here.
Step4:
If(frules=true &Lrule=true)
begin
Acknowledge(Node[i]=true)
Drule=false;
Experimental Analysis:
end
For experimental analysis we implemented our
proposed mechanism in Java platform with some static
number of nodes, communicates through socket
programming. Every node initially make a request for
optimal path in terms of signal strength and channel capacity
for cost computation as follows
end if
Else
Drule=true;
Step 5: exit
8
7
6
5
Weight Based
4
Packet Generation Model
3
Proposed Model
2
1
0
Time complexity
Reliability
Our experimental results shows efficient
results than the traditional approaches in terms of route
computation and auto verification of the rules for
identification of failures of node or data packet, we compared
the paths and time complexity generated by various factors
like weight computation, data rating computation and our
proposed approaches, Our proposed results shows efficient
results than the previous approaches as follows and Time
complexity reduced than the traditional approaches, rate of
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Node failure Identification
success rate is more and improves the performance by
reducing response time.
IV.CONCLUSION
We have been concluding our research work with efficient
cache node failure verification with dynamic verification
model with rules integration to verify the packet delivery and
node failures at every intermediate level data transmission
and routing protocol based on signal strength and channel
capacity for calculation of communication cost. Our primary
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International Journal of Engineering Trends and Technology (IJETT) – Volume 35 Number 2- May 2016
factors give optimal performance than the traditional
approaches and improve the performance by reducing the
response time of the requested node and identify the node
failures dynamically.
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