Presence cloud

Department of Computer Science & Engineering
Shri Sant Gajanan Maharaj College of Engineering
Shegaon (444203)
“Presence Cloud based solution for on
demand data in wireless computing devices”
Guided by :
Prof. N. M. Kandoi
Submitted by:
Ms. Monali D. Akhare
M.E. 2nd year (Computer Engg)
1/16/20171
“Presence Cloud based solution for on demand
data in wireless computing devices”
Seminar on

OVERVIEW
1/16/20172
 Motivation
 Existing Work
 Analysis of Problem
 Proposed Work
 Implementation of Presence Cloud
-Modules
-Algorithm
 Flowchart
 Snapshot
 Result Analysis
 Applications
 Conclusion
 Future Scope
 References
 Dissemination of work

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• Social network services are growing and many
people are sharing digital resources in order
to facilitate, enhance or improve collaborative work.
• A mobile presence service is an essential component of social networking
applications as it keeps user presence information.
• If presence updates occur frequently, enormous number of message
distributed by servers may lead to scalability problem.
MOTIVATION
Cloud
Server-side Virtual World
Compute Power

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• To address this problem , Chi-Jen et la, [1]in 2013 propose an efficient
and scalable server architecture which is called Presence Cloud.
• Presence Cloud organizes presence servers into server-to-server
architecture.
• The performance can be analysed in terms of search cost and search
satisfaction level.

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• Objective is to propose an on demand QoS(Quality of service)
routing algorithm.
• The proposed approach has two phases namely:
-route discovery phase
-route maintenance phase
• This is first work that explicitly design a presence server architecture
that significantly outperforms those based distributed hash table.

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•3 popular commercial IM systems are : AIM[1],Microsoft MSN[8][13],
Yahoo! Messenger.
•They leverage some form of centralized clusters.
•Centralized clusters are used to provide presence services.
•Storing the presence is one of the most messaging traffic in these instant
messaging system.
EXISTING SYSTEM

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• All these IM services use central server architecture leads to
scalability problem at server side.
• Several studies have investigated the issues of user satisfaction in
several domians,including www search engine.
• There is no study of exploring the user satisfaction issues, such as
search response time, search precise etc,about mobile presence
service.
• So to address the problem, Presence Cloud organizes presence
servers into a quorum-based server-to-server architecture for
efficient presence searching.

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• Mobile ubiquity services is important element of cloud computing
environments.
• If presence updates occur often number of messages distributed by
presence server lead to scalability problem & buddy list search problem.
• To overcome scalability problem proposed an efficient and ascendable
server architecture called Presence Cloud.
• People are nomadic and mobile information is more mutable and
dynamic, so new design of mobile presence services is needed to
address the buddy-list search problem especially for the demand of
mobile social n/w application.
ANALYSIS OF PROBLEM

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PROPOSED SYSTEM
•Aim of proposed system is to design Peer–to–peer cloud server
architecture to remove centralized server.
•P2P reduces the maintenance costs and failures in server based
deployment.
•Presence Cloud is based on grid quorum based system the clients are
organized in DHT & size of Presence server node is O√m.
•The results demonstrate Presence-Cloud achieves major performance
gains in terms of reducing number of messages without sacrificing search
satisfaction.

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• There are 3 elements in presence cloud which run across presence
servers such as Presence Cloud Server overlay, One hop Caching
approach, and Directed buddy search .
Overview of Presence Cloud
IMPLEMENTATION

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1. Presence cloud server overlay
• This construction algorithm organizes ps nodes in to server – to –
server overlay.
• It provides a good low diameter property.
• It ensure that a ps node needs only two hops.
Presence Cloud Server Overlay
MODULES

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2. One hop caching
• To improve efficiency, presence cloud requires a caching strategy.
• In Presence Cloud, each PS node maintains a user list of presence
information of the attached users.
• The cache is updated when neighbors establishes a connection to it and
it updated periodically with it neighbors.
• Therefore, when any PS node receives a query, it can respond not only
with its own user list but also matches in the user lists offered by all of
its neighbors.

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3. Directed buddy search
• Minimizing the searching response time is important in presence
services.
• Thus, the buddy list searching algorithm of Presence Cloud coupled
with the two-hop overlay and one-hop caching strategy ensures that
Presence Cloud can typically provide swift responses for a large
number of mobile users.
• Clearly, this mechanism reduces
both network traffic and response
time.
Buddy list searching in Presence Cloud.

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ALGORITHM
Presence Cloud Maintenance Algorithm 1:
/* periodically verify PS node n’s pslist */
Definition:
pslist: set of the current PS list of this PS node, n
pslist[].connection: the current PS node in pslist
pslist[].id: identifier of the correct connection in pslist
node.id: identifier of PS node node
Algorithm:
r .Sizeof(pslist)
for i = 1 to r do
node .pslist[i].connection
if node.id ≠pslist[i].id then
/* ask node to refresh n’s PS list entries */

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findnode.Find_CorrectPSNode(node)
if findnode=nil then
pslist[i].connection.RandomNode(node)
else
pslist[i].connection.findnode
end if
else
/* send a heartbeat message */
bfailed.SendHeartbeatmsg(node)
if bfailed= true then
pslist[i].connection.RandomNode(node)
end if
end if
end for

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Directed Buddy Search Algorithm 2:
1.A mobile user logins Presence Cloud and decides the associated PS node,
q.
2.The user sends a Buddy List Search Message, B to the PS node q.
3.When the PS node q receives a B, then retrieves each bi from B and
searches its user list and one-hop cache to respond to the coming query.
And removes the responded buddies from B.
4. If B = nil, the buddy list search operation is done.
5.Otherwise, if B =nil, the PS node q should hash each remaining identifier
in B to obtain a grid ID, respectively.
6.Then, the PS node q aggregates these b(g) to become a new B(j), for each
g Sj. Here, PS node j is the intersection node of Sq intersection Sg. And
sends the new B(j) to PS node j.

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FLOWCHART
Dataflow Diagram

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SNAPSHOT
The Project is divided into two parts :
•Media Server
•Middle Server
SCREEN 1
Login Screen of
Media Server

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SCREEN 2
Login Successful

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SCREEN 3
Uploading Data

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SCREEN 4
Middle Server
(Creating User
Profile)

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SCREEN 5
Registration Page
of User

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SCREEN 6
Selection of
Starting Nodes

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SCREEN 7
Downloading Data
file through
Algorithm

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SCREEN 8
Saving file

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SCREEN 9
System Database
table showing
distance between
nodes

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SCREEN 10
Android Mobile app
(Video on demand)

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SCREEN 11
Creating User Profile

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SCREEN 12
Connecting nodes and
accessing data

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RESULT ANALYSIS
 Cost Analysis
•The communication cost of searching buddies and replicating presence
information can be formulated as:
Mcost = QMesh +RMesh,
- RMesh communication cost of replicating presence information to all PS
nodes, hence Mcost = O(n).
- QMesh, is only one message.
•The distance between the source node to destination node can be
calculated by using formula: 2((√n)-1)*v
•The maximum communication cost of the buddy list search problem is
O(√n), where n is the number of presence servers.

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SCREEN 13
Calculated Node
Path and Time
Required by
Normal
Algorithm

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SCREEN 14
Calculated Node
Path and Distance
using Our New
Algorithm

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 Performance Metrics
• Performance Metrics Within the context of the model, we measure the
performance of server architectures using the following three metrics:
-Total searching messages
-Average searching messages per-arrived user
-Average searching latency

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• Worst case-The mobile users are distributed equally among all the PS
nodes.
• Presence cloud is used to overcome the several types of existing
problems in presence services of mobility devices.
• The result of output shows that Presence Cloud is very much efficient
system when compare to the previous existing system.

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Efficiency Graph of Normal Algorithm vs Presence Cloud Algorithm
0
5
10
15
20
25
0 500 1000 1500 2000 2500
Pie chart showing Distance
Distance in Normal
Distance By Our Algo
Distance
Time (ms)

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Number Description Alternatives (If
available)
1 PC with 100 GB hard-disk and
2 GB RAM
Not-Applicable
2 PCs in Network
HARDWARE System Configuration

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Number Description Alternatives(If
available)
1 Windows 7/8/XP/linux with MS-
office
Not Applicable
2 Java(1.6), Java 1.5,
3 Eclipse 3.3 Neat Bean 7.4
4 Android Mobile
5 Tomcat server 7
6 Mysql Database Server 5.5
SOFTWARE System Configuration

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APPLICATIONS
• Server overlay and a directed buddy search algorithm are used to
achieve small constant search latency.
• The rationale behind the design of Presence Cloud is to distribute the
information of millions of users among thousands of presence servers
on the Internet, mobile telephones.
• Maps, Robot navigation, Urban traffic planning, Optimal pipelining of
VLSI chip, Subroutine in advanced algorithms, Routing of
telecommunications messages.

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 Presence Cloud, a scalable server architecture that supports mobile
presence services in large-scale social network.
 Scalability problem in server and buddy-list search problem is been
resolved.
 The results of simulations demonstrate that Presence Cloud achieves major
performance gains in terms of the search cost and search satisfaction
within Time.
CONCLUSION

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Presence is a powerful network capability that is useful for consumers,
for enterprises and for mobile operators.
Presence complements new business models in open mobile eco-
systems.
Application developers for Android, iPhone or Windows Mobile can
easily derive and use Presence to offer new social applications.
In the future, mobile devices will become more powerful, sensing, and
media capture devices.
FUTURE SCOPE

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REFERENCES
[1] Chi-Jen Wu, Jan-Ming Ho, Member, IEEE, and Ming-Syan Chen, Fellow, IEEE on “A
Scalable Server Architecture for Mobile Presence Services in Social Network
Applications”, 2013.
[2] R.B. Jennings, E.M. Nahum, D.P. Olshefski, D. Saha, Z.-Y.Shae, and C. Waters, “A Study
of Internet Instant Messaging and Chat Protocols,” IEEE Network, vol. 20, no. 6, pp. 16-
21, July/Aug.2006.
[3] Z. Xiao, L. Guo, and J. Tracey, “Understanding Instant Messaging Traffic
Characteristics,” Proc. IEEE 27th Int’l Conf. Distributed Computing Systems (ICDCS),
2007.
[4] C. Chi, R. Hao, D. Wang, and Z.-Z. Cao, “IMS Presence Server:Traffic Analysis and
Performance Modelling,” Proc. IEEE Int’lConf. Network Protocols (ICNP), 2008.
[5] A. Houri, S. Parameswar, E. Aoki, V. Singh, and H. Schulzrinne, “Scaling Requirements
for Presence in SIP/SIMPLE,” IETF Internet draft, 2009
[6] S.A. Baset, G. Gupta, and H. Schulzrinne, “Open VoIP: An Open Peer-to-Peer VoIP and
IM System,” Proc. ACM SIGCOMM, 2008.
[7] Open Mobile Alliance, “OMA Instant Messaging and Presence Service,” 2005
[8] W.-E. Chen, Y.-B.Lin, and R.-H. Liou, “A Weakly Consistent Scheme for

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IMS Presence Service,” IEEE Trans. Wireless Comm.,vol. 8, no. 7, pp. 3815-3821, July
2009.
[9] N. Banerjee, A. Acharya, and S.K. Das, “Seamless SIP-BasedMobility for Multimedia
Applications,” IEEE Network, vol. 20, no. 2, pp. 6–13, 2006.
[10] Kundan Singh and Henning Schulzrinne Department of Computer Science, Columbia
University {kns10,hgs}@cs.columbia.edu, “SIPPEER : A session iniiation protocol
(SIP)-baed peer-to-peer internet telephony cllient adaptor” .
[11] Michael Piatek, Tomas Isdal, Arvind Krishnamurthy , and Thomas Anderson “One hop
Reputations for Peer to Peer FileSharing Workloads”.
[12] Brent Hecht, Jaime Teevan , Meredith Ringel Morris, and Dan Liebling, “SearchBuddies:
Bringing Search Engines into theConversation”,2012.
[13] K. Singh and H. Schulzrinne, ”Peer-to-peer internet telephony using sip,” Proc. of ACM
NOSSDVA, 2005.
[14] P. Saint-Andre, ”Interdomain presence scaling analysis for the extensible messaging and
presence protocol (xmpp),” RFC Internet Draft, 2008.
[15] A. Houri, T. Rang, and E. Aoki, ”Problem statement for sip/simple,” RFC Internet-Draft,
2009.
[16] A. Houri, S. Parameswar, E. Aoki, V. Singh, and H. Schulzrinne, ”Scaling requirements
for presence in sip/simple,” RFC Internet- Draft, 2009.

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[17] J. Rosenberg, H. Schulzrinne, G. Camarillo, A. Johnston, J. Peterson, R. Sparks, M.
Handley, and E. Schooler, ”Sip: Session initiation protocol,” RFC 3261, 2002.
[18] P. Bellavista, A. Corradi, and L. Foschini, ”Ims-based presence service with enhanced
scalability and guaranteed qos for inter domain enterprise mobility,” IEEE Wireless
Communications, 2009.
[19] A. Houri, E. Aoki, S. Parameswar, T. Rang, , V. Singh, and H. Schulzrinne, ”Presence
interdomain scaling analysis for sip/simple,” RFC Internet-Draft, 2009.
[20] M. Maekawa, ”Ap n algorithm for mutual exclusion in decentralized systems,” ACM
Transactions on Computer Systems, 1985.
[21] D. Eastlake and P. Jones, ”Us secure hash algorithm 1 (SHA1),” RFC 3174, 2001.
[22] M. Steiner, T. En-Najjary, and E. W. Biersack, ”Long term study of peer behavior in the
kad DHT,” IEEE/ACM Trans. Netw., 2009.
[23] K. Singh and H. Schulzrinne, ”Failover and load sharing in sip telephony,” International
Symposium on Performance Evaluation of Computer and Telecommunication Systems,
July 2005. 2011 14
[24] I. Stoica, R. Morris, D. Karger, M. F. Kaashoek, and H. Balakrishnan, ”Chord: A
scalable peer-to-peer lookup service for internet,” IEEE/ACM Tran. on Networking, 2003.
[25] X. Chen, S. Ren, H. Wang, and X. Zhang, ”Scope: scalable consistency maintenance in
structured p2p systems,” Proc. of IEEE INFOCOM, 2005.
[26] K. P. Gummadi, S. Saroiu, and S. D. Gribble., ”King: Estimating latency between
arbitrary internet end hosts,” Proc. of ACM IMW, 2002.

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Published:
1. Monali.D.Akhare and Prof.N.M.Kandoi “AN ANALYSIS OF PRESENCE CLOUD BASED
SOLUTION FOR ON DEMAND DATA IN WIRELESS COMPUTING DEVICES”, International
Journal of Research in Computer & Information Technology, Vol.1, Special Issue 1, 2016,45th
International Conference held in Amravati ISTE,January-2016.
2. Monali.D.Akhare and Prof.N.M.Kandoi “A Survey on Presence Cloud based solution for on demand
data in wireless computing devices”,IJR volume 2,Issue 10,October 2015.
3. Monali.D.Akhare and Prof.N.M.Kandoi “Reviewing the Problem for Presence Cloud Based
Solution for on Demand Data in Wireless Computing Devices”, International Journal on Recent and
Innovation Trends in Computing and Communication ISSN: 2321-8169 Volume: 4 Issue: Jan,2016.
4. Monali.D.Akhare and Prof.N.M.Kandoi “Accessing Data by using Presence Cloud based solution
for on demand services in wireless computing devices” International Conference on Computational
Modeling and Security (CMS 2016) Elsevier Procedia & Science
Direct,doi:10.1016/j.procs.2016.05.270.
DISSEMINATION OF WORK

1/16/2017
45

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