presence cloud

05/02/20231

Department of Computer Science & EngineeringShri 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)


Seminar on

05/02/2023“Presence Cloud based solution for on demand data in wireless computing devices”

2

OVERVIEW 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


3

• 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


4

• 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.


5

• 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.


6

•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


7

• 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.


8

• 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


9

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.


10

• 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


11

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


12

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.


13

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.


14 05/02/2023

ALGORITHMPresence 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 */


15

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 ifend ifend for


16

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.


17

FLOWCHART

Dataflow Diagram

05/02/202318

SNAPSHOT

The Project is divided into two parts :•Media Server•Middle Server

SCREEN 1

Login Screen of Media Server


19

SCREEN 2 Login Successful


20

SCREEN 3

Uploading Data


21

SCREEN 4

Middle Server (Creating User Profile)


22

SCREEN 5

Registration Page of User


23

SCREEN 6

Selection of Starting Nodes


24

SCREEN 7

Downloading Data file through Algorithm


25

SCREEN 8

Saving file


26

SCREEN 9

System Database table showing distance between nodes


27

SCREEN 10

Android Mobile app (Video on demand)


28

SCREEN 11

Creating User Profile


29

SCREEN 12

Connecting nodes and accessing data


30

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.


31

SCREEN 13

Calculated Node Path and Time Required by Normal Algorithm


32

SCREEN 14

Calculated Node Path and Distance using Our New Algorithm


33

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


34

•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.


35

Efficiency Graph of Normal Algorithm vs Presence Cloud Algorithm

0 500 1000 1500 2000 25000

5

10

15

20

25

Pie chart showing Distance

Distance in NormalDistance By Our Algo

Distance

Time (ms)


36

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


37

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


38

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.


39

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


40

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


41

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


42

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.


43

[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.


44

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


45

Thank You !!!