International Journal On Engineering Technology and Sciences – IJETS™

ISSN(P): 2349-3968, ISSN (O): 2349-3976

Volume III, Issue V, May- 2016

INTEGRATING THE K-OUT-OF-N COMPUTING FRAMEWORK

FOR ENERGY EFFICIENT AND FAULT TOLERANT IN MOBI-
CLOUD
1
Ms P.Sugeertha, 2 Mrs.P.Umarani,
Department of Computer Science and Engineering,
SSM College of Engineering, Komarapalayam,
Tamil Nadu, India.
1
Sugeertha8@gmail.com, 2arunaiuma@gmail.com

ABSTRACT
In Personal mobile devices have gained enormous popularity in recent years. Due to their limited
resources (e.g., computation, memory, energy), however, executing sophisticated applications (e.g.,
video and image storage and processing, or map-reduce type) on mobile devices remains challenging
because it need to maintain power consumption. Due to lacking of power in cloud computing of mobile
devices, if any node becomes failure then, entire network of mobile communication may disordered. In
this solution, mobile devices successfully retrieve or process data, in the most energy-efficient way, as
long as k out of n remote servers are accessible. Through a real system implementation the method
proves the feasibility of the proposed approach. Extensive simulations are demonstrated with fault
tolerance and energy efficiency performance of the framework in larger scale networks The integrate k-
out-of-n reliability mechanism into distributed computing in mobile cloud formed by only mobile
devices. K-out-of-n, a well-studied topic in reliability control, ensures that a system of ‗n‘ components
operates correctly as long as k or more components work. More specifically, we investigate how to store
data as well as process the stored data in mobile cloud with k-out of-n reliability such that: 1) The
energy consumption for retrieving distributed data is minimized; 2) The energy consumption for
processing the distributed data is minimized and 3) Data and processing are distributed considering
dynamic topology changes.
Key word: Personal mobile devices, power consumption, fault tolerances, M-K-Out-Data
Allocation, M-K-out-Data Processing

1.INTRODUCTION cloud services include online file storage, social

Cloud computing is the delivery of networking sites, web mail, and online business
computing services over the Internet. Cloud applications. The cloud computing model allows
services allow individuals and businesses to use access to information and computer resources
software and hardware that are managed by from anywhere that a network connection is
third parties at remote locations. Examples of available. Cloud computing provides a shared

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ISSN(P): 2349-3968, ISSN (O): 2349-3976
Volume III, Issue V, May- 2016

pool of resources, including data storage space, network. They are integrating the k-out-of-n
networks, computer processing power, and reliability mechanism into distributed
specialized corporate and user applications. computing in mobile cloud formed by only
Mobile Cloud Computing (MCC) is the mobile devices. k-out-of-n, a well-studied topic
combination of cloud computing, mobile in reliability control to ensures that a system of
computing and wireless networks to bring rich n components operates correctly as long as k or
computational resources to mobile users, more components work. More specifically, they
network operators, as well as cloud computing are study investigate how to store data as well as
providers. The ultimate goal of MCC is to process the stored data in mobile cloud with k-
enable execution of rich mobile applications on out-of-n reliability
a plethora of mobile devices, with a rich user
experience. MCC provides business In paper our proposed study framework, a
opportunities for mobile network operators as data object is encoded and partitioned into n
well as cloud providers. fragments, and then stored on n different nodes.
In MCC, there are four types of cloud based As long as k or more of the n nodes are
resources, namely distant immobile clouds, available, the data object can be successfully
proximate immobile computing entities, recovered. Similarly, another set of n nodes are
proximate mobile computing entities, and assigned tasks for processing the stored data and
hybrid (combination of the other three models). all tasks can be completed as long as k or more
Giant clouds such as Amazon EC2 are in the of the n processing nodes finish the assigned
distant immobile groups whereas cloudlet or tasks. The parameters k and n determine the
surrogates are member of proximate immobile degree of reliability and different k; n pairs may
computing entities. Smart phone‘s, tablets, be assigned to data storage and data processing.
handheld devices, and wearable computing The following main contribution of the papers,
devices are part of the third group of cloud-  Under utilization: Typical deployments
based resources which is proximate mobile have very low utilization of total computing
computing entities. capacity. Users want to run only a few
In this research study the first framework to applications per computer to obtain better
support fault-tolerant and energy-efficient response time. As a result, many computers are
remote storage and processing under a dynamic under-utilized.
network topology, i.e., mobile cloud. The  Security: Desktops are of ten managed
research framework aims for applications that by individual users and they have to regularly
require energy efficient and reliable distributed
data storage and processing in dynamic
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ISSN(P): 2349-3968, ISSN (O): 2349-3976
Volume III, Issue V, May- 2016

apply security patches. Otherwise, the recent years, becoming ever more sophisticated
computers can become vulnerable. and capable. As a result, developers worldwide
 Operational costs: The total cost of are building increasingly complex applications
ownership can grow rapidly for supporting that require ever increasing amounts of
increasing numbers of desktops and laptops, and computational power and energy. They propose
for upgrading and up- dating software. ThinkAir, a framework that makes it simple for
Moreover, these computers may waste power as developers to migrate their smartphone
they are often kept on 24 h. applications to the cloud. ThinkAir exploits the
 The objective of this problem is to find n concept of smartphone virtualization in the
nodes in V as process n nodes such that energy cloud and provides method-level computation
consumption for processing a job of M tasks is offloading. Advancing on previous work, it
minimized. focuses on the elasticity and scalability of the
cloud and enhances the power of mobile cloud
computing by parallelizing method execution
2.RELATED WORKS
using multiple virtual machine (VM) images.
In this paper, the authors explained that
In this paper, they propose ThinkAir, a new
mobile applications are becoming increasingly
mobile cloud computing framework which takes
ubiquitous and provide ever richer functionality
the best of the two worlds.ThinkAir addresses
on mobile devices. At the same time, such
MAUI‘s lack of scalability by creating virtual
devices often enjoy strong connectivity with
machines (VMs) of a complete smartphone
more powerful machines ranging from laptops
system on the cloud, and removes the
and desktops to commercial clouds. This paper
restrictions on
presents the design and implementation of
applications/inputs/environmental conditions
CloneCloud, a system that automatically
that CloneCloud induces by adopting an online
transforms mobile applications to benefit from
method-level offloading. Moreover, ThinkAir
the cloud. The system is a flexible application
provides an efficient way to perform on-demand
partitioner and execution runtime that enables
resource allocation, and exploits parallelism by
unmodified mobile applications running in an
dynamically creating, resuming, and destroying
application-level virtual machine to seamlessly
VMs in the cloud when needed.
off-load part of their execution from mobile
In this paper [4] describe the mobile
devices onto device clones operating in a
devices are increasingly being relied on for
computational cloud.
services that go beyond simple connectivity and
In this paper, the authors explained that
require more complex processing. Fortunately, a
Smartphones have exploded in popularity in
mobile device encounters, possibly
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ISSN(P): 2349-3968, ISSN (O): 2349-3976
Volume III, Issue V, May- 2016

intermittently, many entities capable of lending load and limit hops to both stationary and
it computational resources. At one extreme is mobile HPC nodes.
the traditional cloud-computing context where a 2.1 EXISTING SYSTEM
mobile device is connected to remote cloud
resources maintained by a service provider with The existing presents a mathematical
which it has an established relationship. In this model for both optimizing energy consumption
paper they consider the other extreme, where a and meeting the fault tolerance requirements of
mobile device‘s contacts are only with other data storage and processing under a dynamic
mobile devices, where both the computation network topology. This paper presents an
initiator and the remote computational resources efficient algorithm for estimating the
are mobile, and where intermittent connectivity communication cost in a mobile cloud, where
among these entities is the norm. nodes fail or move, joining/leaving the network.
In this paper [5] describe the
geospatially aware mobile devices, such as The project proposed a first process

smart phones, rely on an architecture that is scheduling algorithm that is both fault-tolerant

power con-strained and processing power and energy efficient. It presents a distributed

limited. The utility of these devices can be protocol for continually monitoring the network

increased by offloading compute-intensive topology, without requiring additional packet

applications to parallel high performance transmissions. The evaluation of this proposed

computing (HPC) architectures, thus limiting framework processed through a real hardware

battery drain, allowing access to large data, and implementation and large scale simulations.

providing faster time to solution. Such a

paradigm can be achieved through tactical The framework, running on all mobile
cloudlets that must operate in environments nodes, provides services to applications that aim
dominated by mobile ad-hoc infrastructure to: (1) store data in mobile cloud reliably such
(common in remote environments or military that the energy consumption for retrieving the
applications). Executing this paradigm is data is minimized (k-out-of-n data allocation
further complicated in that HPC nodes problem); and (2) reliably process the stored
themselves (with some reduced mobility) are data such that energy consumption for
now deployable through the use of ruggedized processing the data is minimized (k-out-of-n
hybrid core technologies. This paper discusses data processing problem).
their concept for cloudlet seeding: the static
strategic placement of HP Cassets in deployed DRAWBACKS
settings in such a way to balance computational
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ISSN(P): 2349-3968, ISSN (O): 2349-3976
Volume III, Issue V, May- 2016

 If n-k node fails before the tasks provide functions that take the stored data as
completion, the task completion ratio is inputs.
less than 1 since single cloud provider
Each function is instantiated as multiple
scenario is higher.
tasks that process the data simultaneously on
 Same execution cost is applied for both
different nodes. Nodes executing tasks are
nodes with fixed power supply and nodes
processor nodes; we call a set of tasks
powered by battery.
instantiated from one function a job. Client
 Multiple cloud provider scenarios with
nodes are the nodes requesting data allocation or
different execution cost for same task is
processing operations. A node can have any
not considered.
combination of roles from: storage node,
 Storage of node consideration becomes
processor node, or client node, and any node can
failure for data processing.
retrieve data from storage nodes. It considered
 Optimizing the energy efficient becomes
Topology Discovery and Monitoring, Failure
tedious for calculating nodes availability.
Probability Estimation, Expected Transmission
 Failure of node with temporary and
Time (ETT) Computation, k-out-of-n Data
permanent disorder for calculating energy
Allocation and k-out-of-n Data Processing.
consumption.
2.2 PROPOSED SYSTEM
In general, each node may have different
The proposed system includes all the
energy cost depending on their energy sources;
existing system approach which covers multiple
e.g., nodes attached to a constant energy source
cloud service provider environments with
may have zero energy cost while nodes powered
different execution cost for same task. In
by battery may have relatively high energy cost.
general, each node may have different energy
For simplicity, we assume the network is
cost depending on their energy sources; e.g.,
homogeneous and nodes consume the same
nodes attached to a constant energy source may
amount of energy for processing the same task.
have zero energy cost while nodes powered by
As a result ,only the transmission energy affects
battery may have relatively high energy cost.
the energy efficiency of the final solution. We

The applications generate data and our leave the modeling of the general case as future

framework stores data in the network. For work

higher data reliability and availability, each data

is encoded and partitioned into fragments; the In existing system, it is assumed the

fragments are distributed to a set of storage network is homogeneous and nodes consume

nodes. In order to process the data, applications the same amount of energy for processing the

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ISSN(P): 2349-3968, ISSN (O): 2349-3976
Volume III, Issue V, May- 2016

same task. As a result, only the transmission The framework, running on all mobile
energy affects the energy efficiency of the final nodes, provides services to applications that aim
solution. But in proposed system, different to:
execution cost for same task scenario is  Store data in mobile cloud reliably such
considered which resembles the real time case. that the energy consumption for retrieving
ADVANTAGES the data is minimized (k-out-of-n data
 Even If n-k node fails before the tasks allocation problem);
completion, the task completion ratio may be  Reliably process the stored data such that
equal to 1 since multiple cloud provider energy consumption for processing the data
scenario. is minimized (k-out-of-n data processing
 Different execution cost is applied for both problem).
nodes with fixed power supply and nodes The application running in a mobile ad-
powered by battery. hoc network may generate a large amount of
 Multiple cloud provider scenarios with media files and these files must be stored
different execution cost for same task is reliably such that they are recoverable even if
considered. certain nodes fail. At later time, the application
 Improve a mathematical model for both may make queries to files for information such
optimizing energy consumption and meeting as the number of times an object appears in a set
the fault tolerance requirements of data of images. Without loss of generality, we
storage and processing under a dynamic assume a data object is stored once, but will be
network topology. retrieved or accessed for processing multiple
 An efficient algorithm for estimating the times later. They first define several terms. As
communication cost in a mobile cloud, where shown in Fig. 1, applications generate data and
nodes fail or move, joining/leaving the our framework stores data in the network. For
network. higher data reliability and availability, each data

 Scheduling algorithm that is both fault- is encoded and partitioned into fragments; the

tolerant and energy efficient. fragments are distributed to a set of storage

 A distributed protocol for continually nodes. In order to process the data, applications

monitoring the network topology, with provide functions that take the stored data as

requiring additional packet transmissions. inputs. Each function is instantiated as multiple

tasks that process the data simultaneously on

3. ENERGY ARCHITECTURE MODEL different nodes. Nodes executing tasks are

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 International Journal On Engineering Technology and Sciences – IJETS™
ISSN(P): 2349-3968, ISSN (O): 2349-3976
Volume III, Issue V, May- 2016

processor nodes; we call a set of tasks retrieving k fragments by any node is

instantiated from one function a job. minimized. K is the minimal number of
fragments required to recover a data. If an
Client nodes are the nodes requesting application needs to process the data, the k-
data allocation or processing operations. A node outof-n Data Processing component creates a
can have any combination of roles from: storage job of M tasks and schedules the tasks on n
node, processor node, or client node, and any processor nodes such that the energy
node can retrieve data from storage nodes. As consumption for retrieving and processing these
shown in Fig. 1, our framework consists of five data is minimized. This component ensures that
components: Topology Discovery and all tasks complete as long as k or more
Monitoring, Failure Probability Estimation, processor nodes finish their assigned tasks. The
Expected Transmission Time (ETT) Topology Discovery and Monitoring component
Computation, k-out-of-n Data Allocation and k- continuously monitors the network for any
out-of-n Data Processing. When a request for significant change of the network topology. It
data allocation or processing is received from starts the Topology Discovery when necessary.
applications, the Topology Discovery and
Monitoring component provides network
topology information and failure probabilities of
nodes.
The failure probability is estimated by
the Failure Probability component on each node.
Based on the retrieved failure probabilities and
network topology, the ETT Computation
component computes the ETT matrix, which
represents the expected energy consumption for
communication between any pair of node.
Given the ETT matrix, our framework finds the
locations for storing fragments or executing FIG. 1 Overview Of Our Proposed Framework
tasks. Is Depicted
3.1 FORMULATION OF K-OUT-OF-N
The k-out-of-n Data Storage component DATA ALLOCATION
partitions data into n fragments by an erasure
code algorithm and stores these fragments in the In this problem, we are interested in
network such that the energy consumption for finding n storage nodes denoted by S ¼ f g s1;

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ISSN(P): 2349-3968, ISSN (O): 2349-3976
Volume III, Issue V, May- 2016

s2; . . . sn ; S V such that the total expected coding, it first needs to retrieve and decode k
transmission cost from any node to its k closest data fragments because nodes can only process
storage nodes — in terms of ETT—is the decoded plain data object, but not the
minimized. We formulate this problem as an encoded data fragment.
ILP in Eqs. (1), (2), (3), (4), and (5). In general, each node may have different
energy cost depending on their energy sources;
e.g., nodes attached to a constant energy source
may have zero energy cost while nodes powered
by battery may have relatively high energy cost.
For simplicity, we assume the network is
homogeneous and nodes consume the same
amount of energy for processing the same task.
As a result, only the transmission energy affects
the energy efficiency of the final solution. They
leave the modeling of the general case as future
The first constraint (Eq. (2)) selects work. Before formulating the problem, we
exactly n nodes as storage nodes; the second define some functions: (1) f1(i) returns 1 if node
constraint (Eq. (3)) indicates that each node has i in S has at least one task; otherwise, it returns
access to k storage nodes; the third constraint 0; (2) f2(j) returns the number of instances of
(Eq (4)) ensures that jth column of R can have a task j in S; and (3) f3(z; j) returns the
non-zero element if only if Xj is 1; and transmission cost of task j when it is scheduled
constraints (Eq (5)) are binary requirements for for the zth time. We now formulate the k out of
the decision variables. n data processing problem.
The objective function minimizes the
3.2 FORMULATION OF K-OUT-OF-N total transmission cost for all processor nodes to
DATA PROCESSING retrieve their tasks. L represents the time slot of
The objective of this problem is to find n executing a task; i is the index of nodes in the
nodes in V as processor nodes such that energy network; j is the index of the task of a job. We
consumption for processing a job of M tasks is note here that T r, the Data Retrieval Time
minimized. In addition, it ensures that the job Matrix, is a N M matrix, where the element T r
can be completed as long as k or more ij corresponds to the estimated time for node i to
processors nodes finish the assigned tasks. retrieve task j. T r is computed by summing the
Before a client node starts processing a data transmission time (in terms of ETT available in
object, assuming the correctness of erasure

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ISSN(P): 2349-3968, ISSN (O): 2349-3976
Volume III, Issue V, May- 2016

D) from node i to its k closest storage nodes of 3.3 K-OUT-OF-N DATA ALLOCATION
the task. After the ETT matrix is computed, the k-out-
of-n data allocation is solved by ILP solver. A
simple example of how the ILP problem is
formulated and solved is shown here.
Considering Fig. 2b, distance Matrix D is a 4 4
symmetric matrix with each component Dij
indicating the expected distance between node i
and node j. Let‘s assume the expected
transmissions times on all edges are equal to 1.
As an example, D23 is calculated by finding the
The first constraint ensures that n nodes
probability of two possible paths: 2 ! 1 ! 3 or 2 !
in the network are selected as processor nodes.
4 ! 3. The probability of 2! 1 ! 3 is 0:8 0:8 0:9
The second constraint indicates that each task is
0:4 ¼ 0:23 and the probability of 2! 4! 3 is 0:8
replicated k - 1 times in the schedule such that
0:6 0:9 0:2 ¼ 0:08.
any subset of k processor nodes must contain at
least one instance of each task. The third
Another possible case is when all nodes
constraint states that each task is replicated at
survive and either path may be taken. This
most once to each processor node. The fourth
probability is 0:8 0:8 0:6 0:9 ¼ 0:34. The
constraint ensures that no duplicate instances of
probability that no path exists between node 2
a task execute at the same time on different
and node 3 is (1-0.23-0.08-0.34 ¼ 0.35). They
nodes. The fifth constraint ensures that a set of
assign the longest possible ETT ¼ 3, to the case
all tasks completed at earlier time should
when two nodes are disconnected. D23 is then
consume lower energy than a set of all tasks
calculated as 0:23 2 þ 0:08 2 þ 0:34 2 þ 0:35 3 ¼
completed at later time. In other words, if no
2:33. Once the ILP problem is solved, the binary
processor node fails and each task completes at
variables X and R give the allocation of data
the earliest possible time, these tasks should
fragments. In our solution, X shows that nodes
consume the least energy.
1-3 are selected as storage nodes; each row of R
indicates where the client nodes should retrieve
the data fragments from. For example, the first
row of R shows that node 1 should retrieve data
fragments from nodes 1 and 3.

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ISSN(P): 2349-3968, ISSN (O): 2349-3976
Volume III, Issue V, May- 2016

that the jth column of R can have a non-zero

element if only if X j is 1; and the constraints
are binary requirements for the decision
variables.
Once processor nodes are determined,
we proceed to the Task Scheduling stage. In this
stage, the tasks assigned to each processor node
are scheduled such that the energy and time for
3.4 K-OUT-OF-N DATA PROCESSING finishing at least M distinct tasks is minimized,
The k-out-of-n data processing problem is meaning that we try to shorten t he job
solved in two stages—Task Allocation and Task completion time while minimizing the overall
Scheduling. In the Task Allocation stage, n energy consumption. The problem is solved in
nodes are selected as processor nodes; each three steps.
processor node is assigned one or more tasks;
each task is replicated to n k þ 1 different
processor nodes. However, not all instances of a
task will be executed—once an instance of the
task completes, all other instances will be
canceled.
The ask allocation can be formulated as
an ILP .In the formulation, Rij is a N M matrix
which predefines the relationship between
processor nodes and tasks; each element Rij is a First, they find the minimal energy for
binary variable indicating whether task j is executing M distinct tasks in Rij. Second, they
assigned to processor node i. X is a binary find a schedule with the minimal energy that h
vector containing processor nodes, i.e., Xi ¼ 1 as the shortest completion time, tasks 1 to 3 are
indicates that vi is a processor node. scheduled on different nodes at time slot 1;
The objective function minimizes the however, it is also possible that tasks 1 through
transmission time for n processor nodes to 3 are allocated on the same node, but are
retrieve all their tasks. The first constraint scheduled in different time slots. These two
indicates that n of the N nodes will be selected steps are repeated n-k+1 times and M distinct
as processor nodes. The second constraint tasks are scheduled upon each iteration. The
replicates each task to ðn k þ 1Þ different third step is to shift tasks to earlier time slots.
processor nodes. The third constraint ensures

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ISSN(P): 2349-3968, ISSN (O): 2349-3976
Volume III, Issue V, May- 2016

A task can be moved to an earlier time

slot as long as no duplicate task is running at the
same time, task 1 on node 6 can be safely
moved to time slot 2 because there is no task 1
scheduled at time slot 2. The ILP problem
shown in Eqs. (17), (18), (19), and (20) finds M
unique tasks from Rij that have the minimal
transmission cost. The decision variable W is an
N M matrix where R ij ¼ 1 indicates that task j
is selected to be executed on processor node i.
The first constraint ensures that each
ALGORITHM : SCHEDULE RE-
task is scheduled exactly one time. The second
ARRANGEMENT
constraint indicates that Wij can be set only if
L ¼ last time slot in the schedule
task j is allocated to node i in Rij. The last
for time t ¼ 2! L do
constraint is a binary requirement for decision
for each scheduled task J in time t do
matrix W.
n processor node of task J
The objective function minimizes integer
while n is idle at t 1 AND
variable Y, which is the largest number of tasks
J is NOT scheduled on any node at t 1 do
on one node. Wij is a decision variable similar to
Move J from t to t 1t ¼ t 1
W ij defined previously. The first constraint
end while end for
ensures that the schedule cannot consume more
end for
energy that the Emin calculated previously. The
TOPOLOGY MONITORING
second constraint schedules each task exactly
The Topology Monitoring component monitors
once. The third constraint forces Y to be the
the network topology continuously and runs in
largest number of tasks on one node. The last
distributed manner on all nodes. Whenever a
constraint is a binary requirement for decision
client node needs to create a file, the Topology
matrix W.Once tasks are scheduled, we then
Monitoring component provides the client with
rearrange tasks—tasks are moved to earlier time
the most recent topology information
slots as long as there is free time slot and no
immediately. When there is a significant
same task is executed on other node
topology change, it notifies the framework to
simultaneously. Algorithm 1 depicts the
update the current solution. We first give several
procedure
notations. A term s refers to a state of a node,
which can be either U or NU. The state becomes

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ISSN(P): 2349-3968, ISSN (O): 2349-3976
Volume III, Issue V, May- 2016

U when a node finds that its neighbor table has

drastically changed; otherwise, a node keeps the ALGORITHM : DISTRIBUTED
state as NU. TOPOLOGY MONITORING
They let p be the number of entries in
the neighbor table that has changed. A set ID At each beacon interval:
contains the node IDs with p greater than t1, a if p > t1 and s 6¼ U then
threshold parameter for a ―significant‖ local sU
topology change. The Topology Monitoring Put þ ID to a beacon message.
component is simple yet energy-efficient as it end if
does not incur significant communication if p t1 and s ¼ U then
overhead—it simply piggybacks node ID on a s NU
beacon message. The protocol is depicted in Put ID to a beacon message.
Algorithm 2. We predefine one node as a end if
topology delegate Vdel who is responsible for Upon receiving a beacon message on Vi:
maintaining the global topology information. If for each ID in the received beacon message do
p of a node is greater than the threshold t1, the if ID > 0 then
node changes its state to U and piggybacks its ID ID S fIDg:
ID on a beacon message. else
Whenever a node with state U finds that ID ID n fIDg:
its p becomes smaller than t1, it changes its state end if
back to NU and puts ID in a beacon message. end for
Upon receiving a beacon message, nodes check if j j f g ID > t2 then
the IDs in it. For each ID, nodes add the ID to Notify Vdel and Vdel initiate topology
set ID if the ID is positive; otherwise, remove discovery
the ID. If a client node finds that the size of set end if
ID becomes greater than t2, a threshold for Add the ID in V 0
―significant‖ global topology change, the node s beacon message.
notifies Vdel; and Vdel executes the Topology RESULT AND DISCUSSION
Discovery protocol. To reduce the amount of
4. EXPERIMENTAL RESULS
traffic, client nodes request the global topology
The Table 7.1 represents experimental
from Vdel, instead of running the topology
result for Single K-Out-N-Algorithm and M-K-
discovery by themselves. After Vdel completes
the topology update, all nodes reset their status Out-N-Algorithm model. The table contains
variables back to NU and set p ¼ 0.
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Volume III, Issue V, May- 2016

finding Data storage node count in K-Out-N and

Stroage Node Analysis
M-K-Out-N- Algorithm model within 1000 sec MCP-SCP Model
90
80
time interval details as shown. 70
AVG 60 MCP-Storage
Storage 50 Node
40
S.N Num M-K-OUT-N- K-OUT-N- node [%]
30
SCP-Storage
Node
20
10
O ber of ALGORITH ALGORITH 0
1 2 3 4 5 6 7 8 9
Data M M Number of Storage Node

Stora MCP Selecti SCP- Selecti

ge - on of Stora on of
Selection for Storage Node
node Stora Storag ge Storag
Analysis MCP-SCP Model
(Cou ge e Node e 100

AVG 80
nt) Node Node Node Selection 60 Selection of
Storage Storage MCP
40
Node [%] Node
1 100 16 22 13 17 20 Selection of
Storage SCP-
0 Node
2 200 32 35 29 30 1 2 3 4 5 6 7 8 9

Number of Storage
3 300 42 49 38 44 Node

4 400 56 62 50 56
5 500 63 72 56 65 Figure 4.1 Selection Storage Node MEP-SCP
6 600 71 83 66 80 Model
The Table 4.2 represents experimental result
7 700 79 89 70 85
for Single K-Out-N-Algorithm and M-K-Out-N-
8 800 85 92 78 86
Algorithm mode time analysis. The table

Table 7.1 Selection of Storage Node Analysis contains Data storage node allocation time

analysis for K-Out-N and M-K-Out-N-

The Figure 7.1 represents experimental
Algorithm model within 1000 sec time interval
result for Single K-Out-N-Algorithm and M-K-
details as shown.
Out-N-Algorithm model. The figure contains

finding Data storage node count in K-Out-N and S. Data K-OUT-N M-K-

M-K-Out-N- Algorithm model within 1000 sce No Storage [sec] OUT-N

time interval details as shown. Node [n] [sec]

1 30 20 25

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 International Journal On Engineering Technology and Sciences – IJETS™
ISSN(P): 2349-3968, ISSN (O): 2349-3976
Volume III, Issue V, May- 2016

2 40 30 35 figure contains Data storage node allocation

3 50 40 45 time analysis for K-Out-N and M-K-Out-N-

4 60 50 55 Algorithm model within 1000 sec time interval

5 70 60 65 details as shown.

6 80 70 75
Data storage Node- Time Analysis
7 90 80 85
140
8 100 90 95 120

AVG Time[sec]
100
9 110 100 105 80 K-OUT-N
60 M-K-OUT-N
10 120 110 116 40
20
Table 4.2 Data storage Node- Time Analysis 0
1 2 3 4 5 6 7 8 9 10 11
No.of.Storage Node
The Fig 4.2 represents experimental

result for Single K-Out-N-Algorithm and M-K- Fig 4.2 Data storage Node- Time Analysis

Out-N-Algorithm mode time analysis. The

CONCLUSION AND FUTURE simulations in larger scale networks proved the

effectiveness of this thesis solution.
ENHANCEMENTS
In this solution, mobile devices successfully

CONCLUSION retrieve or process data, in the most energy-

efficient way, as long as k out of n remote servers
In this thesis work presented the first k-out-
are accessible. Through a real system
of-n framework that jointly addresses the energy-
implementation the method proves the feasibility
efficiency and fault-tolerance problem overcome.
of the proposed approach. Extensive simulations
It assigns data fragments to nodes such that other
are demonstrated with fault tolerance and energy
nodes retrieve data reliably with minimal energy
efficiency performance of the framework in larger
consumption. It also allows nodes to process
scale networks with multi cloud provider.
distributed data such that the energy consumption
FUTURE ENHANCEMENTS
for processing the data is minimized. Through
In the future, to utilize the inferred
system implementation, the feasibility of our
information and extend the framework for
solution on real hardware was validated. Extensive
efficient and effective Cloud services

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 International Journal On Engineering Technology and Sciences – IJETS™
ISSN(P): 2349-3968, ISSN (O): 2349-3976
Volume III, Issue V, May- 2016
monitoring and application design. The new [2]. [Tolia 2006] N. Tolia, M. Kaminsky, D. G.
system become useful if the below Andersen, and S. Patil. An architecture for
enhancements are made in future. Internet data transfer. In NSDI, 2006.
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