This document summarizes a student's seminar presentation on a proposed "Presence Cloud" system for wireless devices. The system would use a cloud-based server architecture to efficiently store and search for user presence information across large numbers of mobile users. It describes the existing centralized server architectures used by IM systems that do not scale well. The proposed Presence Cloud system would organize servers in a peer-to-peer overlay network with one-hop caching and a directed search algorithm to achieve small constant search times. The performance of the system would be evaluated based on search costs, search messages, and latency.

1. 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
27/10/20151
“Presence Cloud based solution for on demand
data in wireless computing devices”
Seminar on

2. OVERVIEW
27/10/20152
“Presence Cloud based solution for on demand
data in wireless computing devices”
 Abstract
 Existing System
 Analysis of Problem
 Proposed System Introduction
-Presence Cloud server overlay
-One hop caching
-Directed buddy search
 Hardware System Configuration
 Software System Configuration
 Applications
 Conclusion & References

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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.
• It is essential because it maintains each mobile user’s presence
information, such as current status (online/offline), GPS location and
network address.
• If presence updates occur frequently, enormous number of message
distributed by servers may lead to scalability problem.
ABSTRACT
Cloud
Server-side Virtual World
Compute Power

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• Most of the previous algorithms are used to address fixed object
searching problem in distributed system for different intentions.
• To address this problem , Chi-Jen et la, [1]in 2013 propose an efficient
and scalable server architecture which is called Presence Cloud.
• The performance can be analysed
in terms of search cost and search
satisfaction level. Implementation Flowchart

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• 3 popular commercial IM systems are :
 AIM
 Microsoft MSN
 Yahoo! Messenger.
EXISTING SYSTEM

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• They leverage some form of Centralized clusters which are used to
provide presence services.
• Storing the presence is one of the most messaging traffic in these instant
messaging system.
Overview of IM traffic

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• All these IM services use central server architecture leads to
scalability problem at server side.
• 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
in large-scale mobile presence services.
• To overcome scalability problem proposed an efficient and ascendable
server architecture called Presence Cloud.
• When a mobile user joins a network or internet, presence cloud searches
presence information achieves small constant search latency by the
Directed search algorithm and One-hop caching strategy.
ANALYSIS OF PROBLEM

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• Aim of proposed system is to design an architecture of disseminate
server for coherence request to system for buddy list search.
• In this project work a scalable server architecture which provides
services to ‘n’ number of users is presented.
• Presenting a precise design by improving thought of peer to peer system
while designing presence cloud.
• 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
PROPOSED SYSTEM INTRODUCTION

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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

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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

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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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Directed buddy search
• Minimizing the searching response time is important in presence
services.
• By organizing PS nodes into server-to-server overlay network, we can
efficiently respond to the queries and can reduce network traffic.
• By capitalizing one–hop caching, that it maintains user list of
neighbors, we can improve response time by increasing the chances of
finding buddies.
• Clearly, this mechanism reduces
both network traffic and response
time.
Buddy list searching in Presence Cloud.

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• Presence servers present in presence cloud, these presence servers are
arranged in quorum based server to server architecture and also load on
servers are balance in presence cloud sever overlay.
• All these presence server keeps caches for buddies in order to increase
query speed is one hop caching approach.
• Finding small constant search delay results in directed buddy search by
decreasing network traffic using one hop search strategy.
Architecture of Presence Cloud

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• Objective of this paper is to propose an on demand QoS routing
algorithm.
• Since the requirements for various applications may vary time to time,
the approach for QoS routing may not be proactive.
• 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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 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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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 design of scalable server-to-server architecture can be used as a
building block for mobile presence services.
• 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.
• To avoid single point of failure, no single presence server is supposed
to maintain service-wide global information about all users.

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 In this paper, we have presented Presence Cloud, a scalable server
architecture that supports mobile presence services in large-scale social
network.
 We have shown that Presence Cloud achieves low search latency and
enhances the performance of mobile presence services.
 In addition, we discussed scalability problem in server and introduced
buddy-list search problem.
 Through a simple mathematical model, we show that total number of
buddy search messages increases substantially with user arrival rate and
number of presence servers.
 The results of simulations demonstrate that Presence Cloud achieves major
performance gains in terms of the search cost and search satisfaction.
CONCLUSION

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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] Instant Messaging and Presence Protocol IETF Working
Group,http://www.ietf.org/html.charters/impp-charter.html, 2014.
[6] Extensible Messaging and Presence Protocol IETF Working
Group,http://www.ietf.org/html.charters/xmpp-charter.html,2012.
[7] http://www.jabber.org, 2012.

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[8] A. Houri, S. Parameswar, E. Aoki, V. Singh, and H. Schulzrinne, “Scaling Requirements
for Presence in SIP/SIMPLE,” IETF Internet draft, 2009.
[9] S.A. Baset, G. Gupta, and H. Schulzrinne, “OpenVoIP: An Open Peer-to-Peer VoIP and
IM System,” Proc. ACM SIGCOMM, 2008.
[10] Open Mobile Alliance, “OMA Instant Messaging and PresenceService,” 2005
[11] W.-E. Chen, Y.-B.Lin, and R.-H. Liou, “A Weakly ConsistentScheme for IMS Presence
Service,” IEEE Trans. Wireless Comm.,vol. 8, no. 7, pp. 3815-3821, July 2009.
[12] N. Banerjee, A. Acharya, and S.K. Das, “Seamless SIP-BasedMobility for Multimedia
Applications,” IEEE Network, vol. 20,
[13] 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” .
[14] Michael Piatek, Tomas Isdal, Arvind Krishnamurthy , and Thomas Anderson “One hop
Reputations for Peer to Peer FileSharing Workloads”.
[15] Brent Hecht, Jaime Teevan , Meredith Ringel Morris, and Dan Liebling, “SearchBuddies:
Bringing Search Engines into theConversation”, 2012

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