An Open Jackson Network Model for Heterogeneous Infrastructure as A Service on Cloud Computing

Chien Nguyen Khac^{1, 2}, Khiet Bui Thanh^{3, 4}, ⁴Hung Ho Dac, ²Son Nguyen Hong³Vu Pham Tran and ²Hung Tran Cong

¹Department of Mathematics – Informatics, The People's Police University, Ho Chi Minh City, Vietnam

²Training and Science Technology Department, Posts and Telecoms Institute of Technology Ho Chi Minh City, Vietnam

³Faculty of Computer Science and Engineering, Ho Chi Minh City University of Technology, Vietnam

⁴Faculty of Technology Engineering, Thu Dau Mot University, Vietnam

Abstract

Cloud computing is an environment which provides services for user demand such as software, platform, infrastructure. Applications which are deployed on cloud computing have become more varied and complex to adapt to increase end-user quantity and fluctuating workload. One popular characteristic of cloud computing is the heterogeneity of network, hosts and virtual machines (VM). There were many studies on cloud computing modeling based on queuing theory, but most studies have focused on homogeneity characteristic. In this study, we propose a cloud computing model based on open Jackson network for multi-tier application systems which are deployed on heterogeneous VMs of IaaS cloud computing. The important metrics are analyzed in our experiments such as mean waiting time; mean request quantity, the throughput of the system. Besides that, metrics in model is used to modify number VMs allocated for applications. Result of experiments shows that open queue network provides high efficiency.

Keywords

Heterogeneous Infrastructure as a Service, Cloud Computing, Open Jackson Network

For More Details: http://aircconline.com/ijcnc/V11N1/11119cnc04.pdf

Volume Link:  http://airccse.org/journal/ijc2019.html

REFERENCES

[1].    Ali-Eldin, Ahmed, Tordsson, Johan, and Elmroth, Erik (2012), An adaptive hybrid elasticity controller for cloud infrastructures, Network Operations and Management Symposium (NOMS), 2012 IEEE, IEEE, pp. 204-212.

[2].    Armbrust, Michael, et al. (2010), "A view of cloud computing", Communications of the ACM. 53(4), pp. 50-58.

[3].    Bai, Wei-Hua, et al. (2015), "Performance analysis of heterogeneous data centers in cloud computing using a complex queuing model", Mathematical Problems in Engineering. 2015.

[4].    Burke, Paul J (1956), "The output of a queuing system", Operations research. 4(6), pp. 699-704.

[5].    Cao, Junwei, et al. (2013), "Optimal multiserver configuration for profit maximization in cloud computing", ieee transactions on parallel and distributed systems. 24(6), pp. 1087-1096.

[6].    Cao, Junwei, Li, Keqin, and Stojmenovic, Ivan (2014), "Optimal power allocation and load distribution for multiple heterogeneous multicore server processors across clouds and data centers", IEEE Transactions on Computers. 63(1), pp. 45-58.

[7].    Chiang, Yi-Ju and Ouyang, Yen-Chieh (2014), "Profit optimization in SLA-aware cloud services with a finite capacity queuing model", Mathematical Problems in Engineering. 2014.

[8].    Delimitrou, Christina and Kozyrakis, Christos (2013), Paragon: QoS-aware scheduling for heterogeneous datacenters, ACM SIGPLAN Notices, ACM, pp. 77-88.

[9].    Guo, Lizheng, et al. (2014), "Dynamic performance optimization for cloud computing using m/m/m queueing system", Journal of applied mathematics. 2014.

[10].  Jackson, James R (1957), "Networks of waiting lines", Operations research. 5(4), pp. 518-521.

[11].  Jackson, James R (1963), "Jobshop-Like Queueing Systems. Mgmt. Sci. 10, 131-142", Jackson13110Mgmt. Sci.

[12].  Khazaei, Hamzeh, Misic, Jelena, and Misic, Vojislav B (2012), "Performance analysis of cloud computing centers using m/g/m/m+ r queuing systems", IEEE Transactions on parallel and distributed systems. 23(5), pp. 936-943.

[13].  Liang, Yanbing, et al. (2010), Optimizing particle swarm optimization to solve knapsack problem, International Conference on Information Computing and Applications, Springer, pp. 437-443.

[14].  Mao, Ming, Li, Jie, and Humphrey, Marty (2010), Cloud auto-scaling with deadline and budget constraints, Grid Computing (GRID), 2010 11th IEEE/ACM International Conference on, IEEE, pp. 41-48.

[15].  Mars, Jason, Tang, Lingjia, and Hundt, Robert (2011), "Heterogeneity in “homogeneous” warehouse-scale computers: A performance opportunity", IEEE Computer Architecture Letters. 10(2), pp. 29-32.

[16].  Nah, Fiona Fui-Hoon (2004), "A study on tolerable waiting time: how long are web users willing to wait?", Behaviour & Information Technology. 23(3), pp. 153-163.

[17].  Salah, Khaled (2013), "A queuing model to achieve proper elasticity for cloud cluster jobs", International Journal of Cloud Computing. 1, pp. 53-64.

[18].  Salah, Khaled, Elbadawi, Khalid, and Boutaba, Raouf (2016), "An analytical model for estimating cloud resources of elastic services", Journal of Network and Systems Management. 24(2), pp. 285-308.

[19].  Slothouber, Louis P (1996), A model of web server performance, Proceedings of the 5th International World wide web Conference.

[20].  Urgaonkar, Bhuvan, et al. (2008), "Agile dynamic provisioning of multi-tier internet applications", ACM Transactions on Autonomous and Adaptive Systems (TAAS). 3(1), p. 1.

[21].  Vaquero, Luis M, et al. (2008), "A break in the clouds: towards a cloud definition", ACM SIGCOMM Computer Communication Review. 39(1), pp. 50-55.

[22].  Vecchiola, Christian, Pandey, Suraj, and Buyya, Rajkumar (2009), High-performance cloud computing: A view of scientific applications, Pervasive Systems, Algorithms, and Networks (ISPAN), 2009 10th International Symposium on, IEEE, pp. 4-16.

[23].  Vilaplana, Jordi, et al. (2014), "A queuing theory model for cloud computing", The Journal of Supercomputing. 69(1), pp. 492-507.

[24].  Villela, Daniel, Pradhan, Prashant, and Rubenstein, Dan (2007), "Provisioning servers in the application tier for e-commerce systems", ACM Transactions on Internet Technology (TOIT). 7(1), p. 7.

[25].  Xiong, Kaiqi and Perros, Harry (2009), Service performance and analysis in cloud computing, Services-I, 2009 World Conference on, IEEE, pp. 693-700.

[26].  Yang, Bo, Tan, Feng, and Dai, Yuan-Shun (2013), "Performance evaluation of cloud service considering fault recovery", The Journal of Supercomputing. 65(1), pp. 426-444.

[27].  Zhang, Qi, Cherkasova, Ludmila, and Smirni, Evgenia (2007), A regression-based analytic model for dynamic resource provisioning of multi-tier applications, Autonomic Computing, 2007. ICAC'07. Fourth International Conference on, IEEE, pp. 27-27.

[28].  Qu, Chenhao (2016), "Auto-scaling and Deployment of Web Applications in Distributed Computing Clouds".

[29].  Sahni, Jyoti and Vidyarthi, Deo Prakash (2016), "Heterogeneity-aware adaptive auto-scaling heuristic for improved QoS and resource usage in cloud environments", Computing, pp. 1-31.

[30].  Sowjanya, T Sai, et al. (2011), "The queueing theory in cloud computing to reduce the waiting time".

  

AUTHORs

Chien Nguyen Khac, received his master degree in Computer Science from the University of Natural Sciences in HCM City in 2008. He is currently a lecturer at the University of the People's Police, and is doing a PhD candidate in Computer Engineering at the PTIT, Hanoi. His research interests: Auto-Scaling in cloud computing. Email: nkchienster@gmail.com.

Khiet Bui Thanh, is a PhD candidate at Computer Science, Faculty of Computer Science and Engineering, Ho Chi Minh City University of Technology. Research Interests: Cloud computing. Email: khietbt@tdmu.edu.vn

Hung HoDac, Born in 1991 in Binh Duong. Graduated from the Graduate School of Information Systems at the Ho Chi Minh City Post and Telecommunications Institute of Technology. Currently working at Faculty of Engineering and Technology, Thu Dau Mot University, Binh Duong.Research: Game, Cloud Computing. Email: hunghd@tdmu.edu.vn

Son Nguyen Hong, received his B.Sc. in Computer Engineering from the University of Technology in HCM city, his M.Sc. and PhD in Communication Engineering from the Post and Telecommunication Institute of Technology Hanoi. His Current research interests include communication engineering, network security, computer engineering and cloud computing. Email: ngson@ptithcm.edu.vn

Vu Pham Tran, Currently Deputy Dean of Computer Science and Technique, Ho Chi Minh City University of Technology. Research: Intelligent Transport Systems (ITS), Big Data Analytics, Peer-to-Peer Computing Email: ptvu@hcmut.edu.vn

Hung Tran Cong,He received the master of engineering degree in telecommunications engineering course from postgraduate department Hanoi University of technology in Vietnam, 1998. He received Ph.D at Hanoi University of technology in Vietnam, 2004. His main research areas are B – ISDN performance parameters and measuring methods, QoS in high speed networks, MPLS. He is, currently, Associate Professor PhD. of Faculty of Information Technology II, Posts and Telecoms Institute of Technology in Ho Chi Minh, Vietnam. Email: conghung@ptithcm.edu.vn

Energy Efficient Node Rank-Based Routing Algorithm in Mobile Ad-Hoc Networks

 D. Kothandaraman^1*and C. Chellappan²

^1*Department of Computer Science and Engineering, S R Engineering College,

Warangal, TS, India.

² Department of Computer Science and Engineering, CEG, Anna University,

Chennai, TN, India.

ABSTRACT

Mobile Ad-hoc Network (MANET) is an emerging technology, infrastructure less with self-organizing, self-healing, multi-hop wireless routing networks in real time. In such networks, many routing problems arise due to complexity in the network mobility which results from difficulty in achieving energy efficient routing in the field of MANET. Due to the dynamic nature and the limited battery energy of the mobile nodes, the communication links between intermediate relay nodes may fail frequently, thus affecting the routing performance of the network and also the availability of the nodes. Though existing protocols are not concentrating about communication links and battery energy, node links are very important factor for improving quality of routing protocols because Node Rank helps us to determine whether the node is within transmission range or out of transmission range through considering residual energy of the node during the routing process. This paper proposes a novel Energy Efficient Node Rank-based Routing (EENRR) algorithm which includes certain performance metrics such as control overhead and residual energy in order to improve the Packet Delivery Ratio (PDR), and Network Life Time (NLT) from its originally observed routing performance obtained through other existing protocols. Simulation results show that, when the number of nodes increases from 10 to 100 nodes, EENRR algorithm increases the average residual energy by 31.08% and 21.26% over the existing Dynamic Source Routing (DSR) and Energy Efficient Delay Time Routing (EEDTR) protocols, respectively. Similarly it increases the PDR by 45.38% and 28.3% over the existing DSR and EEDTR protocols respectively.

KEYWORDS

Energy efficiency; Link stability; Node rank; Packet delivery ratio; Residual energy; Threshold energy

For More Details: http://aircconline.com/ijcnc/V11N1/11119cnc03.pdf

Volume Link:  http://airccse.org/journal/ijc2019.html

REFERENCES

[1]     C. Daniel Nesa Kumar &V. Saravanan,(2018)“A Survival Study on Energy Efficient and Secured Routing in Mobile Ad-hoc Network”, IOSR Journal of Computer Engineering, Vol. 20, Issue No. 1, pp. 35-46.

[2]     M.N. Kalpana, K.N. Sridevi & Jitendranath Mungara, (2013)“Enhanced Energy Efficient Routing for Self Adaptive Geographic Routing In MANETs”, International Journal of Computational Engineering Research, Vol.  03, Issue. 7,

[3]     Senthil Murugan Tamilarasan &Kannan Eswariah,(2013) “Link Stability With Energy Aware Ad Hoc On Demand Multipath Routing Protocol In Mobile Ad Hoc Networks”, American Journal of Applied Sciences, Vol.10, No. 8, pp. 844-848.

[4]     D. Kothandaraman, A. Amuthan, C. Chellappan &N. Sreenath,(2011) “Prevention of Vulnerabilities on Location- based Geocasting and Forwarding Routing Protocol in Mobile Ad-hoc Network”, International Journal of Security and Its Applications Vol. 5, No. 1, pp.65-76

[5]     C. Siva Ram Murthy &B.S. Manoj,(2007),“Ad hoc Wireless Networks Architecture and Protocols”, Pearson Education Publication, 2006, pp. 1- 878.

[6]     Rashmi Rohankar, Rinkoo Bhatia, Vineet Shrivastava & Deepak Kumar Sharma, (2012),“Performance Analysis of Various Routing Protocols (Proactive and Reactive) for Random Mobility Models of Ad-hoc Networks”, 1st Int. Conf. on Recent Advances in Information Technology , RAITDhanbad, India , 15-17 March.

[7]     S. Singh &C. Raghavendra, (1998) “PAMAS: Power Aware Multi-Access protocol with Signalling for Ad hoc Networks”, ACM Computer Communication Review, Vol. 28, No. 3, pp. 5-26.

[8]     Baisakh, (2013)“A Review of Energy Efficient Dynamic Source Routing Protocol for Mobile Ad Hoc Networks”, International Journal of Computer Applications, Vol. 68, No. 20, pp. 0975 – 8887

[9]     Z. Zhang, G. Mao &B.D.O. Anderson, (2015)“Energy-Efficient Broadcast in Mobile Networks Subject to Channel Randomness, Wireless Communications”, IEEE Transactions, Vol.14, No.6, pp. 2929-2941.

[10]   Rafael Pérez TorresCésar, Torres-HuitzilHiram &Galeana-Zapién,(2016)“Power management techniques in smartphone-based mobility sensing systems: A survey”, Pervasive and Mobile Computing, Vol. 6, pp. 1-21.

[11]   Ming Shen Jian &Chienting Lin., (2005) “Improved rank-based multi-path routing in mobile ad-hoc networks”, The 7th International Conference on Advanced Communication Technology, ICACT.

[12]   Deepti Badal, Gwalior &Rajendra Singh Kushwah, (2016)“Energy Efficient Approach of Route Selection for Dynamic Source Routing Protocol in MANET”, ProceedingAICTC '16 Proceedings of the International Conference on Advances in Information Communication Technology & Computing,Bikaner, India.

[13]   Rjab Hajlaoui, Sami Touil &Wissem achour, (2015)“O-DSR: Optimized DSR Routing Protocol for Mobile Ad Hoc Network”, International Journal of Wireless & Mobile Networks, Vol. 7, No. 4. pp. 37-47

[14]   Sukhdev Singh Ghuman,(2016)“Dynamic Source Routing (DSR) Protocol in Wireless Networks”, IJCSMC, Vol. 5, Issue. 6, pp. 251-254

[15]   Navin Mani Upadhyay &Kunal Gaurav Arun Kumar,(2014) “Modified DSR, an energy conserving approach to DSR protocol in MANET”, Published in: 2014 International Conference on Communication and Signal Processing,pp. 109 – 112

[16]   M. Tamilarasi, S. Chandramithi &T. G. Palanivelu,(2008) “Overhead Reduction and Energy Management in DSR for MANET”, Proc. Of COMSWARE,), pp. 762-766.

[17]   R. Dube, Rais, C. D. Kuang Yeh Wang &S. K. Tripathi,(1997)“Signal Stability based Adaptive Routing (SSA) for Ad-Hoc Mobile Networks,” IEEE Personal Communications, Vol. 4, Issue. No. 1, pp. 36-45.

[18]   E. M. Royer &C. K. Toh, (1999)“A Review of Current Routing Protocols for Ad Hoc Mobile Wireless Networks”, IEEE Personal Communications, Vol. 6, Issue. No. 2, pp. 46-55.

[19]   S. Chakrabarti &A. Mishra, (2001)“QoS Issues in Ad Hoc Wireless Networks”, IEEE Communications Magazine, Vol. 39, No. 2,

[20]   Hui Xia, Jia Yu, Zhen-Kuan Pan, Xiang-Guo Cheng &Edwin H. Sha., (2016)“Applying trust enhancements to reactive routing protocols in mobile ad hoc networks”, Journal Wireless Networks archive, Vol. 22, Issue No. 7, pp. 2239-2257

[21]   S. Sridhar &R. Baskaran,(2015) “Efficient Routing in Mobile Adhoc Networks Emphasizing Quality of Service by Trust & Energy based AODV”, Journal of Communications Software and Systems, Vol. 11, No. 1, pp.1-7

[22]   C.K. Toh, (2001)“Maximum Battery Life Routing to Support Ubiquitous Mobile Computing in Wireless Ad Hoc Networks”, IEEE Communications Magazine, Vol. 39, Issue No. 6,

[23]   Shuchita Upadhayaya &CharuGandhi,(2010)“Node Disjoint Multipath Routing Considering Link and Node Stability protocol:Acharacteristic Evaluation”, International Journal of Computer Science, Vol.7, Issue 1, No.2, pp. 18-25

[24]   P. Sivasanka, C. Chellappan &S. Balaji,(2011) “Energy Efficient Routing Algorithms and performance comparison for MANET”, CIIT International Journal of Wireless Communication, Vol. 3, No.10.

[25]   M. Venkata Ramana &P. Venkata Krishna, (2016) “An approach for routing in delay tolerant mobile ad-hoc networks using quadrant-based look-ahead method”, International Journal of Wireless and Mobile Computing, Vol. 10, Issue 3,

[26]   Bhed B. Bista &Danda B., Rawat, (2017) “EA-Epidemic: An Energy Aware Epidemic-Based Routing Protocol for Delay Tolerant Networks” , Journal of Communications,  Vol. 12, No. 6, pp. 304-311

[27]   D. Jyothi Preshiya & C.D. Suriyakala, (2015)“A Survey of Factors Influencing Network Lifetime in Delay Tolerant Network”, Indian Journal of Science and Technology, Vol 8, No. 15, pp. 1-9

[28]   Federica Bogo &Enoch Peserico, (2013)“Optimal Throughput and Delay in Delay-tolerant Networks with Ballistic Mobility”, MobiCom 13 Proceedings of the 19th Annual International Conference on Mobile Computing & Networking, pp.303-314.

[29]   S Aluvala, D Vodnala, N Yamsani, S Phani Kumar, (2014) “A routing algorithm for localization of link failure in MANET”, International Journal of Current Engineering and Scientific Research (IJCESR), Vol.1, Issue. 3, pp. 25-30.

AUTHORS

Dr. D. Kothandaraman received his B.E. computer Science and Engineering from Dr. Pauls Engineering College (Anna University), M.Tech., in Information Security from Pondicherry Engineering College(Pondicherry University(Govt. of India)) and Ph.D. in Computer Science and Engineering from College of Engineering, Guindy, Anna University(Govt. of Tamil Nadu). To his credit, he has 7 years of teaching and research experience. His area of research interest is computer networks, Wireless Sensor Networks (WSN), Mobile Ad-hoc Networks (MANETs) and Internet of Things (IoT). He has published various papers in International Journals and in conferences.

Dr. C. Chellappan is a Professor (retd.) in the Department of Computer Science and Engineering at Anna University, Chennai, India. He received his B.Sc. in Applied Sciences and M.Sc in Applied Science– Applied Mathematics from PSG College Technology, Coimbatore under University of Madras. He received his M.E and Ph.D in Computer Science and Engineering from Anna University. He held in various positions during his working periods. He has published more than 200 papers in reputed International Journals and Conferences. His research areas are Image Processing, Computer Networks, Cyber security Distributed Computing, Network Security, Mobile Agents. Currently working as the senior Professor and Principal in G.K.M. College of Engineering, Perungalathur, Chennai.

Machine Learning Algorithm of Detection of DOS Attacks on An Automotive Telematic Unit

Eric Perraud

Renault Software Labs, Toulouse, France

Abstract

Today vehicles are connected to private networks which are owned by the car manufacturer. But in coming years, vehicles become more and more connected to the public Internet for infotainment applications but also to safety applications. Like any Internet terminal, some hackers can attack the wireless connectivity unit of the vehicle with Distribution Denial of Services (DDOS) attacks, so that the wireless connectivity unit of the vehicle is not available and the service is lost. Therefore, it is critical to developing a mechanism to detect such an attack and eliminate it, to maintain the availability of the wireless connectivity unit. This paper proposes an algorithm which proceeds in 2 steps: it uses an unsupervised machine learning algorithm to detect DDOS attacks in the incoming Internet data. When it detects an attack, it uses the results of the machine learning algorithm to split the legitimate flow and the rogue flows. The rogue flow is filtered so that the availability of the wireless connectivity unit of the vehicle is restored. This proposed algorithm needs very few CPU computing power and is compatible with low-cost CPUs which are used in an automotive wireless connectivity unit.

Keywords

Clustering algorithm, vehicle, DDOS, unsupervised learning

For More Details: http://aircconline.com/ijcnc/V11N1/11119cnc02.pdf

Volume Link: http://airccse.org/journal/ijc2019.html

REFERENCES

[1]     Andrew Ng – Coursera Online – Machine Learning

[2]     D. Ippoliti and X. Zhou. Online adaptive anomaly detection for augmented network flows. In IEEE 22nd Int. Symp. on Modelling, Analysis Simulation of Comput. and Telecommun. Syst., pages 433–442, Sept. 2014.

[3]     A. Lakhina, M. Crovella, and C. Diot. Diagnosing network-wide traffic anomalies. In Conf. on Applications, Technologies, Architectures, and Protocols for Comput. Commun., pages 219–230, New York, NY, USA, 2004. ACM.

[4]     Juliette Dromard, Gilles Roudiere, Philippe Owezarski. Online and scalable unsupervised network anomaly detection method – IEEE Transactions on Networks and Service Management ,pages 34-47, 2017.

[5]     N. Chen, A. Chen, and L. Zhou. An incremental grid density-based clustering algorithm. Journal of Software, 13(1), Aug. 2002.

[6]     P. Casas, J. Mazel, and P. Owezarski. NETWORKING 2011: 10th Int. IFIP TC 6 Networking Conf., chapter UNADA: Unsupervised Network Anomaly Detection Using Sub-space Outliers Ranking, pages 40–51. Springer Berlin Heidelberg, 2011.

[7]     Y. Bengio, O. Delalleau, and N. Le Roux. The curse of highly variable functions for local kernel machines. In Proc. of the 18th Int. Conf. on Neural Information Processing Systems, pages 107–114, Cambridge, MA, USA, 2005. MIT Press.

[8]     Chiadighikaobi Ikenna Rene and Johari Abdullah. Malicious Code Intrusion Detection using Machine Learning And Indicators of Compromise. International Journal of Computer Science and Information Security (IJCSIS), Vol. 15, No. 9, September 2017

[9]     A. Patcha and J. Park. An overview of anomaly detection techniques: Existing solutions and latest technological trends. Comput. Netw., 51(12), Aug 2007.

[10]   Vishwas S. Ciza, T. and Balakrishnan N. Usefulness of darpa dataset for intrusion detection system evaluation, 2014

[11]   P. F. Evangelista, M. J. Embrechts, and B. K. Szymanski. Taming the Curse of Dimensionality in Kernels and Novelty Detection, pages 425– 438. Springer Berlin Heidelberg, Berlin, Heidelberg, 2006

[12]   M-LShyu,S-CChen,K.Sarinnapakorn,andL.Chang. A novel anomaly detection scheme based on principal component classifier. IEEE Foundations and New Directions of Data Mining Workshop, pages 171– 179, 2003.

[13]   J. Dromard, V. Baudin, P. Owezarski, A. Mozo-Velasco, B. Ordozgoiti, and S. Gomez-Canaval.D4.3: Experimental evaluation of algorithms for online network characterizations. Technical report, FP7 European ONTIC project: Online Network Traffic Characterization, January 2017.

[14]   J. Mazel. Unsupervised network anomaly detection. PhD thesis, INSA Toulouse, France, Dec. 2011

[15]   Vijaya P. Vipin, D., Sattvik S., Sreevathsan S., and Gireesh K. T. Network intrusion detection system based on machine learning algorithms. International Journal of Computer Science and Information Technology (IJCSIT), 2(6):138–151, 2010.

[16]   Deshmukh S. Patil, R. and Rajeswari K. Analysis of simple k-means with multiple dimensions using weka. International Journal of Advanced Research in Computer Science and Software Engineering, 110:14–17, 2015.

[17]   K. Leung and C. Leckie. Unsupervised anomaly detection in network intrusion detection using clusters. In Twenty-Eighth Australasian Comput. Science Conf. (ACSC2005), pages 333–342, 2005.

[18]   P. Casas, J. Mazel, and P. Owezarski. Unsupervised network intrusion detection systems: Detecting the unknown without knowledge. Comput. Comm., 35(7):772 – 783, 2012.

[19]   11. L. Parsons et al., “Subspace Clustering for High Dimensional Data: a Review”, in ACM SIGKDD Expl. Newsletter, vol. 6 (1), pp. 90-105, 2004 ACM SIGKDD Expl. Newsletter, vol. 6 (1), pp. 90-105, 2004

AUTHORS 

Eric Perraud works at Renault Software labs in the Strategy Office. He had previously been working at Intel and Motorola as modem chief architect. He has a Telecommunication master from Sup Telecom Bretagne and a opto-electronics PhD from Sup-Aero