Estratégias Eficientes para Identificação de Falhas
Utilizando o Diagnóstico Baseado em Comparações



Roverli Pereira Ziwich

Tese de Doutorado em Ciência da Computação, Universidade Federal do Paraná.
Data da Defesa: 12 de Abril, 2013. Curitiba, PR, Brasil.

Banca da tese:
Profa. Jussara Marques de Almeida (UFMG),  Prof. Altair Olivo Santin (PUC/PR),
Prof. Luis Carlos Erpen de Bona (UFPR),  Prof. Luiz Carlos Pessoa Albini (UFPR),
Prof. Elias Procópio Duarte Júnior (Orientador, UFPR).

pp. 1-217, Abr. 2013.  [pdf]  dspace.c3sl.ufpr.br



Abstract

Comparison-based diagnosis is a practical approach to detect faults in hardware, software, and network-based systems. Diagnosis is based on the comparison of task outputs returned by pairs of system units in order to determine whether those units are faulty or fault-free. If the comparison results in a mismatche then one ore both units are faulty. System diagnosis is based on the complete set of all comparison results. This work introduces a novel diagnosis algorithm to identify faults in $t$-diagnosable systems of arbitrary topology under the MM* model. The complexity of the proposed algorithm is $O(t^2 \Delta N)$ in the worst case for systems with $N$ units, where $t$ denotes the maximum number of faulty units allowed and $\Delta$ corresponds to the maximum degree of a unit in the system. This complexity is significantly lower than those of previously published algorithms. Besides the algorithm specification and correctness proofs, exhaustive simulations results are presented, showing the typical performance of the algorithm for different systems. Moreover, this work also presents two different strategies to detect and fight content pollution in P2P live streaming transmissions -- the first strategy is centralized and performs the diagnosis of content pollution in the network, and the second strategy is a completely distributed solution to combat the propagation of the pollution. Both strategies employ comparison-based diagnosis in order to detect any modification in the data transmitted. The solutions were also implemented in Fireflies, a scalable and fault-tolerant overlay network protocol, and a large number of simulation experiments were conduced. Results show that both strategies are feasible solutions to identify and fight content pollution in live streaming sessions and that they add low overhead in terms of network bandwidth usage. In particular, the solution proposed to combat content pollution was able to significantly reduce the pollution over the system in diverse network configurations -- in many cases the solution nearly eliminated the pollution during the transmission.


Resumo

O diagnóstico baseado em comparações é uma forma realista para detectar falhas em hardware, software, redes e sistemas distribuídos. O diagnóstico se baseia na comparação de resultados de tarefas produzidos por pares de unidades para determinar quais são as unidades falhas e sem-falha do sistema. Qualquer diferença no resultado da comparação indica que uma ou ambas as unidades estão falhas. O diagnóstico completo do sistema é baseado no resultado de todas as comparações. Este trabalho apresenta um novo algoritmo de diagnóstico para identificar falhas em sistemas de topologia arbitrária com base no modelo MM*. A complexidade do algoritmo proposto é $O(t^2 \Delta N)$ no pior caso para sistemas de $N$ unidades, onde $t$ denota o número máximo permitido de unidades falhas e $\Delta$ é o grau da unidade de maior grau no sistema. Esta complexidade é significativamente menor que a dos outros algoritmos previamente publicados. Além da especificação do algoritmo e das provas de correção, resultados obtidos através da execução exaustiva de experimentos são apresentados, mostrando o desempenho médio do algoritmo para diferentes sistemas. Além do novo algoritmo para sistemas de topologia arbitrária, este trabalho também apresenta duas outras soluções para detecção e combate à poluição de conteúdo, ou alterações não autorizadas, em transmissões de mídia contínua ao vivo em redes P2P -- a primeira é uma solução centralizada e que realiza o diagnóstico da poluição na rede, e a segunda é uma solução completamente distribuída e descentralizada que tem o objetivo de combater a propagação da poluição na rede. Ambas as soluções utilizam o diagnóstico baseado em comparações para detectar alterações no conteúdo dos dados transmitidos. As soluções foram implementadas no Fireflies, um protocolo escalável para redes overlay, e diversos experimentos através de simulação foram conduzidos. Os resultados mostram que ambas as estratégias são soluções viáveis para identificar e combater a poluição de conteúdo em transmissões ao vivo e que adicionam baixa sobrecarga ao tráfego da rede. Em particular a estratégia de combate à poluição foi capaz de reduzir consideravelmente a poluição de conteúdo em diversas configurações, em vários casos chegando a eliminá-la no decorrer das transmissões.


Referências

[1]     K. Abrougui and M. Elhadef. Parallel Self-Diagnosis of Large Multiprocessor Systems Under the Generalized Comparison Model. Proc. of the 11th Intl. Conf. on Parallel and Distributed Systems, pages 78–84, July 2005.

[2]     S. B. Akers and B. Krishnamurthy. A Group-Theoretic Model for Symmetric Interconnection Networks. IEEE Transactions on Computers, 38(4):555–566, Apr. 1989.

[3]     L. C. P. Albini, A. Caruso, S. Chessa, and P. Maestrini. Reliable Routing inWireless Ad Hoc Networks: The Virtual Routing Protocol. Journal of Network and Systems Management, 14(3):335–358, Sept. 2006.

[4]     L. C. P. Albini, S. Chessa, and P. Maestrini. Diagnosis of Symmetric Graphs Under the BGM Model. The Computer Journal, 47(1):85–92, 2004.

[5]     L. C. P. Albini and E. P. Duarte Jr. Generalized Distributed Comparison-Based System-Level Diagnosis. Proc of the 2nd IEEE Latin American Test Workshop, pages 285–290, Sept. 2001.

[6]     L. C. P. Albini, E. P. Duarte Jr., and R. P. Ziwich. A Generalized Model for Distributed Comparison-Based System-Level Diagnosis. Journal of the Brazilian Computer Society, 10(3):44–56, Apr. 2005.

[7]     J. Amaral, J. Amaral, R. Tanscheit, and M. Pacheco. An Immune Inspired Fault Diagnosis System for Analog Circuits Using Wavelet Signatures. Proc. of the 2004 NASA/DoD Conf. on Evolvable Hardware, pages 138–141, June 2004.

[8]     E. Ammann and M. Dal Cin. Efficient Algorithms for Comparison-Based Self-Diagnosis. Self-Diagnosis and Fault Tolerance, Werkhefte der Universitat Ttibingen, 4 Attempto-Verlag, Tubingen, pages 1–18, 1981.

[9]     T. Araki and Y. Shibata. Diagnosability of Networks by the Cartesian Product. IEICE Transactions on Fundamentals, E83-A(3):465–470, Mar. 2000.

[10]   T. Araki and Y. Shibata. Diagnosability of Butterfly Networks Under the Comparison Approach. IEICE Transactions on Fundamentals, E85-A(5):1152–1160, May 2002.

[11]   T. Araki and Y. Shibata. Efficient Diagnosis on Butterfly Networks Under the Comparison Approach. IEICE Transactions on Fundamentals, E85-A(4), Apr. 2002.

[12]   T. Araki and Y. Shibata. (t, k)-Diagnosable System: A Generalization of the PMC Models. IEEE Transactions on Computers, 52(7):971–975, July 2003.

[13]   A. Bagchi and S. L. Hakimi. An Optimal Algorithm for Distributed System-Level Diagnosis. Proc. of the 21th IEEE Fault-Tolerant Computing Symp., pages 214–221, June 1991.

[14]   M. Barborak, A. Dahbura, and M. Malek. The Consensus Problem in Fault-Tolerant Computing. ACM Computing Surveys, 25(2):171–220, June 1993.

[15]   F. Barsi, F. Grandoni, and P. Maestrini. A Theory of Diagnosability Without Repair. IEEE Transactions on Computers, C-25(6):585–593, June 1976.

[16]   C. Basile, M. Killijian, and D. Powel. A Survey of Dependability Issues in Mobile Wireless Networks. Technical Report, Laboratory for Analysis and Architecture of Systems, National Center for Scientific Research, Toulouse, France, Feb. 2003.

[17]   S. Bell, B. Edwards, J. Amann, R. Conlin, K. Joyce, V. Leung, J. MacKay, M. Reif, L. Bao, J. Brown, M. Mattina, C.-C. Miao, C. Ramey, D. Wentzlaff, W. Anderson, E. Berger, N. Fairbanks, D. Khan, F.Montenegro, J. Stickney, and J. Zook. TILE64-Processor: A 64-Core SoC with Mesh Interconnect. Proc. of the IEEE Intl. Solid-State Circuits Conf., pages 88–598, Feb. 2008.

[18]   S. Bettayeb. On the k-Ary n-Cubes. Theoretical Computer Science, 140(2):333–339, Apr. 1995.

[19]   R. P. Bianchini and R. Buskens. An Adaptive Distributed System-Level Diagnosis Algorithm and Its Implementation. Proc. of the 21th IEEE Fault-Tolerance Computing Symp., pages 222–229, June 1991.

[20]   R. P. Bianchini and R. Buskens. Implementation of On-Line Distributed System-Level Diagnosis Theory. IEEE Transactions on Computers, 41(5):616–626, May 1992.

[21]   R. P. Bianchini, K. Goodwin, and D. S. Nydick. Practical Application and Implementation of System-Level Diagnosis Theory. Proc. of the 16th IEEE Fault-Tolerance Computing Symp., pages 332–339, June 1990.

[22]   D. M. Blough and H. W. Brown. The Broadcast Comparison Model for On-Line Fault Diagnosis in Multicomputer Systems: Theory and Implementation. IEEE Transactions on Computers, 48(5):470–493, May 1999.

[23]   D. M. Blough and A. Pelc. Complexity of Fault Diagnosis in Comparison Models. IEEE Transactions on Computers, 41(3):318–324, Mar. 1992.

[24]   D. M. Blough, G. F. Sullivan, and G. M. Masson. Almost Certain Diagnosis for Intermittently Faulty Systems. Proc. of the 18th IEEE Fault-Tolerant Computing Symp., pages 260–271, June 1988.

[25]   M. L. Blount. Probabilistc Treatment of Diagnosis in Digital Systems. Proc. of the 7th IEEE Fault-Tolerance Computing Symp., pages 72–77, 1977.

[26]   A. Borges, J. Almeida, and S. Campos. Fighting Pollution in P2P Live Streaming Systems. IEEE Intl. Conf. on Multimedia and Expo, pages 481–484, Aug. 2008.

[27]   A. Borges, P. Gomes, J. Nacif, R. Mantini, J. M. Almeida, and S. Campos. Characterizing SopCast Client Behavior. Computer Communications, 35(8):1004–1016, May 2012.

[28]   C. J. C. Burges. A Tutorial on Support Vector Machines for Pattern Recognition. Data Mining and Knowledge Discovery Journal, 2(2):121–167, June 1998.

[29]   C.-P. Chang, P.-L. Lai, J. J.-M. Tan, and L.-H. Hsu. Diagnosability of t-Connected Networks and Product Networks Under the Comparison Diagnosis Model. IEEE Transactions on Computers, 53(12):1582–1590, Dec. 2004.

[30]   C.-P. Chang, T.-Y. Sung, and L.-H. Hsu. Edge Congestion and Topological Properties of Crossed Cubes. IEEE Transactions on Parallel and Distributed Systems, 11(1):64–80, Jan. 2000.

[31]   G.-Y. Chang, G.-H. Chen, and G. J. Chang. (t, k)-Diagnosis for Matching Composition Networks Under the MM* Model. IEEE Transactions on Computers, 56(1):73– 79, Jan. 2007.

[32]   R. Chen, E. K. Lua, J. Crowcroft, W. Guo, L. Tang, and Z. Chen. Securing Peer-to-Peer Content Sharing Service from Poisoning Attacks. Proc. of the 8th IEEE Intl. Conf. on Peer-to-Peer Computing, pages 22–29, Sept. 2008.

[33]   Y. Chen, W. Bucken, and K. Echtle. Efficient Algorithms for System Diagnosis with Both Processor and Comparator Faults. IEEE Transactions on Parallel and Distributed Systems, 4(4):371–381, Apr. 1993.

[34]   S. Chessa and P. Santi. Comparison-Based System-Level Fault Diagnosis in Ad Hoc Networks. Proc. of the 20th Symp. on Reliable Distributed Systems, pages 257–266, Oct. 2001.

[35]   C.-F. Chiang and J. J. M. Tan. A Novel Approach to Comparison-Based Diagnosis for Hypercube-Like Multiprocessor Systems. Intl. Computer Symp., pages 166–169, Jan. 2007.

[36]   C.-F. Chiang and J. J. M. Tan. A Novel Approach to Comparison-Based Diagnosis for Hypercube-Like Systems. Journal of Information Science and Engineering, 24(1):1–9, Jan. 2008.

[37]   C.-F. Chiang and J. J. M. Tan. Using Node Diagnosability to Determine t-Diagnosability Under the Comparison Diagnosis Model. IEEE Transactions on Computers, 58(1):251–259, Jan. 2009.

[38]   Y.-H. Choi and T. Jung. Probabilistic Diagnosis for Sparsely Interconnected Systems. Proc. of the ACM Annual Conf. on Cooperation, pages 298–304, Feb. 1990.

[39]   S. A. Choudum and V. Sunitha. Augmented Cubes. Networks Journal, 40(2):71–84, Sept. 2002.

[40]   N. Christin, A. S. Weigend, and J. Chuang. Content Availability, Pollution and Poisoning in File Sharing Peer-to-Peer Networks. Proc. of the 6th ACM Conf. on Electronic Commerce, pages 68–77, June 2005.

[41]   K. Y. Chwa and S. L. Hakimi. On Fault Identification in Diagnosable Systems. IEEE Transactions on Computers, C-30(6):414–422, June 1981.

[42]   K. Y. Chwa and S. L. Hakimi. Schemes for Fault-Tolerant Computing: A Comparison of Modularly Redundant and t-Diagnosable Systems. Information and Control, 49(3):212–238, June 1981.

[43]   R. V. Coelho, J. T. Pastro, R. S. Antunes, M. P. Barcellos, I. Jansch-Porto, and L. P. Gaspary. Challenging the Feasibility of Authentication Mechanisms for P2P Live Streaming. Proc. of the 6th Latin America Networking Conference, pages 55–63, Oct. 2011.

[44]   P. Cull and S. M. Larson. The Möbius Cubes. IEEE Transactions on Computers, 44(5):647–659, May 1995.

[45]   A. T. Dahbura and G. M. Masson. An O(n^2.5) Fault Identification Algorithm for Diagnosable Systems. IEEE Transactions on Computers, C-33(6):486–492, June 1984.

[46]   A. T. Dahbura, K. K. Sabnani, and L. L. King. The Comparison Approach to Multiprocessor Fault Diagnosis. IEEE Transactions on Computers, C-36(3):373– 378, Mar. 1987.

[47]   M. Dal Cin. A Diagnostic Device for Large Multiprocessor Systems. Proc. of the 12th IEEE Intl. Symp. on Fault-Tolerant Computing, pages 357–360, June 1982.

[48]   S. K. Das, S. R. Ohring, and A. K. Banerjee. Embeddings Into Hyper Petersen Networks: Yet Another Hypercube-Like Interconnection Topology. VLSI Design, 2(4):335–351, 1995.

[49]   D. Dasgupta, K. KrishnaKumar, D. Wong, and M. Berry. Negative Selection Algorithm for Aircraft Fault Detection. Proc. of the 3rd Intl. Conf. on Artificial Immune Systems, pages 1–13, Sept. 2004.

[50]   J. Davies. Implementing SSL / TLS: Using Cryptography and PKI. Wiley, Jan. 2011.

[51]   H. Deshpande, M. Bawa, and H. Garcia-Molina. Streaming Live Media Over a Peer-to-Peer Network. Technical Report, Stanford InfoLab, (2001-30), Apr. 2001.

[52]   P. Dhungel, X. Hei, K. W. Ross, and N. Saxena. The Pollution Attack in P2P Live Video Streaming: Measurement Results and Defenses. Proc. of the Workshop on Peer-to-peer Streaming and IP-TV, pages 323–328, Aug. 2007.

[53]   P. Dhungel, X. Hei, K. W. Ross, and N. Saxena. Pollution in P2P Live Video Streaming. Intl. Journal of Computer Networks and Communications, 1(2):99–110, July 2009.

[54]   E. P. Duarte Jr., A. Brawerman, and L. C. P. Albini. An Algorithm for Distributed Hierarquical Diagnosis of Dynamic Fault and Repair Events. Proc. of the IEEE Intl. Conf. on Parallel and Distributed Systems, pages 299–306, 2000.

[55]   E. P. Duarte Jr. and T. Nanya. Multi-Cluster Adaptive Distributed System-Level Diagnosis Algorithms. IEICE Techinical Report FTS 95-73, 1995.

[56]   E. P. Duarte Jr. and T. Nanya. A Hierarquical Adaptive Distributed System-Level Diagnosis Algotithm. IEEE Transactions on Computers, 47(1):34–45, Jan. 1998.

[57]   E. P. Duarte Jr. and A. Weber. A Distributed Network Connectivity Algorithm. Proc. of the 6th IEEE Intl. Symp. on Autonomous Decentralized Systems, pages 285–292, Apr. 2003.

[58]   E. P. Duarte Jr., A. Weber, and K. V. O. Fonseca. Distributed Diagnosis of Dynamic Events in Partitionable Arbitrary Topology Networks. IEEE Transactions on Parallel and Distributed Systems, 23(8):1415–1426, Aug. 2012.

[59]   E. P. Duarte Jr., R. P. Ziwich, and L. C. P. Albini. A Survey of Comparison-Based System-Level Diagnosis. ACM Computing Surveys, 43(3):22:1–22:56, Apr. 2011.

[60]   K. Efe. A Variation on the Hypercube with Lower Diameter. IEEE Transactions on Computers, 40(11):1312–1316, Nov. 1991.

[61]   K. Efe. The Crossed Cube Architecture for Parallel Computing. IEEE Transactions on Parallel and Distributed Systems, 3(5):513–524, Sept. 1992.

[62]   K. Efe, P. K. Blackwell, W. Slough, and T. Shiau. Topological Properties of the Crossed Cubes Architecture. IEEE Transactions on Computers, 44(7):923–929, July 1995.

[63]   A. El-Amawy and S. Latifi. Properties and Performance of Folded Hypercubes. IEEE Transactions on Parallel and Distributed Systems, 2(1):31–42, Jan. 1991.

[64]   M. Elhadef. A Perceptron Neural Network for Asymmetric Comparison-Based System-Level Fault Diagnosis. Proc of the 5th Intl. Conf. on Availability, Reliability and Security, pages 265–272, Mar. 2009.

[65]   M. Elhadef. A Modified Hopfield Neural Network for Diagnosing Comparison-Based Multiprocessor Systems Using Partial Syndromes. Proc. of the 17th IEEE Intl. Conf. on Parallel and Distributed Systems, pages 646–653, Dec. 2011.

[66]   M. Elhadef. Using Linear Support Vector Machines to Solve the Asymmetric Comparison-Based Fault Diagnosis Problem. Proc of the 7th Intl. Conf. on Availability, Reliability and Security, pages 18–27, Aug. 2012.

[67]   M. Elhadef and B. Ayeb. An Evolutionary Algorithm for Identifying faults in t-Diagnosable Systems. Proc. of the 19th Symp. on Reliable Distributed Systems, pages 74–83, Oct. 2000.

[68]   M. Elhadef and B. Ayeb. Efficient Comparison-Based Fault Diagnosis of Multiprocessor Systems Using an Evolutionary Approach. Proc. of the 15th Intl. Parallel and Distributed Processing Symp., 1:6, Apr. 2001.

[69]   M. Elhadef and B. Ayeb. Self-Diagnosis of Multiprocessor Systems Under Generalized Comparison Model. Proc. of the ISCA Intl. Conf. on Parallel and Distributed Computing Systems, pages 372–379, Aug. 2001.

[70]   M. Elhadef and B. Ayeb. An Evolutionary Algorithm for Generalized Comparison-Based Self-Diagnosis of Multiprocessor Systems. Applied Artificial Intelligence, 16(1):73–95, Jan. 2002.

[71]   M. Elhadef, A. Boukerche, and H. Elkadiki. Diagnosing Mobile Ad Hoc Networks: Two Distributed Comparison-Based Self-Diagnosis Protocols. Proc. of the 4th ACM Intl. Workshop on Mobility Management and Wireless Access, pages 18–27, Oct. 2006.

[72]   M. Elhadef, A. Boukerche, and H. Elkadiki. Performance Analysis of a Distributed Comparison-Based Self-Diagnosis Protocol for Wireless Ad Hoc Networks. Proc. of the 9th ACM Intl. Symp. on Modeling Analysis and Simulation of Wireless and Mobile Systems, pages 165–172, Oct. 2006.

[73]   M. Elhadef, A. Boukerche, and H. Elkadiki. Self-Diagnosing Wireless Mesh and Ad Hoc Networks Using an Adaptable Comparison-Based Approach. Proc. of the 2nd Intl. Conf. Availability, Reliability and Security, pages 983–990, Apr. 2007.

[74]   M. Elhadef, S. Das, and A. Nayak. System-Level Fault Diagnosis Using Comparison Models: An Artificial-Immune-Systems-Based Approach. Journal of Networks, 1(5):43–53, Oct. 2006.

[75]   M. Elhadef and A. Nayak. Efficient Symmetric Comparison-Based Self-Diagnosis Using Backpropagation Artificial Neural Networks. Proc. of the IEEE 28th Intl. Performance Computing and Communications Conf., pages 264–271, Dec. 2009.

[76]   M. Elhadef and A. Nayak. A Novel Generalized-Comparison-Based Self-Diagnosis Algorithm for Multiprocessor and Multicomputer Systems Using a Multilayered Neural Network. Proc. of the 13th IEEE Intl. Conf. on Computational Science and Engineering, pages 245–252, Dec. 2010.

[77]   M. Elhadef and A. Nayak. Comparison-Based System-Level Fault Diagnosis: A Neural Network Approach. IEEE Transactions on Parallel and Distributed Systems, 23(6):1047–1059, June 2012.

[78]   A.-H. Esfahanian, L. M. Ni, and B. E. Sagan. The Twisted n-Cube with Application to Multiprocessing. IEEE Transactions on Computers, 40(1):88–93, Jan. 1991.

[79]   J. Fan. Diagnosability of the Möbius Cubes. IEEE Transactions on Parallel and Distributed Systems, 9(9):923–928, Sept. 1998.

[80]   J. Fan. Diagnosability of Crossed Cubes. IEEE Transactions on Computers, 13(10):1099–1104, Oct. 2002.

[81]   M. Feldman, C. Papadimitriou, J. Chuang, and I. Stoica. Free-riding and Whitewashing in Peer-to-Peer Systems. IEEE Journal on Selected Areas in Communications, 24(5):1010–1019, May 2006.

[82]   C. Feng and B. Li. On Large-Scale Peer-to-Peer Streaming Systems with Network Coding. Proc. of the 16th ACM Intl. Conf. on Multimedia, pages 269–278, Oct. 2008.

[83]   V. Fodor and G. Dan. Resilience in Live Peer-to-peer Streaming. IEEE Communications Magazine, 45(6):116–123, June 2007.

[84]   A. D. Friedman. A New Measure of Digital System Diagnosis. Proc. of the 5th IEEE Fault-Tolerant Computing Symp., pages 167–169, June 1975.

[85]   C. P. Fuhrman and H. J. Nussbaumer. A New Comparison Model in System-Level Diagnosis. Proc. of the Intl. Conf. on Parallel and Distributed Processing Techniques and Applications, pages 687–690, Aug. 1996.

[86]   C. P. Fuhrman and H. J. Nussbaumer. Comparison Diagnosis in Large Multiprocessor Systems. Proc. of the 5th Asian Test Symp., pages 244–249, Nov. 1996.

[87]   H. Fujiwara and K. Kinoshita. Connection Assignments for Probabilistically Diagnosable Systems. IEEE Transactions on Computers, C-27(3):280–283, Mar. 1978.

[88]   D. Fussell, M. Malek, and S. Rangarajan. Wafer-Scale Testing/Design for Testability, chapter 9, pages 413–472. Kluwer, 1989.

[89]   D. Fussell and S. Rangarajan. Probabilistic Diagnosis of Multiprocessor Systems with Arbitrary Connectivity. Proc. of the 19th IEEE Fault-Tolerant Computing Symp., pages 560–565, June 1989.

[90]   M. Garland and D. B. Kirk. Understanding Throughput-Oriented Architectures. Communications of the ACM, 53(11):58–66, Nov. 2010.

[91]   G. Gheorghe, R. L. Cigno, and A. Montresor. Security and Privacy Issues in P2P Streaming Systems: A Survey. Peer-to-Peer Networking and Applications, 4(2):75– 91, June 2011.

[92]   D. Gourley and B. Totty. HTTP: The Definitive Guide. O'Reilly, Sept. 2002.

[93]   V. Hadzilacos and S. Toueg. Fault-Tolerant Broadcasts and Related Problems, Distributed Systems. S. Mullender, ACM Press, C.5, 1993.

[94]   W. Haizhou, C. Xingshu, and W. Wenxian. A Measurement Study of Polluting a Large-Scale P2P IPTV System. China Communications, 8(2):95–102, Mar. 2011.

[95]   S. L. Hakimi and A. T. Amin. Characterization of Connection Assignment of Diagnosable Systems. IEEE Transactions on Computers, C-23(1):86–88, 1974.

[96]   S. L. Hakimi and K. Nakajima. On Adaptive System Diagnosis. IEEE Transactions on Computers, C-33(3):234–240, Mar. 1984.

[97]   M. Haridasan and R. van Renesse. Defense Against Intrusion in a Live Streaming Multicast System. Proc. of the 6th IEEE Intl. Conf. on Peer-to-Peer Computing, pages 185–192, Sept. 2006.

[98]   M. Haridasan and R. van Renesse. SecureStream: An Intrusion-Tolerant Protocol for Live-Streaming Dissemination. Computer Communications, 31(3):563–575, Feb. 2008.

[99]   X. Hei, C. Liang, J. Liang, Y. Liu, and K. Ross. A Measurement Study of a Large-Scale P2P IPTV System. IEEE Transactions on Multimedia, 9(8):1672–1687, Dec. 2007.

[100] X. Hei, Y. Liu, and K. W. Ross. IPTV Over P2P Streaming Networks: The Mesh-Pull Approach. IEEE Communications Magazine, 46(2):86–92, Feb. 2008.

[101] M. Hollick, I. Martinovic, T. Krop, and I. Rimac. A Survey on Dependable Routing in Sensor Networks, Ad Hoc Networks, and Cellular Networks. Proc. of the 30th Euromicro Conf., pages 495–502, Sept. 2004.

[102] W.-S. Hong and S.-Y. Hsieh. Strong Diagnosability and Conditional Diagnosability of Augmented Cubes Under the Comparison Diagnosis Model. IEEE Transactions on Reliability, 61(1):140–148, Mar. 2012.

[103] S. H. Hosseini, J. G. Kuhl, and S. M. Reddy. A Diagnosis Algorithm for Distributed Computing Systems with Dynamic Failure and Repair. IEEE Transactions on Computers, C-33(3):223–233, Mar. 1984.

[104] S.-Y. Hsieh and Y.-S. Chen. Strongly Diagnosable Product Networks Under the Comparison Diagnosis Model. IEEE Transactions on Computers, 57(6):721–732, June 2008.

[105] S.-Y. Hsieh and Y.-S. Chen. Strongly Diagnosable Systems Under the Comparison Model. IEEE Transactions on Computers, 57(12):1720–1725, Dec. 2008.

[106] S.-Y. Hsieh and C.-Y. Kao. Determining the Conditional Diagnosability of k-Ary n-Cubes Under the MM* Model. Lecture Notes in Computer Science, 6796:78–88, June 2011.

[107] S.-Y. Hsieh, C.-Y. Tsai, and C.-A. Chen. Strong Diagnosability and Conditional Diagnosability of Multiprocessor Systems and Folded Hypercubes. IEEE Transactions on Computers, PP(99), May 2012.

[108] G.-H. Hsu, D.-F. Chiang, L.-M. Shih, L.-H. Hsu, and J. J. M. Tan. Conditional Diagnosability of Hypercubes Under the Comparison Diagnosis Model. Journal of Systems Architecture, 55(2):140–146, Feb. 2009.

[109] G. H. Hsu and J. J. M. Tan. Conditional Diagnosability of the BC Networks Under the Comparison Diagnosis Model. Proc. of the Intl. Computer Symp., 1:269–274, Nov. 2008.

[110] B. Hu and H. Zhao. Joint Pollution Detection and Attacker Identification in Peer-to-Peer Live Streaming. Proc. of the IEEE Intl. Conf. on Acoustics Speech and Signal Processing, pages 2318–2321, Mar. 2010.

[111] Internet World Stats. “World Internet Usage Statistics News and World Population Stats”. http://www.internetworldstats.com/stats.htm. Accessed in Jan 2013.

[112] Y. Ishida. Active Diagnosis by Self-Organization: An Approach by The Immune Network Metaphor. Proc. of the 15th Intl. Joint Conf. on Artificial Intelligence, pages 1084–1091, Aug. 1997.

[113] P. Jalote. Fault Tolerance in Distributed Systems. Prentice Hall, 1994.

[114] H. Johansen, A. Allavena, and R. van Renesse. Fireflies: Scalable Support for Intrusion-Tolerant Network Overlays. Proc. of the 1st ACM SIGOPS/EuroSys European Conf. on Computer Systems, pages 3–13, Apr. 2006.

[115] A. Kavianpour. Sequential Diagnosability of Star Graphs. Journal of Computers and Electrical Engineering, 22(1):37–44, Jan. 1996.

[116] S. W. Keckler and S. K. Reinhardt. Massively Multithreaded Computing Systems. IEEE Computer, pages 24–25, Aug. 2012.

[117] W. E. Kozlowski and H. Krawczyk. A Comparison-Based Approach in Multicomputer System Diagnosis in Hybrid Fault Situations. IEEE Transations on Computers, 40(11):1283–1286, Nov. 1991.

[118] S. E. Kreutzer and S. L. Hakimi. Adaptive Fault Identification in Two Diagnostic Models. Proc. of the 21th Allerton Conf. on Communication, Control and Computing, pages 353–362, Mar. 1983.

[119] J. G. Kuhl. Fault Diagnosis in Computing Networks. Dep. Elec. Comput. Eng., Univ. of Iowa, Technical Report, Aug. 1980.

[120] J. G. Kuhl and S. M. Reddy. Distributed Fault-Tolerance for Large Multiprocessor Systems. Proc. of the 7th Annual Intl. Symp. on Computer Architecture, pages 23–30, May 1980.

[121] J. G. Kuhl and S. M. Reddy. Fault-Diagnosis in Fully Distributed Systems. Proc. of the 11th IEEE Fault-Tolerant Computing Symp., pages 100–105, June 1981.

[122] P. Kulasinghe and S. Bettayeb. Embedding Binary Trees into Crossed Cubes. IEEE Transactions on Computers, 44(7):923–929, July 1995.

[123] L. E. LaForge, K. F. Kover, and M. S. Fadali. What Designers of Bus and Networks Architectures Should Know about Hypercubes. IEEE Transactions on Computers, 52(4):525–533, Apr. 2003.

[124] P.-L. Lai, J. J. Tan, C.-H. Tsai, and L.-H. Hsu. The Diagnosability of the Matching Composition Netork Under the Comparison Diagnosis Model. IEEE Transactions on Computers, 53(8):1064–1069, Aug. 2004.

[125] P.-L. Lai, J. J. M. Tan, C.-P. Chang, and L.-H. Hsu. Conditional Diagnosability Measures for Large Multiprocessor Systems. IEEE Transactions on Computers, 54(2):165–175, Feb. 2005.

[126] L. A. Laranjeira, M. Malek, and R. M. Jenevein. On Tolerating Faults in Naturally Redundant Algorithms. Proc. of the 10th IEEE Symp. Reliable Distributed Systems, pages 118–127, Oct. 1991.

[127] C. W. Lee and S. Y. Hsieh. Diagnosability of Two-Matching Composition Networks Under the MM* Model. IEEE Transactions on Dependable and Secure Computing, 8(2):246–255, Apr. 2011.

[128] S. Lee and K. G. Shin. On Probabilistic Diagnosis of Multiprocessor Systems Using Multiple Syndromes. IEEE Transactions on Parallel and Distributed Systems, 5(6):630–638, June 1994.

[129] F. T. Leighton. Introduction to Parallel Algorithms and Architectures: Arrays, Trees, Hypercubes. Morgan Kaufmann, San Mateo, CA, 1992.

[130] F. T. Leighton, B. M. Maggs, and R. K. Sitaraman. On The Fault Tolerance of Some Popular Bounded-Degree Networks. SIAM Journal on Computing, 27(5):1303–1333, Oct. 1998.

[131] H. Li, H.Wang, and G. Feng. Adaptive Hierarchical Intrusion Tolerant Model Based on Autonomic Computing. Proc. of the Intl. Conf. on Security Technology, pages 137–141, Dec. 2008.

[132] J.-S. Li, C.-J. Hsieh, and Y.-K. Wang. Distributed Key Management Scheme for Peer-to-Peer Live Streaming Services. Intl. Journal of Communication Systems, Feb. 2012.

[133] J. Liang, R. Kumar, and K. W. Ross. The FastTrack Overlay: A Measurement Study. Computer Networks, 50(6):842–858, Apr. 2006.

[134] J. Liang, N. Naoumov, and K. W. Ross. Efficient Blacklisting and Pollution-Level Estimation in P2P File-Sharing Systems. Asian Internet Engineering Conference, pages 173–175, Dec. 2005.

[135] E. Lin, D. M. N. de Castro, M. Wang, and J. Aycock. SPoIM: A Close Look at Pollution Attacks in P2P Live Streaming. Proc. of the 18th Intl. Workshop on Quality of Service, pages 1–9, June 2010.

[136] F. Lombardi. Comparison-Based Diagnosis with Faulty Comparators. Eletronic Letters, 22(22):1158–1160, Oct. 1986.

[137] T. Loocher, R. Meier, S. Schmid, and R. Wattenhofer. Push-to-Pull Peer-to-Peer Live Streaming. Proc. of the 21st Intl. Symp. on Distributed Computing, pages 388–402, Sept. 2007.

[138] M. Lu, P. P. C. Lee, and J. C. S. Lui. Identity Attack and Anonymity Protection for P2P-VoD Systems. Proc. of the ACM/IEEE Intl. Workshop on Quality of Service, pages 1–9, June 2011.

[139] M. J. Ma and J. M. Xu. Panconnectivity of Locally Twisted Cubes. Appl. Math. Lett., 19(7):673–677, July 2006.

[140] J. Maeng and M. Malek. A Comparison Connection Assignment for Self-Diagnosis of Multiprocessor Systems. Proc. of the 11th IEEE Fault-Tolerant Computing Symp., pages 173–175, Apr. 1981.

[141] P. Maestrini and P. Santi. Self Diagnosis of Processor Arrays Using a Comparison Model. Proc. of the 14th Symp. on Reliable Distributed Systems, pages 218–228, Sept. 1995.

[142] S. N. Maheshwari and S. L. Hakimi. On Models for Diagnosable Systems and Probabilistic Fault Diagnosis. IEEE Transactions on Computers, C-25(3):228–236, Mar. 1976.

[143] M. Malek. A Comparison Connection Assignment for Diagnosis of Multiprocessor Systems. Proc. of the 7th Annual Intl. Symp. on Computer Architecture, pages 31–36, May 1980.

[144] F. S. Martins, R. M. Andrade, A. L. Santos, B. Schulze, and J. N. Souza. Detecting Misbehaving Units on Computational Grids. Concurrency and Computation: Practice & Experience, 22(3):329–342, Mar. 2009.

[145] F. S. Martins, R. M. C. Andrade, A. L. Santos, J. N. Souza, and B. Schulze. Diagnosis on Computational Grids for Detecting Intelligent Cheating Nodes. Proc. of the 2nd Intl. Latin American Grid Workshop, pages 7–14, Nov. 2008.

[146] F. S. Martins, M. Maia, R. M. Andrade, A. L. Santos, and J. N. de Souza. A Grid Computing DiagnosisModel for ToleratingManipulation Attacks. Intl. Transactions on Systems Science and Applications, 2(2):135–146, 2006.

[147] F. S. Martins, M. Maia, R. M. Andrade, A. L. Santos, and J. N. de Souza. Detecting Malicious Manipulation in Grid Environments. Proc. of the 18th Intl. Symp. on Computer Architecture and High Performance Computing, pages 28–35, Oct. 2006.

[148] G. Masson, D. Blough, and G. Sullivan. System Diagnosis. Prentice-Hall, 1996.

[149] S. Micali and V. V. Vazirani. An O(p|V ||E|) Algorithm for Maximum Matching in General Graphs. Proc. of the 16th Annual Symp. Foundations of Comput. Science, pages 17–27, Oct. 1980.

[150] G. Montassier, T. Cholez, G. Doyen, R. Khatoun, I. Chrisment, and O. Festor. Content Pollution Quantification in Large P2P Networks: A Measurement Study on KAD. IEEE Intl. Conf. on Peer-to-Peer Computing, pages 30–33, Sept. 2011.

[151] K. Nakajima. A New Approach to System Diagnosis. Proc. of the 19th Allerton Conf. on Communication, Control and Computing, pages 697–706, Sept. 1981.

[152] B. T. Nassu, E. P. Duarte Jr., and A. T. R. Pozo. A Comparison of Evolutionary Algorithms for System-Level Diagnosis. Proc. of the 7th ACM Genetic and Evolutionary Computation Conf., pages 2053–2060, June 2005.

[153] J. Oliveira, A. Borges, and S. Campos. Content Pollution on P2P Live Streaming Systems. Proc. of the 15th Brazilian Symposium on Multimedia and the Web, Oct. 2009.

[154] N. Oualha and Y. Roudier. A Game Theoretical Approach in Securing P2P Storage Against Whitewashers. Proc. of the 18th IEEE Intl. Workshops on Enabling Technologies: Infrastructures for Collaborative Enterprises, pages 128–133, July 2009.

[155] V. Pai, K. Kumar, K. Tamilmani, V. Sambamurthy, A. E. Mohr, and E. E. Mohr. Chainsaw: Eliminating Trees from Overlay Multicast. Proc. of the 4th Intl. Workshop on Peer-To-Peer Systems, pages 127–140, Feb. 2005.

[156] A. Pelc. Undirected Graph Models for System-Level Fault Diagnosis. IEEE Transactions on Computers, 40(11):1271–1276, Nov. 1991.

[157] A. Pelc. Optimal Fault Diagnosis in Comparison Models. IEEE Transations on Computers, 41(6):779–786, June 1992.

[158] F. Preparata, G. Metze, and R. T. Chien. On the Connection Assignment Problem of Diagnosable Systems. IEEE Transactions on Computers, 16:848–854, Dec. 1967.

[159] R. Pressman. Software Engineering: A Practitioner’s Approach. McGraw-Hill, 2004.

[160] V. Raghavan and A. R. Tripathi. Sequential Diagnosability is co-NP-Complete. IEEE Transactions on Computers, 40(5):584–595, May 1991.

[161] H. V. Ramasamy, P. Pandey, M. Cukier, and W. H. Sanders. Experiences with Building an Intrusion-Tolerant Group Communication System. Software - Practice & Experience, 38(6):639–666, May 2008.

[162] S. Rangarajan, A. T. Dahbura, and E. A. Ziegler. A Distributed System-Level Diagnosis Algorithm for Arbitrary Network Topologies. IEEE Transactions on Computers, 44(2):312–333, Feb. 1995.

[163] S. Rangarajan and D. Fussell. A Probabilistic Method for Fault Diagnosis of Multiprocessor Systems. Proc. of the 18th IEEE Fault-Tolerant Computing Symp., pages 278–283, June 1988.

[164] S. Rangarajan, D. Fussell, and M. Malek. Built-in Testing of Integrated Circuits Wafers. IEEE Transactions on Computers, 39(2):195–205, Feb. 1990.

[165] R. D. Rettberg. Shared Memory Parallel Processing: The Butterfly and the Monarch. MIT Press, 1986.

[166] B. Sallay, P. Maestrini, and P. Santi. Wafer-Scale Diagnosis Tolerating Comparator Faults. IEE Proc. Computer and Digital Techniques, 146(4):211–215, July 1999.

[167] E. A. Schimidt, R. P. Ziwich, E. P. Duarte Jr., and I. Jansch-Pôrto. Diagnóstico de Poluição de Conteúdo em Redes P2P para Transmissões de Mídia Contínua ao Vivo. Proc. of the 17th Brazilian Symposium on Multimedia and the Web, pages 221–228, Oct. 2011.

[168] J. Seibert, X. Sun, C. Nita-Rotaru, and S. Rao. Towards Securing Data Delivery in Peer-to-Peer Streaming. Proc. of the 2nd Intl. Conf. on Comunication Systems and Networks, pages 1–10, Jan. 2010.

[169] A. Sengupta and A. T. Dahbura. On Self-Diagnosable Multiprocessor Systems: Diagnosis by Comparison Approach. IEEE Transactions on Computers, 41(11):1386– 1396, Nov. 1992.

[170] A. Sengupta and C. Rhee. On the Diagnosability of Systems with Three Valued Test Results: Diagnosis by Comparison Strategy. Proc. of the 20th Intl. Symp. on Multiple-Valued Logic, pages 115–120, May 1990.

[171] J.-J. Sheu, W.-T. Huang, and C.-H. Chen. Strong Diagnosability of Regular Networks Under the Comparison Model. Information Processing Letters, 106(1):19– 25, Mar. 2008.

[172] J. So and D. Reeves. AntiLiar: Defending Against Cheating Attacks in Mesh Based Streaming. Proc. of the IEEE 12th Intl. Conf. on Peer-to-Peer Computing, pages 115–125, Sept. 2012.

[173] M. Stahl, R. Buskens, and R. Bianchini. Simulation of the Adapt On-Line Diagnosis Algorithm for General Topology Networks. Proc. of the 11th IEEE Symp. Reliable Distributed Systems, pages 180–187, Oct. 1992.

[174] I. A. Stewart. A General Algorithm for Detecting Faults Under the Comparison Diagnosis Model. Proc. of the 24th IEEE Intl. Symp. on Parallel and Distributed Processing, pages 1–9, Apr. 2010.

[175] J. A. Stratton, C. Rodrigues, I.-J. Sung, L.-W. Chang, N. Anssari, G. Liu, and W.-M. W. Hwu. Algorithm and Data Optimization Techniques for Scaling to Massively Threaded Systems. IEEE Computer, pages 26–32, Aug. 2012.

[176] A. Subbiah and D. M. Blough. Distributed Diagnosis in Dynamic Fault Environments. IEEE Transactions on Parallel and Distributed Systems, 15(5):453–467, May 2004.

[177] G. Sullivan. An O(t3+|E|) Fault Identification Algorithm for Diagnosable Systems. IEEE Transactions on Computers, 37(4):388–397, Apr. 1988.

[178] H. Tamaki. Efficient Self-Embedding of Butterfly Networks with Random Faults. SIAM Journal on Computing, 27(3):614–636, June 1998.

[179] N. F. Tzeng and S. Wei. Enhanced Hypercubes. IEEE Transactions on Computers, 40(3):284–294, Mar. 1991.

[180] US-CERT. “United States Computer Emergency Readiness Team”. http://www.uscert.gov. Accessed in Dec. 2012.

[181] A. S. Vaidya, P. S. N. Rao, and S. R. Shankar. A Class of Hypercube-like Networks. Proc. of the 5th IEEE Symp. Parallel and Distributed Processing, 1(4):800–803, Dec. 1993.

[182] V. N. Vapnik. Statistical Learning Theory. John Wiley and Sons, 1998.

[183] A. Vieira, S. Campos, and J. Almeida. Fighting Attacks in P2P Live Streaming. Simpler is Better. Proc. of the 28th IEEE Intl. Conf. on Computer Communications Workshops, pages 355–356, Apr. 2009.

[184] A. B. Vieira. Transmissão de Mídia Contínua ao Vivo em P2P: Modelagem, Caracterização e Implementação de Mecanismos de Resiliência a Ataques. Tese de Doutorado, Universidade Federal de Minas Gerais (UFMG), 2010.

[185] K. Walsh and E. G. Sirer. Experience with an Object Reputation System for Peer-to-Peer Filesharing. Proc. of the 3rd USENIX Symp. on Networked Systems Design and Implementation, 3:1–14, May 2006.

[186] D. Wang. Diagnosability of Hipercubes and Enhanced Hypercubes Under the Comparison Diagnosis Model. IEEE Transactions on Computers, 48(12):1369–1374, Dec. 1999.

[187] H. Wang, D. M. Blough, and L. Alkalaj. Analysis and Experimental Evaluation of Comparison-Based System-Level Diagnosis for Multiprocessor Systems. Proc. of the 24th IEEE Fault-Tolerant Computing Symp., pages 55–64, June 1994.

[188] H. Wang, D. M. Blough, and L. Alkalaj. Practical Approach to Comparison-based Fault Diagnosis in Multiprocessor Systems. Intl. Journal of Computer Systems Science and Engineering, 9(1):11–20, Jan. 1994.

[189] M. Wang and B. Li. Lava: A Reality Check of Network Coding in Peer-to-Peer Live Streaming. Proc. of the 26th IEEE Intl. Conf. on Computer Communications, pages 1082–1090, May 2007.

[190] Q. Wang, L. Vu, K. Nahrstedt, and H. Khurana. MIS: Malicious Nodes Identification Scheme in Network-Coding-Based Peer-to-Peer Streaming. Proc. of the 29th IEEE Intl. Conf. on Computer Communications, pages 1–5, Mar. 2010.

[191] C. K.Wong and S. S. Lam. Digital Signatures for Flows and Multicasts. IEEE/ACM Transactions on Networking, 7(4):502–513, Aug. 1999.

[192] J. Xu and S. Huang. A New Comparison-Based Scheme for Multiprocessor Fault Tolerance. Microprocessing and Microprogramming, 30(1–5):617–623, Aug. 1990.

[193] J. Xu and B. Randell. Software Fault Tolerance: t/(n-1)-Variant Programming. IEEE Transactions on Reliability, 46(1):60–68, Mar. 1997.

[194] C.-L. Yang and G. M. Masson. An Efficient Algorithm for Multiprocessor Fault Diagnosis Using the Comparison Approach. Information and Computation, 74(1):50–63, July 1987.

[195] H. Yang and X. Yang. A Fast Diagnosis Algorithm for Locally Twisted Cube Multiprocessor Systems under the MM* Model. Computers & Mathematics with Applications, 53(6):918–926, Mar. 2007.

[196] S. Yang, H. Jin, B. Li, X. Liao, H. Yao, and X. Tu. The Content Pollution in Peer-to-Peer Live Streaming Systems: Analysis and Implications. Proc. of the 37th Intl. Conf. on Parallel Processing, pages 652–659, Sept. 2008.

[197] X. Yang. A Linear Time Fault Diagnosis Algorithm for Hypercube Multiprocessors Under the MM* Model. Proc. of the 12th Asian Test Symp., pages 50–55, Nov. 2003.

[198] X. Yang and Y. Y. Tang. Efficient Fault Identification of Diagnosable Systems Under the Comparison Model. IEEE Transactions on Computers, 56(12):1612–1618, Dec. 2007.

[199] X. F. Yang, D. J. Evans, and G. M. Megson. Locally Twisted Cubes are 4-Pancyclic. Appl. Math. Lett., 17(8):919–925, Aug. 2004.

[200] X. F. Yang, D. J. Evans, and G. M. Megson. The Locally Twisted Cubes. Intl. Journal of Computer Mathematics, 82(4):401–413, Apr. 2005.

[201] X. F. Yang, G. M. Megson, and D. J. Evans. A Comparison-Based Diagnosis Algorithm Tailored for Crossed Cube Multiprocessor Systems. Microprocessors and Microsystems, 19(4):169–175, May 2005.

[202] X. Yu and S. Fujita. Whitewash-Aware Reputation Management in Peer-to-Peer File Sharing System. Proc. of the World Congress in Computer Science, Computer Engineering, and Applied Computing, July 2012.

[203] P. Zhang and B. E. Helvik. Modeling and Analysis of P2P Content Distribution Under Coordinated Attack Strategies. IEEE Consumer Communications and Networking Conf., pages 131–135, Jan. 2011.

[204] J. Zheng, S. Latifi, E. Regentova, K. Luo, and X. Wu. Diagnosability of Star Graphs under the Comparison Diagnosis Model. Information Processing Letters, 16(1):73–95, Jan. 2002.

[205] S. Zhou. The Conditional Diagnosability of Crossed Cubes Under the Comparison Model. Intl. Journal of Computer Mathematics, 87(15):3387–3396, Dec. 2010.

[206] Q. Zhu. On Conditional Diagnosability and Reliability of the BC Networks. The Journal of Supercomputing, 45(2):173–184, Aug. 2008.

[207] Q. Zhu, X.-K. Wang, and G. Cheng. Reliability Evaluation of BC Networks. IEEE Transactions on Computers, PP(99):1–6, 2012.

[208] R. P. Ziwich, E. P. Duarte Jr., and L. C. P. Albini. Distributed Integrity Checking for System with Replicated Data. Proc. of the 11th IEEE Intl. Conf. on Parallel and Distributed Systems, pages 363–369, July 2005.

[209] R. P. Ziwich, E. A. Schimidt, E. P. Duarte Jr., and I. Jansch-Pôrto. Diagnosis of Content Pollution in P2P Live Streaming Networks. Proc. of the 6th Latin-American Symp. on Dependable Computing, pages 48-57, Apr. 2013.